diff --git a/data/met_division_test.json b/data/met_division_test.json
new file mode 100644
index 000000000..4d26a9ac5
--- /dev/null
+++ b/data/met_division_test.json
@@ -0,0 +1 @@
+{"global_time": 4800, "bulk": [["--TRANS-ACENAPHTHENE-12-DIOL[j]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[c]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[e]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[s]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[m]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[o]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[p]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[l]", 0], ["--TRANS-ACENAPHTHENE-12-DIOL[i]", 0], ["1-5-Galactofuran[j]", 0], ["1-5-Galactofuran[c]", 0], ["1-5-Galactofuran[e]", 0], ["1-5-Galactofuran[s]", 0], ["1-5-Galactofuran[m]", 0], ["1-5-Galactofuran[o]", 0], ["1-5-Galactofuran[p]", 0], ["1-5-Galactofuran[l]", 0], ["1-5-Galactofuran[i]", 0], ["1-6-Galactofuran[j]", 0], ["1-6-Galactofuran[c]", 0], ["1-6-Galactofuran[e]", 0], ["1-6-Galactofuran[s]", 0], ["1-6-Galactofuran[m]", 0], ["1-6-Galactofuran[o]", 0], ["1-6-Galactofuran[p]", 0], ["1-6-Galactofuran[l]", 0], ["1-6-Galactofuran[i]", 0], ["1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER[i]", 392], ["1-AMINO-PROPAN-2-OL[j]", 0], 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0], ["5-OXOPROLINE[i]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[j]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[c]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[e]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[s]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[m]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[o]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[p]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[l]", 0], ["5-P-BETA-D-RIBOSYL-AMINE[i]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[j]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[c]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[e]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[s]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[m]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[o]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[p]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[l]", 0], ["5-P-NITROPHENYLTHIOADENOSINE[i]", 0], ["5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[j]", 0], ["5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[c]", 0], ["5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[e]", 0], ["5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[s]", 0], ["5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[m]", 0], ["5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[o]", 0], 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0], ["D-Fucoses[m]", 0], ["D-Fucoses[o]", 0], ["D-Fucoses[p]", 0], ["D-Fucoses[l]", 0], ["D-Fucoses[i]", 0], ["D-GALACTARATE[j]", 0], ["D-GALACTARATE[c]", 0], ["D-GALACTARATE[e]", 0], ["D-GALACTARATE[s]", 0], ["D-GALACTARATE[m]", 0], ["D-GALACTARATE[o]", 0], ["D-GALACTARATE[p]", 0], ["D-GALACTARATE[l]", 0], ["D-GALACTARATE[i]", 0], ["D-GALACTONATE[j]", 0], ["D-GALACTONATE[c]", 0], ["D-GALACTONATE[e]", 0], ["D-GALACTONATE[s]", 0], ["D-GALACTONATE[m]", 0], ["D-GALACTONATE[o]", 0], ["D-GALACTONATE[p]", 0], ["D-GALACTONATE[l]", 0], ["D-GALACTONATE[i]", 0], ["D-GALACTONO-1-4-LACTONE[j]", 0], ["D-GALACTONO-1-4-LACTONE[c]", 0], ["D-GALACTONO-1-4-LACTONE[e]", 0], ["D-GALACTONO-1-4-LACTONE[s]", 0], ["D-GALACTONO-1-4-LACTONE[m]", 0], ["D-GALACTONO-1-4-LACTONE[o]", 0], ["D-GALACTONO-1-4-LACTONE[p]", 0], ["D-GALACTONO-1-4-LACTONE[l]", 0], ["D-GALACTONO-1-4-LACTONE[i]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[j]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[c]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[e]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[s]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[m]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[o]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[p]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[l]", 0], ["D-GALACTOSAMINE-6-PHOSPHATE[i]", 0], ["D-GALACTOSYLAMINE[j]", 0], ["D-GALACTOSYLAMINE[c]", 0], ["D-GALACTOSYLAMINE[e]", 0], ["D-GALACTOSYLAMINE[s]", 0], ["D-GALACTOSYLAMINE[m]", 0], ["D-GALACTOSYLAMINE[o]", 0], ["D-GALACTOSYLAMINE[p]", 0], ["D-GALACTOSYLAMINE[l]", 0], ["D-GALACTOSYLAMINE[i]", 0], ["D-GALACTURONATE[j]", 0], ["D-GALACTURONATE[c]", 0], ["D-GALACTURONATE[e]", 0], ["D-GALACTURONATE[s]", 0], ["D-GALACTURONATE[m]", 0], ["D-GALACTURONATE[o]", 0], ["D-GALACTURONATE[p]", 0], ["D-GALACTURONATE[l]", 0], ["D-GALACTURONATE[i]", 0], ["D-GLT[j]", 0], ["D-GLT[c]", 0], ["D-GLT[e]", 0], ["D-GLT[s]", 0], ["D-GLT[m]", 0], ["D-GLT[o]", 0], ["D-GLT[p]", 0], ["D-GLT[l]", 0], ["D-GLT[i]", 0], ["D-GLUCARATE[j]", 0], ["D-GLUCARATE[c]", 0], ["D-GLUCARATE[e]", 0], ["D-GLUCARATE[s]", 0], ["D-GLUCARATE[m]", 0], ["D-GLUCARATE[o]", 0], ["D-GLUCARATE[p]", 0], ["D-GLUCARATE[l]", 0], ["D-GLUCARATE[i]", 0], ["D-GLUCOSAMINE-6-P[j]", 0], ["D-GLUCOSAMINE-6-P[c]", 0], ["D-GLUCOSAMINE-6-P[e]", 0], ["D-GLUCOSAMINE-6-P[s]", 0], ["D-GLUCOSAMINE-6-P[m]", 0], ["D-GLUCOSAMINE-6-P[o]", 0], ["D-GLUCOSAMINE-6-P[p]", 0], ["D-GLUCOSAMINE-6-P[l]", 0], ["D-GLUCOSAMINE-6-P[i]", 0], ["D-Galactopyranuronate[j]", 0], ["D-Galactopyranuronate[c]", 0], ["D-Galactopyranuronate[e]", 0], ["D-Galactopyranuronate[s]", 0], ["D-Galactopyranuronate[m]", 0], ["D-Galactopyranuronate[o]", 0], ["D-Galactopyranuronate[p]", 0], ["D-Galactopyranuronate[l]", 0], ["D-Galactopyranuronate[i]", 0], ["D-Galactose[j]", 0], ["D-Galactose[c]", 0], ["D-Galactose[e]", 0], ["D-Galactose[s]", 0], ["D-Galactose[m]", 0], ["D-Galactose[o]", 0], ["D-Galactose[p]", 0], ["D-Galactose[l]", 0], ["D-Galactose[i]", 0], ["D-Glucopyranuronate[j]", 0], ["D-Glucopyranuronate[c]", 0], ["D-Glucopyranuronate[e]", 0], ["D-Glucopyranuronate[s]", 0], ["D-Glucopyranuronate[m]", 0], ["D-Glucopyranuronate[o]", 0], ["D-Glucopyranuronate[p]", 0], ["D-Glucopyranuronate[l]", 0], ["D-Glucopyranuronate[i]", 0], ["D-Glucose[j]", 0], ["D-Glucose[c]", 0], ["D-Glucose[e]", 0], ["D-Glucose[s]", 0], ["D-Glucose[m]", 0], ["D-Glucose[o]", 0], ["D-Glucose[p]", 0], ["D-Glucose[l]", 0], ["D-Glucose[i]", 0], ["D-Iduronate[j]", 0], ["D-Iduronate[c]", 0], ["D-Iduronate[e]", 0], ["D-Iduronate[s]", 0], ["D-Iduronate[m]", 0], ["D-Iduronate[o]", 0], ["D-Iduronate[p]", 0], ["D-Iduronate[l]", 0], ["D-Iduronate[i]", 0], ["D-LACTATE[j]", 0], ["D-LACTATE[c]", 0], ["D-LACTATE[e]", 0], ["D-LACTATE[s]", 0], ["D-LACTATE[m]", 0], ["D-LACTATE[o]", 0], ["D-LACTATE[p]", 0], ["D-LACTATE[l]", 0], ["D-LACTATE[i]", 0], ["D-MANNONATE[j]", 0], ["D-MANNONATE[c]", 0], ["D-MANNONATE[e]", 0], ["D-MANNONATE[s]", 0], ["D-MANNONATE[m]", 0], ["D-MANNONATE[o]", 0], ["D-MANNONATE[p]", 0], ["D-MANNONATE[l]", 0], ["D-MANNONATE[i]", 0], ["D-METHYL-MALONYL-COA[j]", 0], ["D-METHYL-MALONYL-COA[c]", 0], ["D-METHYL-MALONYL-COA[e]", 0], ["D-METHYL-MALONYL-COA[s]", 0], ["D-METHYL-MALONYL-COA[m]", 0], ["D-METHYL-MALONYL-COA[o]", 0], ["D-METHYL-MALONYL-COA[p]", 0], ["D-METHYL-MALONYL-COA[l]", 0], ["D-METHYL-MALONYL-COA[i]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[j]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[c]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[e]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[s]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[m]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[o]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[p]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[l]", 0], ["D-MYO-INOSITOL-1-MONOPHOSPHATE[i]", 0], ["D-Mannopyranuronate[j]", 0], ["D-Mannopyranuronate[c]", 0], ["D-Mannopyranuronate[e]", 0], ["D-Mannopyranuronate[s]", 0], ["D-Mannopyranuronate[m]", 0], ["D-Mannopyranuronate[o]", 0], ["D-Mannopyranuronate[p]", 0], ["D-Mannopyranuronate[l]", 0], ["D-Mannopyranuronate[i]", 0], ["D-Mannosamines[j]", 0], ["D-Mannosamines[c]", 0], ["D-Mannosamines[e]", 0], ["D-Mannosamines[s]", 0], ["D-Mannosamines[m]", 0], ["D-Mannosamines[o]", 0], ["D-Mannosamines[p]", 0], ["D-Mannosamines[l]", 0], ["D-Mannosamines[i]", 0], ["D-Psicose[j]", 0], ["D-Psicose[c]", 0], ["D-Psicose[e]", 0], ["D-Psicose[s]", 0], ["D-Psicose[m]", 0], ["D-Psicose[o]", 0], ["D-Psicose[p]", 0], ["D-Psicose[l]", 0], ["D-Psicose[i]", 0], ["D-RIBULOSE[j]", 0], ["D-RIBULOSE[c]", 0], ["D-RIBULOSE[e]", 0], ["D-RIBULOSE[s]", 0], ["D-RIBULOSE[m]", 0], ["D-RIBULOSE[o]", 0], ["D-RIBULOSE[p]", 0], ["D-RIBULOSE[l]", 0], ["D-RIBULOSE[i]", 0], ["D-RIBULOSE-1-P[j]", 0], ["D-RIBULOSE-1-P[c]", 0], ["D-RIBULOSE-1-P[e]", 0], ["D-RIBULOSE-1-P[s]", 0], ["D-RIBULOSE-1-P[m]", 0], ["D-RIBULOSE-1-P[o]", 0], ["D-RIBULOSE-1-P[p]", 0], ["D-RIBULOSE-1-P[l]", 0], ["D-RIBULOSE-1-P[i]", 0], ["D-Ribofuranose[j]", 0], ["D-Ribofuranose[c]", 0], ["D-Ribofuranose[e]", 0], ["D-Ribofuranose[s]", 0], ["D-Ribofuranose[m]", 0], ["D-Ribofuranose[o]", 0], ["D-Ribofuranose[p]", 0], ["D-Ribofuranose[l]", 0], ["D-Ribofuranose[i]", 0], ["D-Ribopyranose[j]", 0], ["D-Ribopyranose[c]", 0], ["D-Ribopyranose[e]", 0], ["D-Ribopyranose[s]", 0], ["D-Ribopyranose[m]", 0], ["D-Ribopyranose[o]", 0], ["D-Ribopyranose[p]", 0], ["D-Ribopyranose[l]", 0], ["D-Ribopyranose[i]", 0], ["D-SEDOHEPTULOSE-1-7-P2[j]", 0], ["D-SEDOHEPTULOSE-1-7-P2[c]", 0], ["D-SEDOHEPTULOSE-1-7-P2[e]", 0], ["D-SEDOHEPTULOSE-1-7-P2[s]", 0], ["D-SEDOHEPTULOSE-1-7-P2[m]", 0], ["D-SEDOHEPTULOSE-1-7-P2[o]", 0], ["D-SEDOHEPTULOSE-1-7-P2[p]", 0], ["D-SEDOHEPTULOSE-1-7-P2[l]", 0], ["D-SEDOHEPTULOSE-1-7-P2[i]", 0], ["D-SEDOHEPTULOSE-7-P[j]", 0], ["D-SEDOHEPTULOSE-7-P[c]", 777400], ["D-SEDOHEPTULOSE-7-P[e]", 0], ["D-SEDOHEPTULOSE-7-P[s]", 0], ["D-SEDOHEPTULOSE-7-P[m]", 0], ["D-SEDOHEPTULOSE-7-P[o]", 0], ["D-SEDOHEPTULOSE-7-P[p]", 0], ["D-SEDOHEPTULOSE-7-P[l]", 0], ["D-SEDOHEPTULOSE-7-P[i]", 0], ["D-SERINE[j]", 0], ["D-SERINE[c]", 0], ["D-SERINE[e]", 0], ["D-SERINE[s]", 0], ["D-SERINE[m]", 0], ["D-SERINE[o]", 0], ["D-SERINE[p]", 0], ["D-SERINE[l]", 0], ["D-SERINE[i]", 0], ["D-SORBITOL-6-P[j]", 0], ["D-SORBITOL-6-P[c]", 0], ["D-SORBITOL-6-P[e]", 0], ["D-SORBITOL-6-P[s]", 0], ["D-SORBITOL-6-P[m]", 0], ["D-SORBITOL-6-P[o]", 0], ["D-SORBITOL-6-P[p]", 0], ["D-SORBITOL-6-P[l]", 0], ["D-SORBITOL-6-P[i]", 0], ["D-TAGATURONATE[j]", 0], ["D-TAGATURONATE[c]", 0], ["D-TAGATURONATE[e]", 0], ["D-TAGATURONATE[s]", 0], ["D-TAGATURONATE[m]", 0], ["D-TAGATURONATE[o]", 0], ["D-TAGATURONATE[p]", 0], ["D-TAGATURONATE[l]", 0], ["D-TAGATURONATE[i]", 0], ["D-TARTRATE[j]", 0], ["D-TARTRATE[c]", 0], ["D-TARTRATE[e]", 0], ["D-TARTRATE[s]", 0], ["D-TARTRATE[m]", 0], ["D-TARTRATE[o]", 0], ["D-TARTRATE[p]", 0], ["D-TARTRATE[l]", 0], ["D-TARTRATE[i]", 0], ["D-THREONINE[j]", 0], ["D-THREONINE[c]", 0], ["D-THREONINE[e]", 0], ["D-THREONINE[s]", 0], ["D-THREONINE[m]", 0], ["D-THREONINE[o]", 0], ["D-THREONINE[p]", 0], ["D-THREONINE[l]", 0], ["D-THREONINE[i]", 0], ["D-TRYPTOPHAN[j]", 0], ["D-TRYPTOPHAN[c]", 0], ["D-TRYPTOPHAN[e]", 0], ["D-TRYPTOPHAN[s]", 0], ["D-TRYPTOPHAN[m]", 0], ["D-TRYPTOPHAN[o]", 0], ["D-TRYPTOPHAN[p]", 0], ["D-TRYPTOPHAN[l]", 0], ["D-TRYPTOPHAN[i]", 0], ["D-TYROSINE[j]", 0], ["D-TYROSINE[c]", 0], ["D-TYROSINE[e]", 0], ["D-TYROSINE[s]", 0], ["D-TYROSINE[m]", 0], ["D-TYROSINE[o]", 0], ["D-TYROSINE[p]", 0], ["D-TYROSINE[l]", 0], ["D-TYROSINE[i]", 0], ["D-XYLONATE[j]", 0], ["D-XYLONATE[c]", 0], ["D-XYLONATE[e]", 0], ["D-XYLONATE[s]", 0], ["D-XYLONATE[m]", 0], ["D-XYLONATE[o]", 0], ["D-XYLONATE[p]", 0], ["D-XYLONATE[l]", 0], ["D-XYLONATE[i]", 0], ["D-XYLULOSE[j]", 0], ["D-XYLULOSE[c]", 0], ["D-XYLULOSE[e]", 0], ["D-XYLULOSE[s]", 0], ["D-XYLULOSE[m]", 0], ["D-XYLULOSE[o]", 0], ["D-XYLULOSE[p]", 0], ["D-XYLULOSE[l]", 0], ["D-XYLULOSE[i]", 0], ["D-Xylopyranose[j]", 0], ["D-Xylopyranose[c]", 0], ["D-Xylopyranose[e]", 0], ["D-Xylopyranose[s]", 0], ["D-Xylopyranose[m]", 0], ["D-Xylopyranose[o]", 0], ["D-Xylopyranose[p]", 0], ["D-Xylopyranose[l]", 0], ["D-Xylopyranose[i]", 0], ["D-Xylose[j]", 0], ["D-Xylose[c]", 0], ["D-Xylose[e]", 0], ["D-Xylose[s]", 0], ["D-Xylose[m]", 0], ["D-Xylose[o]", 0], ["D-Xylose[p]", 0], ["D-Xylose[l]", 0], ["D-Xylose[i]", 0], ["D-altropyranoses[j]", 0], ["D-altropyranoses[c]", 0], ["D-altropyranoses[e]", 0], ["D-altropyranoses[s]", 0], ["D-altropyranoses[m]", 0], ["D-altropyranoses[o]", 0], ["D-altropyranoses[p]", 0], ["D-altropyranoses[l]", 0], ["D-altropyranoses[i]", 0], ["D-arabinofuranose[j]", 0], ["D-arabinofuranose[c]", 0], ["D-arabinofuranose[e]", 0], ["D-arabinofuranose[s]", 0], ["D-arabinofuranose[m]", 0], ["D-arabinofuranose[o]", 0], ["D-arabinofuranose[p]", 0], ["D-arabinofuranose[l]", 0], ["D-arabinofuranose[i]", 0], ["D-arabinopyranose[j]", 0], ["D-arabinopyranose[c]", 0], ["D-arabinopyranose[e]", 0], ["D-arabinopyranose[s]", 0], ["D-arabinopyranose[m]", 0], ["D-arabinopyranose[o]", 0], ["D-arabinopyranose[p]", 0], ["D-arabinopyranose[l]", 0], ["D-arabinopyranose[i]", 0], ["D-aspartate-peptidoglycan[j]", 0], ["D-aspartate-peptidoglycan[c]", 0], ["D-aspartate-peptidoglycan[e]", 0], ["D-aspartate-peptidoglycan[s]", 0], ["D-aspartate-peptidoglycan[m]", 0], ["D-aspartate-peptidoglycan[o]", 0], ["D-aspartate-peptidoglycan[p]", 0], ["D-aspartate-peptidoglycan[l]", 0], ["D-aspartate-peptidoglycan[i]", 0], ["D-fructofuranose-1-phosphate[j]", 0], ["D-fructofuranose-1-phosphate[c]", 0], ["D-fructofuranose-1-phosphate[e]", 0], ["D-fructofuranose-1-phosphate[s]", 0], ["D-fructofuranose-1-phosphate[m]", 0], ["D-fructofuranose-1-phosphate[o]", 0], ["D-fructofuranose-1-phosphate[p]", 0], ["D-fructofuranose-1-phosphate[l]", 0], ["D-fructofuranose-1-phosphate[i]", 0], ["D-fructofuranuronate[j]", 0], ["D-fructofuranuronate[c]", 0], ["D-fructofuranuronate[e]", 0], ["D-fructofuranuronate[s]", 0], ["D-fructofuranuronate[m]", 0], ["D-fructofuranuronate[o]", 0], ["D-fructofuranuronate[p]", 0], ["D-fructofuranuronate[l]", 0], ["D-fructofuranuronate[i]", 0], ["D-fructopyranose-1-phosphate[j]", 0], ["D-fructopyranose-1-phosphate[c]", 0], ["D-fructopyranose-1-phosphate[e]", 0], ["D-fructopyranose-1-phosphate[s]", 0], ["D-fructopyranose-1-phosphate[m]", 0], ["D-fructopyranose-1-phosphate[o]", 0], ["D-fructopyranose-1-phosphate[p]", 0], ["D-fructopyranose-1-phosphate[l]", 0], ["D-fructopyranose-1-phosphate[i]", 0], ["D-galactofuransoe[j]", 0], ["D-galactofuransoe[c]", 0], ["D-galactofuransoe[e]", 0], ["D-galactofuransoe[s]", 0], ["D-galactofuransoe[m]", 0], ["D-galactofuransoe[o]", 0], ["D-galactofuransoe[p]", 0], ["D-galactofuransoe[l]", 0], ["D-galactofuransoe[i]", 0], ["D-galactopyranose[j]", 0], ["D-galactopyranose[c]", 0], ["D-galactopyranose[e]", 0], ["D-galactopyranose[s]", 0], ["D-galactopyranose[m]", 0], ["D-galactopyranose[o]", 0], ["D-galactopyranose[p]", 0], ["D-galactopyranose[l]", 0], ["D-galactopyranose[i]", 0], ["D-galactopyranose-6-phosphate[j]", 0], ["D-galactopyranose-6-phosphate[c]", 0], ["D-galactopyranose-6-phosphate[e]", 0], ["D-galactopyranose-6-phosphate[s]", 0], ["D-galactopyranose-6-phosphate[m]", 0], ["D-galactopyranose-6-phosphate[o]", 0], ["D-galactopyranose-6-phosphate[p]", 0], ["D-galactopyranose-6-phosphate[l]", 0], ["D-galactopyranose-6-phosphate[i]", 0], ["D-galactose-1-phosphate[j]", 0], ["D-galactose-1-phosphate[c]", 0], ["D-galactose-1-phosphate[e]", 0], ["D-galactose-1-phosphate[s]", 0], ["D-galactose-1-phosphate[m]", 0], ["D-galactose-1-phosphate[o]", 0], ["D-galactose-1-phosphate[p]", 0], ["D-galactose-1-phosphate[l]", 0], ["D-galactose-1-phosphate[i]", 0], ["D-glucopyranose-6-phosphate[j]", 0], ["D-glucopyranose-6-phosphate[c]", 0], ["D-glucopyranose-6-phosphate[e]", 0], ["D-glucopyranose-6-phosphate[s]", 0], ["D-glucopyranose-6-phosphate[m]", 0], ["D-glucopyranose-6-phosphate[o]", 0], ["D-glucopyranose-6-phosphate[p]", 0], ["D-glucopyranose-6-phosphate[l]", 0], ["D-glucopyranose-6-phosphate[i]", 0], ["D-glucose-1-phosphates[j]", 0], ["D-glucose-1-phosphates[c]", 0], ["D-glucose-1-phosphates[e]", 0], ["D-glucose-1-phosphates[s]", 0], ["D-glucose-1-phosphates[m]", 0], ["D-glucose-1-phosphates[o]", 0], ["D-glucose-1-phosphates[p]", 0], ["D-glucose-1-phosphates[l]", 0], ["D-glucose-1-phosphates[i]", 0], ["D-idopyranuronate[j]", 0], ["D-idopyranuronate[c]", 0], ["D-idopyranuronate[e]", 0], ["D-idopyranuronate[s]", 0], ["D-idopyranuronate[m]", 0], ["D-idopyranuronate[o]", 0], ["D-idopyranuronate[p]", 0], ["D-idopyranuronate[l]", 0], ["D-idopyranuronate[i]", 0], ["D-mannopyranose[j]", 0], ["D-mannopyranose[c]", 0], ["D-mannopyranose[e]", 0], ["D-mannopyranose[s]", 0], ["D-mannopyranose[m]", 0], ["D-mannopyranose[o]", 0], ["D-mannopyranose[p]", 0], ["D-mannopyranose[l]", 0], ["D-mannopyranose[i]", 0], ["D-mannuronates[j]", 0], ["D-mannuronates[c]", 0], ["D-mannuronates[e]", 0], ["D-mannuronates[s]", 0], ["D-mannuronates[m]", 0], ["D-mannuronates[o]", 0], ["D-mannuronates[p]", 0], ["D-mannuronates[l]", 0], ["D-mannuronates[i]", 0], 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0], ["L-HISTIDINOL-P[i]", 0], ["L-HOMOCYSTEATE[j]", 0], ["L-HOMOCYSTEATE[c]", 0], ["L-HOMOCYSTEATE[e]", 0], ["L-HOMOCYSTEATE[s]", 0], ["L-HOMOCYSTEATE[m]", 0], ["L-HOMOCYSTEATE[o]", 0], ["L-HOMOCYSTEATE[p]", 0], ["L-HOMOCYSTEATE[l]", 0], ["L-HOMOCYSTEATE[i]", 0], ["L-IDONATE[j]", 0], ["L-IDONATE[c]", 0], ["L-IDONATE[e]", 0], ["L-IDONATE[s]", 0], ["L-IDONATE[m]", 0], ["L-IDONATE[o]", 0], ["L-IDONATE[p]", 0], ["L-IDONATE[l]", 0], ["L-IDONATE[i]", 0], ["L-Iduronate[j]", 0], ["L-Iduronate[c]", 0], ["L-Iduronate[e]", 0], ["L-Iduronate[s]", 0], ["L-Iduronate[m]", 0], ["L-Iduronate[o]", 0], ["L-Iduronate[p]", 0], ["L-Iduronate[l]", 0], ["L-Iduronate[i]", 0], ["L-LACTATE[j]", 0], ["L-LACTATE[c]", 0], ["L-LACTATE[e]", 0], ["L-LACTATE[s]", 0], ["L-LACTATE[m]", 0], ["L-LACTATE[o]", 0], ["L-LACTATE[p]", 0], ["L-LACTATE[l]", 0], ["L-LACTATE[i]", 0], ["L-LACTDEHYDROGFMN-MONOMER[i]", 287], ["L-LYXOSE[j]", 0], ["L-LYXOSE[c]", 0], ["L-LYXOSE[e]", 0], ["L-LYXOSE[s]", 0], ["L-LYXOSE[m]", 0], 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0], ["L-PANTOATE[l]", 0], ["L-PANTOATE[i]", 0], ["L-PENICILLAMINE[j]", 0], ["L-PENICILLAMINE[c]", 0], ["L-PENICILLAMINE[e]", 0], ["L-PENICILLAMINE[s]", 0], ["L-PENICILLAMINE[m]", 0], ["L-PENICILLAMINE[o]", 0], ["L-PENICILLAMINE[p]", 0], ["L-PENICILLAMINE[l]", 0], ["L-PENICILLAMINE[i]", 0], ["L-PHOSPHINOTHRICIN[j]", 0], ["L-PHOSPHINOTHRICIN[c]", 0], ["L-PHOSPHINOTHRICIN[e]", 0], ["L-PHOSPHINOTHRICIN[s]", 0], ["L-PHOSPHINOTHRICIN[m]", 0], ["L-PHOSPHINOTHRICIN[o]", 0], ["L-PHOSPHINOTHRICIN[p]", 0], ["L-PHOSPHINOTHRICIN[l]", 0], ["L-PHOSPHINOTHRICIN[i]", 0], ["L-PIPECOLATE[j]", 0], ["L-PIPECOLATE[c]", 0], ["L-PIPECOLATE[e]", 0], ["L-PIPECOLATE[s]", 0], ["L-PIPECOLATE[m]", 0], ["L-PIPECOLATE[o]", 0], ["L-PIPECOLATE[p]", 0], ["L-PIPECOLATE[l]", 0], ["L-PIPECOLATE[i]", 0], ["L-RHAMNOFURANOSE[j]", 0], ["L-RHAMNOFURANOSE[c]", 0], ["L-RHAMNOFURANOSE[e]", 0], ["L-RHAMNOFURANOSE[s]", 0], ["L-RHAMNOFURANOSE[m]", 0], ["L-RHAMNOFURANOSE[o]", 0], ["L-RHAMNOFURANOSE[p]", 0], ["L-RHAMNOFURANOSE[l]", 0], ["L-RHAMNOFURANOSE[i]", 0], ["L-RHAMNONATE[j]", 0], ["L-RHAMNONATE[c]", 0], ["L-RHAMNONATE[e]", 0], ["L-RHAMNONATE[s]", 0], ["L-RHAMNONATE[m]", 0], ["L-RHAMNONATE[o]", 0], ["L-RHAMNONATE[p]", 0], ["L-RHAMNONATE[l]", 0], ["L-RHAMNONATE[i]", 0], ["L-RIBULOSE[j]", 0], ["L-RIBULOSE[c]", 0], ["L-RIBULOSE[e]", 0], ["L-RIBULOSE[s]", 0], ["L-RIBULOSE[m]", 0], ["L-RIBULOSE[o]", 0], ["L-RIBULOSE[p]", 0], ["L-RIBULOSE[l]", 0], ["L-RIBULOSE[i]", 0], ["L-RIBULOSE-5-P[j]", 0], ["L-RIBULOSE-5-P[c]", 0], ["L-RIBULOSE-5-P[e]", 0], ["L-RIBULOSE-5-P[s]", 0], ["L-RIBULOSE-5-P[m]", 0], ["L-RIBULOSE-5-P[o]", 0], ["L-RIBULOSE-5-P[p]", 0], ["L-RIBULOSE-5-P[l]", 0], ["L-RIBULOSE-5-P[i]", 0], ["L-SELENOCYSTEINE[j]", 0], ["L-SELENOCYSTEINE[c]", 74934], ["L-SELENOCYSTEINE[e]", 0], ["L-SELENOCYSTEINE[s]", 0], ["L-SELENOCYSTEINE[m]", 0], ["L-SELENOCYSTEINE[o]", 0], ["L-SELENOCYSTEINE[p]", 0], ["L-SELENOCYSTEINE[l]", 0], ["L-SELENOCYSTEINE[i]", 0], ["L-THREO-3-METHYL-ASPARTATE[j]", 0], ["L-THREO-3-METHYL-ASPARTATE[c]", 0], ["L-THREO-3-METHYL-ASPARTATE[e]", 0], ["L-THREO-3-METHYL-ASPARTATE[s]", 0], ["L-THREO-3-METHYL-ASPARTATE[m]", 0], ["L-THREO-3-METHYL-ASPARTATE[o]", 0], ["L-THREO-3-METHYL-ASPARTATE[p]", 0], ["L-THREO-3-METHYL-ASPARTATE[l]", 0], ["L-THREO-3-METHYL-ASPARTATE[i]", 0], ["L-THREONINE-O-3-PHOSPHATE[j]", 0], ["L-THREONINE-O-3-PHOSPHATE[c]", 0], ["L-THREONINE-O-3-PHOSPHATE[e]", 0], ["L-THREONINE-O-3-PHOSPHATE[s]", 0], ["L-THREONINE-O-3-PHOSPHATE[m]", 0], ["L-THREONINE-O-3-PHOSPHATE[o]", 0], ["L-THREONINE-O-3-PHOSPHATE[p]", 0], ["L-THREONINE-O-3-PHOSPHATE[l]", 0], ["L-THREONINE-O-3-PHOSPHATE[i]", 0], ["L-XYLOSE[j]", 0], ["L-XYLOSE[c]", 0], ["L-XYLOSE[e]", 0], ["L-XYLOSE[s]", 0], ["L-XYLOSE[m]", 0], ["L-XYLOSE[o]", 0], ["L-XYLOSE[p]", 0], ["L-XYLOSE[l]", 0], ["L-XYLOSE[i]", 0], ["L-XYLULOSE[j]", 0], ["L-XYLULOSE[c]", 0], ["L-XYLULOSE[e]", 0], ["L-XYLULOSE[s]", 0], ["L-XYLULOSE[m]", 0], ["L-XYLULOSE[o]", 0], ["L-XYLULOSE[p]", 0], ["L-XYLULOSE[l]", 0], ["L-XYLULOSE[i]", 0], ["L-XYLULOSE-5-P[j]", 0], ["L-XYLULOSE-5-P[c]", 0], ["L-XYLULOSE-5-P[e]", 0], ["L-XYLULOSE-5-P[s]", 0], ["L-XYLULOSE-5-P[m]", 0], ["L-XYLULOSE-5-P[o]", 0], ["L-XYLULOSE-5-P[p]", 0], ["L-XYLULOSE-5-P[l]", 0], ["L-XYLULOSE-5-P[i]", 0], ["L-arabinofuranose[j]", 0], ["L-arabinofuranose[c]", 0], ["L-arabinofuranose[e]", 0], ["L-arabinofuranose[s]", 0], ["L-arabinofuranose[m]", 0], ["L-arabinofuranose[o]", 0], ["L-arabinofuranose[p]", 0], ["L-arabinofuranose[l]", 0], ["L-arabinofuranose[i]", 0], ["L-arabinopyranose[j]", 0], ["L-arabinopyranose[c]", 0], ["L-arabinopyranose[e]", 0], ["L-arabinopyranose[s]", 0], ["L-arabinopyranose[m]", 0], ["L-arabinopyranose[o]", 0], ["L-arabinopyranose[p]", 0], ["L-arabinopyranose[l]", 0], ["L-arabinopyranose[i]", 0], ["L-fucoses[j]", 0], ["L-fucoses[c]", 0], ["L-fucoses[e]", 0], ["L-fucoses[s]", 0], ["L-fucoses[m]", 0], ["L-fucoses[o]", 0], ["L-fucoses[p]", 0], ["L-fucoses[l]", 0], ["L-fucoses[i]", 0], ["L-gulopyranose[j]", 0], ["L-gulopyranose[c]", 0], ["L-gulopyranose[e]", 0], ["L-gulopyranose[s]", 0], ["L-gulopyranose[m]", 0], ["L-gulopyranose[o]", 0], ["L-gulopyranose[p]", 0], ["L-gulopyranose[l]", 0], ["L-gulopyranose[i]", 0], ["L-idopyranuronate[j]", 0], ["L-idopyranuronate[c]", 0], ["L-idopyranuronate[e]", 0], ["L-idopyranuronate[s]", 0], ["L-idopyranuronate[m]", 0], ["L-idopyranuronate[o]", 0], ["L-idopyranuronate[p]", 0], ["L-idopyranuronate[l]", 0], ["L-idopyranuronate[i]", 0], ["L-mannose[j]", 0], ["L-mannose[c]", 0], ["L-mannose[e]", 0], ["L-mannose[s]", 0], ["L-mannose[m]", 0], ["L-mannose[o]", 0], ["L-mannose[p]", 0], ["L-mannose[l]", 0], ["L-mannose[i]", 0], ["L-rhamnopyranose[j]", 0], ["L-rhamnopyranose[c]", 0], ["L-rhamnopyranose[e]", 0], ["L-rhamnopyranose[s]", 0], ["L-rhamnopyranose[m]", 0], ["L-rhamnopyranose[o]", 0], ["L-rhamnopyranose[p]", 0], ["L-rhamnopyranose[l]", 0], ["L-rhamnopyranose[i]", 0], ["L-rhamnose[j]", 0], ["L-rhamnose[c]", 0], ["L-rhamnose[e]", 0], ["L-rhamnose[s]", 0], ["L-rhamnose[m]", 0], ["L-rhamnose[o]", 0], ["L-rhamnose[p]", 0], ["L-rhamnose[l]", 0], ["L-rhamnose[i]", 0], ["L-sorbose[j]", 0], ["L-sorbose[c]", 0], ["L-sorbose[e]", 0], ["L-sorbose[s]", 0], ["L-sorbose[m]", 0], ["L-sorbose[o]", 0], ["L-sorbose[p]", 0], ["L-sorbose[l]", 0], ["L-sorbose[i]", 0], ["L-xylopyranose[j]", 0], ["L-xylopyranose[c]", 0], ["L-xylopyranose[e]", 0], ["L-xylopyranose[s]", 0], ["L-xylopyranose[m]", 0], ["L-xylopyranose[o]", 0], ["L-xylopyranose[p]", 0], ["L-xylopyranose[l]", 0], ["L-xylopyranose[i]", 0], ["LACTALD[j]", 0], ["LACTALD[c]", 0], ["LACTALD[e]", 0], ["LACTALD[s]", 0], ["LACTALD[m]", 0], ["LACTALD[o]", 0], ["LACTALD[p]", 0], ["LACTALD[l]", 0], ["LACTALD[i]", 0], ["LACTALDDEHYDROG-MONOMER[c]", 1], ["LACTALDREDUCT-CPLX[c]", 34], ["LACTALDREDUCT-MONOMER[c]", 0], ["LACY-MONOMER[m]", 7], ["LARGEILVB-MONOMER[c]", 4], ["LAUROYLACYLTRAN-MONOMER[m]", 156], ["LAUROYLCOA-CPD[j]", 0], ["LAUROYLCOA-CPD[c]", 0], ["LAUROYLCOA-CPD[e]", 0], ["LAUROYLCOA-CPD[s]", 0], ["LAUROYLCOA-CPD[m]", 0], ["LAUROYLCOA-CPD[o]", 0], ["LAUROYLCOA-CPD[p]", 0], ["LAUROYLCOA-CPD[l]", 0], ["LAUROYLCOA-CPD[i]", 0], ["LCTP-MONOMER[i]", 145], ["LDC2-CPLX[c]", 7], ["LDC2-MONOMER[c]", 1], ["LEU[j]", 0], ["LEU[c]", 312055], ["LEU[e]", 0], ["LEU[s]", 0], ["LEU[m]", 0], ["LEU[o]", 0], ["LEU[p]", 0], ["LEU[l]", 0], ["LEU[i]", 0], ["LEUC-MONOMER[c]", 4], ["LEUD-MONOMER[c]", 6899], ["LEUS-MONOMER[c]", 3041], ["LI+[j]", 0], ["LI+[c]", 0], ["LI+[e]", 0], ["LI+[s]", 0], ["LI+[m]", 0], ["LI+[o]", 0], ["LI+[p]", 0], ["LI+[l]", 0], ["LI+[i]", 0], ["LINAMARIN[j]", 0], ["LINAMARIN[c]", 0], ["LINAMARIN[e]", 0], ["LINAMARIN[s]", 0], ["LINAMARIN[m]", 0], ["LINAMARIN[o]", 0], ["LINAMARIN[p]", 0], ["LINAMARIN[l]", 0], ["LINAMARIN[i]", 0], ["LIPID-IV-A[j]", 0], ["LIPID-IV-A[c]", 0], ["LIPID-IV-A[e]", 0], ["LIPID-IV-A[s]", 0], ["LIPID-IV-A[m]", 0], ["LIPID-IV-A[o]", 0], ["LIPID-IV-A[p]", 0], ["LIPID-IV-A[l]", 0], ["LIPID-IV-A[i]", 0], ["LIPIDADISACCHARIDESYNTH-MONOMER[i]", 1], ["LIPOAMIDE[j]", 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false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], 1985650, 5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2031211584348458646], [2922, 1, 0, [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], 1985650, 6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5014856213179202006], [2923, 1, 0, [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], 1986137, 3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5711555027199119885], [2923, 1, 0, [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], 1986137, 4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2166007611098179374], [2923, 1, 0, [false, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}, "process_state": {"polypeptide_elongation": {"aa_count_diff": {}, "gtp_to_hydrolyze": 9416808.0}}, "request": {"ecoli-chromosome-replication": {"bulk": []}, "ecoli-complexation": {"bulk": []}, "ecoli-equilibrium": {"bulk": []}, "ecoli-polypeptide-elongation": {"bulk": []}, "ecoli-polypeptide-initiation": {"bulk": []}, "ecoli-protein-degradation": {"bulk": []}, "ecoli-rna-degradation": {"bulk": []}, "ecoli-tf-binding": {"bulk": []}, "ecoli-transcript-elongation": {"bulk": []}, "ecoli-transcript-initiation": {"bulk": []}, "ecoli-two-component-system": {"bulk": []}}, "division_threshold": 1131.709984514699, "bulk_dtypes": "[('id', '<U43'), ('count', '<i8')]", "unique_dtypes": {"active_replisome": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('coordinates', '<i8'), ('domain_index', '<i4'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('right_replichore', '?'), ('unique_index', '<i8')]", "oriC": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('domain_index', '<i4'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]", "chromosome_domain": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('child_domains', '<i4', (2,)), ('domain_index', '<i4'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]", "full_chromosome": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('division_time', '<f8'), ('domain_index', '<i4'), ('has_triggered_division', '?'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]", "active_ribosome": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('mRNA_index', '<i8'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('peptide_length', '<i8'), ('pos_on_mRNA', '<i8'), ('protein_index', '<i8'), ('unique_index', '<i8')]", "RNA": "[('RNAP_index', '<i8'), ('TU_index', '<i8'), ('_entryState', 'i1'), ('_globalIndex', '<i8'), ('can_translate', '?'), ('is_full_transcript', '?'), ('is_mRNA', '?'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('transcript_length', '<i8'), ('unique_index', '<i8')]", "promoter": "[('TU_index', '<i8'), ('_entryState', 'i1'), ('_globalIndex', '<i8'), ('bound_TF', '?', (24,)), ('coordinates', '<i8'), ('domain_index', '<i4'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]", "active_RNAP": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('coordinates', '<i8'), ('direction', '?'), ('domain_index', '<i4'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]", "DnaA_box": "[('DnaA_bound', '?'), ('_entryState', 'i1'), ('_globalIndex', '<i8'), ('coordinates', '<i8'), ('domain_index', '<i4'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]", "chromosomal_segment": "[('_entryState', 'i1'), ('_globalIndex', '<i8'), ('boundary_coordinates', '<i8', (2,)), ('boundary_molecule_indexes', '<i8', (2,)), ('domain_index', '<i4'), ('linking_number', '<f8'), ('massDiff_DNA', '<f8'), ('massDiff_mRNA', '<f8'), ('massDiff_metabolite', '<f8'), ('massDiff_miscRNA', '<f8'), ('massDiff_nonspecific_RNA', '<f8'), ('massDiff_protein', '<f8'), ('massDiff_rRNA', '<f8'), ('massDiff_tRNA', '<f8'), ('massDiff_water', '<f8'), ('unique_index', '<i8')]"}}
\ No newline at end of file
diff --git a/ecoli/composites/ecoli_configs/fba_new_environments.json b/ecoli/composites/ecoli_configs/fba_new_environments.json
new file mode 100644
index 000000000..db4e22ca9
--- /dev/null
+++ b/ecoli/composites/ecoli_configs/fba_new_environments.json
@@ -0,0 +1,14 @@
+{
+    "experiment_id" : "fba_redux",
+    "total_time" : 10,
+    "swap_processes" : {
+        "ecoli-metabolism" : "ecoli-metabolism-redux"
+    },
+    "flow": {
+        "ecoli-metabolism-redux": [["ecoli-chromosome-structure"]],
+        "ecoli-mass-listener": [["ecoli-metabolism-redux"]],
+        "mRNA_counts_listener": [["ecoli-mass-listener"]],
+        "monomer_counts_listener": [["mRNA_counts_listener"]]
+    },
+    "raw_output" : false
+}
diff --git a/ecoli/composites/ecoli_configs/fba_redux_div.json b/ecoli/composites/ecoli_configs/fba_redux_div.json
index 401dde384..f8934d21e 100644
--- a/ecoli/composites/ecoli_configs/fba_redux_div.json
+++ b/ecoli/composites/ecoli_configs/fba_redux_div.json
@@ -10,6 +10,6 @@
         "mRNA_counts_listener": [["ecoli-mass-listener"]],
         "monomer_counts_listener": [["mRNA_counts_listener"]]
     },
-    "raw_output" : false,
-    "divide" : false
+    "divide" : true,
+    "initial_state_file": "met_division_test"
 }
diff --git a/ecoli/experiments/metabolism_redux_sim.py b/ecoli/experiments/metabolism_redux_sim.py
index a8fd7f704..1a17fe98e 100644
--- a/ecoli/experiments/metabolism_redux_sim.py
+++ b/ecoli/experiments/metabolism_redux_sim.py
@@ -123,7 +123,7 @@ def run_metabolism_composite():
 
 def run_ecoli_with_metabolism_redux(
         filename='fba_redux',
-        total_time=10,
+        total_time=50,
         divide=True,
         initial_state_file='wcecoli_t0',
         progress_bar=True,
@@ -163,47 +163,22 @@ def run_ecoli_with_metabolism_redux(
     save_sim_output(folder, query, sim, save_model=True)
 
 
-
-@pytest.mark.slow
-def test_ecoli_with_metabolism_redux(
-        filename='fba_redux',
-        total_time=4,
-        divide=False,
-        progress_bar=True,
-        log_updates=False,
-        emitter='timeseries',
-):
-    sim = EcoliSim.from_file(CONFIG_DIR_PATH + filename + '.json')
-    sim.total_time = total_time
-    sim.divide = divide
-    sim.progress_bar = progress_bar
-    sim.log_updates = log_updates
-    sim.emitter = emitter
-    sim.build_ecoli()
-
-    # run simulation and add asserts to output
-    sim.run()
-
-    # assert 'ecoli-metabolism-gradient-descent' in sim.ecoli['processes']
-    # assert 'ecoli-metabolism' not in sim.ecoli['processes']
-    # assert 'ecoli-metabolism-gradient-descent' in sim.ecoli['topology']
-    # assert 'ecoli-metabolism' not in sim.ecoli['topology']
-
-
-
 @pytest.mark.slow
 def test_ecoli_with_metabolism_redux_div(
         filename='fba_redux_div',
-        total_time=4,
-        divide=True,
+        total_time=30,
         emitter='timeseries',
+        initial_state_file = 'met_division_test'
 ):
     # TODO (Cyrus) - Add test that affirms structure of output query.
     sim = EcoliSim.from_file(CONFIG_DIR_PATH + filename + '.json')
     sim.total_time = total_time
-    sim.divide = divide
     sim.emitter = emitter
     sim.build_ecoli()
+    sim.initial_state = get_state_from_file(path=f'data/{initial_state_file}.json')
+
+    # this means that sims will not create conflicting random indices
+    sim.seed += sim.initial_state['global_time']
 
     sim.run()
     
@@ -213,16 +188,12 @@ def test_ecoli_with_metabolism_redux_div(
     # assert 'ecoli-metabolism-gradient-descent' in sim.processes
     # assert 'ecoli-metabolism' not in sim.processes
 
-    query = []
-    agents = sim.query()['agents'].keys()
-    for agent in agents:
-        query.extend([('agents', agent, 'listeners', 'fba_results'),
-                      ('agents', agent, 'listeners', 'mass'),
-                      ('agents', agent, 'bulk')])
-    output = sim.query(query)
+    # assert division occured
+    assert len(sim.query()[total_time]['agents']) == 2 , "Cell did not divide in metabolism division test"
+
+    # TODO assert fluxes in the output query
+    # TODO assert that processes have been swapped
 
-    # test that water is being used (model is running)
-    assert sum(output['agents'][agent]['listeners']['fba_results']['estimated_exchange_dmdt']['WATER[p]']) != 0
 
 def run_ecoli_with_default_metabolism(
         filename='default',
@@ -256,7 +227,6 @@ def run_ecoli_with_default_metabolism(
     '0': run_metabolism,
     '1': run_metabolism_composite,
     '2': run_ecoli_with_metabolism_redux,
-    '3': test_ecoli_with_metabolism_redux,
     '4': test_ecoli_with_metabolism_redux_div,
     '5': run_ecoli_with_default_metabolism,
 }
diff --git a/ecoli/experiments/new_environments_test/actual_sim_analysis.ipynb b/ecoli/experiments/new_environments_test/actual_sim_analysis.ipynb
new file mode 100644
index 000000000..4726733cc
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/actual_sim_analysis.ipynb
@@ -0,0 +1,604 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Need to install lxml"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "8022bfb4-c69b-4cb7-a24c-fec017cc3661",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-27T17:15:52.197541Z",
+     "start_time": "2023-07-27T17:15:52.188326Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import xmltodict\n",
+    "import os\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a143cbbb-3570-4548-bc24-f499c070a880",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Import simulation output\n",
+    "\n",
+    "Before running this, I ran a sim in ecoli/experiments/new_environments.py with -n 0 to generate a simulation output. This is necessary to replicate the simulation environment, where we e.g. have dynamic demand for amino acids and nucleotides, and varying cell volumes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "outputs": [],
+   "source": [
+    "flux_save = pd.DataFrame()\n",
+    "\n",
+    "condition_df = pd.read_excel(\"ecoli/experiments/new_environments_test/done.xlsx\")\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Demand\"] = condition_df[\"Demand\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "\n",
+    "for i in range(len(condition_df)):\n",
+    "    time = '50'\n",
+    "    date = '2023-07-21'\n",
+    "    experiment = 'fba_new_environments'\n",
+    "    env_name = f'{condition_df.loc[i, \"Environment\"]}'\n",
+    "    entry = f'{experiment}_{env_name}_{time}_{date}'\n",
+    "    folder = f'out/fba_new_env/{entry}/'\n",
+    "    try:\n",
+    "        output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "        # output = np.load(r\"out/geneRxnVerifData/output_glc.npy\", allow_pickle=True, encoding='ASCII').tolist()\n",
+    "        output = output['agents']['0']\n",
+    "        fba = output['listeners']['fba_results']\n",
+    "        mass = output['listeners']['mass']\n",
+    "        bulk = pd.DataFrame(output['bulk'])\n",
+    "        # this is the model itself - we'll pull e.g. stoichiometry from here.\n",
+    "        f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "        agent = dill.load(f)\n",
+    "        f.close()\n",
+    "\n",
+    "        metabolism = agent['ecoli-metabolism-redux']\n",
+    "        stoichiometry = metabolism.stoichiometry\n",
+    "\n",
+    "        # sim_exchanges = pd.DataFrame(fba[\"estimated_exchange_dmdt\"])\n",
+    "        # sim_exchanges = pd.DataFrame(sim_exchanges.loc[24, :].abs().sort_values(ascending=False))\n",
+    "        # if os.path.exists(f'out/new_env_analysis/{env_name}'):\n",
+    "        #     pass\n",
+    "        # else:\n",
+    "        #     os.mkdir(f'out/new_env_analysis/{env_name}')\n",
+    "        # sim_exchanges.to_csv(rf'out/new_env_analysis/{env_name}/sim_exchanges.csv')\n",
+    "\n",
+    "        sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "        sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "\n",
+    "        if i == 0:\n",
+    "            flux_save = sim_fluxes\n",
+    "        else:\n",
+    "            new_col = sim_fluxes.iloc[:,0].to_frame()\n",
+    "            flux_save[env_name] = new_col\n",
+    "        # sim_fluxes.to_csv(rf'out/new_env_analysis/{env_name}/sim_fluxes.csv')\n",
+    "        #\n",
+    "        # sim_catalysts = pd.DataFrame(fba[\"reaction_catalyst_counts\"], columns=metabolism.reaction_names)\n",
+    "        # sim_catalysts = pd.DataFrame(sim_catalysts.loc[4, :].abs().sort_values(ascending=False))\n",
+    "        # sim_catalysts.to_csv(rf'out/new_env_analysis/{env_name}/sim_catalysts.csv')\n",
+    "\n",
+    "    except FileNotFoundError:\n",
+    "        pass"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-24T22:28:49.970745Z",
+     "start_time": "2023-07-24T22:24:35.275845Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(flux_save)\n",
+    "flux_save.to_csv(rf'out/new_env_analysis/overall_sim_fluxes.csv')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-24T22:28:50.400495Z",
+     "start_time": "2023-07-24T22:28:49.973553Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Analysis on spreadsheet with all fluxes for all environments"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "outputs": [],
+   "source": [
+    "overall_flux = pd.read_excel(\"out/new_env_analysis/overall_sim_fluxes.xlsx\")\n",
+    "# print(overall_flux.head())"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-01T22:20:06.622442Z",
+     "start_time": "2023-08-01T22:19:54.049957Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "outputs": [],
+   "source": [
+    "array = overall_flux.to_numpy()\n",
+    "\n",
+    "for row in array:\n",
+    "    for i in range(len(row)):\n",
+    "        if i == 0 or i == len(row) - 1:\n",
+    "            continue\n",
+    "        else:\n",
+    "            row[i] = row[i] / (row[len(row) - 1] + 100)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-01T22:20:47.960338Z",
+     "start_time": "2023-08-01T22:20:47.154768Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "outputs": [],
+   "source": [
+    "data = array[:,1:]\n",
+    "\n",
+    "reactions = list(array[:,0])\n",
+    "environments = list(overall_flux.columns)[1:]\n",
+    "\n",
+    "heatmap_df = pd.DataFrame(data, index=reactions, columns=environments)\n",
+    "for env in environments:\n",
+    "    heatmap_df[env] = heatmap_df[env].astype(float)\n",
+    "\n",
+    "heatmap_df.to_csv(rf'out/new_env_analysis/divide_heatmap.csv')\n",
+    "\n",
+    "# heatmap_df = pd.read_excel(\"out/new_env_analysis/absolute_heatmap.xlsx\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-01T22:20:49.811083Z",
+     "start_time": "2023-08-01T22:20:47.976338Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "outputs": [],
+   "source": [
+    "# plt.figure(figsize=(125, 125))\n",
+    "# sns.set(font_scale=0.5)\n",
+    "#\n",
+    "# svm = sns.heatmap(heatmap_df, annot=True, fmt=\".2f\", linewidths=.5, cmap=\"YlGnBu\")\n",
+    "#\n",
+    "# plt.xlabel(\"Environment\")\n",
+    "# plt.ylabel(\"Reaction\")\n",
+    "# figure = svm.get_figure()\n",
+    "# figure.savefig('out/new_env_analysis/heatmap.png', dpi=400)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-24T22:29:04.877183Z",
+     "start_time": "2023-07-24T22:29:04.823865Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-26T17:20:58.359195Z",
+     "start_time": "2023-07-26T17:20:57.655880Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['BRANCHED-CHAINAMINOTRANSFERLEU-RXN (reverse)', 'BRANCHED-CHAINAMINOTRANSFERLEU-RXN__BRANCHED-CHAINAMINOTRANSFER-CPLX', 'BRANCHED-CHAINAMINOTRANSFERLEU-RXN']\n",
+      "KeyError\n",
+      "KeyError\n",
+      "KeyError\n",
+      "[]\n",
+      "[]\n",
+      "[]\n",
+      "['TRANS-RXN-126B']\n",
+      "KeyError\n",
+      "['TRANS-RXN0-270']\n",
+      "KeyError\n",
+      "['RXN0-6979']\n",
+      "KeyError\n",
+      "['ABC-35-RXN']\n",
+      "KeyError\n"
+     ]
+    }
+   ],
+   "source": [
+    "function = \"reactions-of-compound\"\n",
+    "name = \"LEU\"\n",
+    "env = \"Hydrogen_fumarate_-O2_Glc\"\n",
+    "req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{name}&detail=none&fmt=json\"\n",
+    "\n",
+    "r = s.get(req_str)\n",
+    "o = xmltodict.parse(r.content)['ptools-xml']['Reaction']\n",
+    "if type(o) is dict:\n",
+    "    o = [o]\n",
+    "for rxn in o:\n",
+    "    if type(rxn) is not str:\n",
+    "        to_check = []\n",
+    "        for reaction in reactions:\n",
+    "            if rxn['@frameid'] in reaction:\n",
+    "                to_check.append(reaction)\n",
+    "        for reaction in to_check:\n",
+    "            try:\n",
+    "                if heatmap_df.loc[reaction, env] != 0:\n",
+    "                    print(str(heatmap_df.loc[reaction, env]) + f\" {reaction}\")\n",
+    "            except KeyError:\n",
+    "                print(\"KeyError\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-25T22:14:08.830750Z",
+     "start_time": "2023-07-25T22:14:08.730099Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Tool to create graphs of upstream metabolites"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-07-21'\n",
+    "experiment = 'fba_new_environments'\n",
+    "# Change the environment name here\n",
+    "env_name = 'Hydrogen_fumarate_-O2_Glc'\n",
+    "entry = f'{experiment}_{env_name}_{time}_{date}'\n",
+    "folder = f'out/fba_new_env/{entry}/'\n",
+    "\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry\n",
+    "\n",
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])\n",
+    "\n",
+    "sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "\n",
+    "metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "reaction_catalysts = metabolism.parameters['reaction_catalysts']\n",
+    "metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-27T17:17:50.480718Z",
+     "start_time": "2023-07-27T17:17:44.659483Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "outputs": [],
+   "source": [
+    "class Node:\n",
+    "    def __init__(self, val, order):\n",
+    "        self.val = val\n",
+    "        self.nodes = []\n",
+    "        self.order = order\n",
+    "\n",
+    "    def add_node(self, val, order):\n",
+    "        self.nodes.append(Node(val, order))\n",
+    "\n",
+    "    def __repr__(self, level=0):\n",
+    "        ret = \"\\t\"*level+repr(self.val)+\"\\n\"\n",
+    "        for child in self.nodes:\n",
+    "            ret += child.__repr__(level+1)\n",
+    "        return ret"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-26T18:47:24.200051Z",
+     "start_time": "2023-07-26T18:47:24.173216Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "outputs": [],
+   "source": [
+    "index_to_met = {v: k for k, v in metabolites_idx.items()}\n",
+    "met_length = len(metabolism.metabolite_names)\n",
+    "reaction_length = np.shape(stoichiometry)[1]\n",
+    "met_not_include = [\"ATP[c]\", \"WATER[c]\", \"PROTON[p]\", \"Pi[c]\"]\n",
+    "do_not_include = []\n",
+    "\n",
+    "for met in met_not_include:\n",
+    "    do_not_include.append(metabolites_idx[met])\n",
+    "\n",
+    "def find_upstream_metabolites(metabolite):\n",
+    "    to_return = []\n",
+    "    index = metabolites_idx[metabolite]\n",
+    "    for k in range(reaction_length):\n",
+    "        if stoichiometry[index, k] > 0:\n",
+    "            for l in range(met_length):\n",
+    "                if stoichiometry[l, k] < 0:\n",
+    "                    if l != index and l not in do_not_include and index_to_met[l] not in to_return:\n",
+    "                        to_return.append(index_to_met[l])\n",
+    "    return to_return"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-26T18:57:12.910710Z",
+     "start_time": "2023-07-26T18:57:12.902344Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'LEU[c]'\n",
+      "\t'LEU[p]'\n",
+      "\t\t'LEU[c]'\n",
+      "\t\t'LEU[e]'\n",
+      "\t'2K-4CH3-PENTANOATE[c]'\n",
+      "\t\t'2-KETOGLUTARATE[c]'\n",
+      "\t\t'LEU[c]'\n",
+      "\t\t'2-D-THREO-HYDROXY-3-CARBOXY-ISOCAPROATE[c]'\n",
+      "\t\t'NAD[c]'\n",
+      "\t\t'CPD-7100[c]'\n",
+      "\t\t'PROTON[c]'\n",
+      "\t'GLT[c]'\n",
+      "\t\t'2-KETOGLUTARATE[c]'\n",
+      "\t\t'Aromatic-Amino-Acids[c]'\n",
+      "\t\t'PUTRESCINE[c]'\n",
+      "\t\t'5-OXOPROLINE[c]'\n",
+      "\t\t'GLT[p]'\n",
+      "\t\t'L-ALPHA-ALANINE[c]'\n",
+      "\t\t'CHORISMATE[c]'\n",
+      "\t\t'GLN[c]'\n",
+      "\t\t'L-ASPARTATE[c]'\n",
+      "\t\t'ILE[c]'\n",
+      "\t\t'LEU[c]'\n",
+      "\t\t'VAL[c]'\n",
+      "\t\t'HCO3[c]'\n",
+      "\t\t'UTP[c]'\n",
+      "\t\t'CYS[c]'\n",
+      "\t\t'5-P-RIBOSYL-N-FORMYLGLYCINEAMIDE[c]'\n",
+      "\t\t'4-AMINO-BUTYRATE[c]'\n",
+      "\t\t'NADPH[c]'\n",
+      "\t\t'PROTON[c]'\n",
+      "\t\t'PHOSPHORIBULOSYL-FORMIMINO-AICAR-P[c]'\n",
+      "\t\t'AMMONIUM[c]'\n",
+      "\t\t'D-GLT[c]'\n",
+      "\t\t'XANTHOSINE-5-PHOSPHATE[c]'\n",
+      "\t\t'L-HISTIDINOL-P[c]'\n",
+      "\t\t'FRUCTOSE-6P[c]'\n",
+      "\t\t'ACETYL-GLU[c]'\n",
+      "\t\t'CO-A[c]'\n",
+      "\t\t'DEAMIDO-NAD[c]'\n",
+      "\t\t'PRPP[c]'\n",
+      "\t\t'3-P-SERINE[c]'\n",
+      "\t\t'4-PHOSPHONOOXY-THREONINE[c]'\n",
+      "\t\t'L-DELTA1-PYRROLINE_5-CARBOXYLATE[c]'\n",
+      "\t\t'NAD[c]'\n",
+      "\t\t'MET[c]'\n",
+      "\t\t'PHE[c]'\n",
+      "\t\t'L-GLUTAMATE_GAMMA-SEMIALDEHYDE[c]'\n",
+      "\t\t'CPD-14736[c]'\n",
+      "\t\t'UDP-4-AMINO-4-DEOXY-L-ARABINOSE[c]'\n",
+      "\t\t'CPD-9000[c]'\n",
+      "\t\t'CPD0-889[c]'\n",
+      "\t\t'CPD0-1445[c]'\n",
+      "\t\t'CPD-3569[c]'\n",
+      "\t\t'N2-SUCCINYLGLUTAMATE[c]'\n",
+      "\t\t'N2-SUCCINYLORNITHINE[c]'\n",
+      "\t\t'NA+[p]'\n",
+      "\t\t'TYR[c]'\n",
+      "\t\t'5-AMINOPENTANOATE[c]'\n",
+      "\t'CPD-13398[c]'\n",
+      "\t\t'CPD-13398[p]'\n",
+      "\t'NA+[p]'\n",
+      "\t\t'NA+[c]'\n",
+      "\t\t'NA+[e]'\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "metabolite = \"LEU[c]\"\n",
+    "\n",
+    "root = Node(metabolite, 0)\n",
+    "for i in range(2):\n",
+    "    if i == 0:\n",
+    "        for met in find_upstream_metabolites(metabolite):\n",
+    "            root.add_node(met, 1)\n",
+    "    else:\n",
+    "        for j in range(len(root.nodes)):\n",
+    "            for met in find_upstream_metabolites(root.nodes[j].val):\n",
+    "                if met not in root.nodes[j].nodes:\n",
+    "                    root.nodes[j].add_node(met, 2)\n",
+    "\n",
+    "print(root)\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-26T23:14:41.070621Z",
+     "start_time": "2023-07-26T23:14:38.876655Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Pathway visualization of changing fluxes"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                            24\n",
+      "R601-RXN-FUM/REDUCED-MENAQUINONE//SUC/CPD-9728.38.  20133261.0\n",
+      "RXN0-5256-CPD-9728/PROTON/HYDROGEN-MOLECULE//RE...  20101063.0\n",
+      "TRANS-RXN-106                                       20033460.0\n",
+      "TRANS-RXN-145                                       10477226.0\n",
+      "ATPSYN-RXN (reverse)                                 9599983.0\n",
+      "...                                                        ...\n",
+      "RXN-21538                                                  0.0\n",
+      "RXN-21533-CPD-23688/ATP//CPD-23428/ADP/PROTON.36.          0.0\n",
+      "RXN-21533-CPD-23431/ATP//CPD-23690/ADP/PROTON.36.          0.0\n",
+      "RXN-21533-CPD-23431/ATP//CPD-23428/ADP/PROTON.36.          0.0\n",
+      "RXN0-7200-NMNH//NMNH.11. (reverse)                         0.0\n",
+      "\n",
+      "[7504 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-27T17:17:53.121382Z",
+     "start_time": "2023-07-27T17:17:53.109559Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ecoli/experiments/new_environments_test/done.xlsx b/ecoli/experiments/new_environments_test/done.xlsx
new file mode 100644
index 000000000..a34ba1b47
Binary files /dev/null and b/ecoli/experiments/new_environments_test/done.xlsx differ
diff --git a/ecoli/experiments/new_environments_test/flux_visualization.ipynb b/ecoli/experiments/new_environments_test/flux_visualization.ipynb
new file mode 100644
index 000000000..928989c17
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/flux_visualization.ipynb
@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 586,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:31.735361Z",
+     "start_time": "2023-08-03T00:33:30.987501Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from pyvis.network import Network\n",
+    "import math\n",
+    "import community as community_louvain\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import xmltodict\n",
+    "import os\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 587,
+   "outputs": [],
+   "source": [
+    "# Change the environment name here\n",
+    "env_name = 'Hydrogen_fumarate_-O2_Glc'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:31.740490Z",
+     "start_time": "2023-08-03T00:33:31.055939Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 588,
+   "outputs": [],
+   "source": [
+    "condition_df = pd.read_excel(\"ecoli/experiments/new_environments_test/done.xlsx\")\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "\n",
+    "row_num = condition_df[condition_df['Environment'] == env_name].index[0]\n",
+    "important_add = condition_df[\"Add\"].loc[row_num]\n",
+    "important_remove = condition_df[\"Remove\"].loc[row_num]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:31.807191Z",
+     "start_time": "2023-08-03T00:33:31.151740Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 589,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-07-21'\n",
+    "experiment = 'fba_new_environments'\n",
+    "entry = f'{experiment}_{env_name}_{time}_{date}'\n",
+    "folder = f'out/fba_new_env/{entry}/'\n",
+    "\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry\n",
+    "\n",
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])\n",
+    "\n",
+    "sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "\n",
+    "metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "index_to_met = {v: k for k, v in metabolites_idx.items()}\n",
+    "\n",
+    "reaction_idx = {reaction: i for i, reaction in enumerate(metabolism.reaction_names)}\n",
+    "index_to_rxn = {v: k for k, v in reaction_idx.items()}\n",
+    "\n",
+    "metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:43.697106Z",
+     "start_time": "2023-08-03T00:33:31.466493Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 590,
+   "outputs": [],
+   "source": [
+    "homeostatic = pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:43.716645Z",
+     "start_time": "2023-08-03T00:33:43.708629Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 591,
+   "outputs": [],
+   "source": [
+    "heatmap_df = pd.read_excel(\"out/new_env_analysis/heatmap.xlsx\")\n",
+    "heatmap_df = heatmap_df[heatmap_df[env_name] != 0]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:56.791162Z",
+     "start_time": "2023-08-03T00:33:43.724032Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Building the graph"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 592,
+   "outputs": [],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "\n",
+    "met_length = len(metabolism.metabolite_names)\n",
+    "reaction_length = np.shape(stoichiometry)[1]\n",
+    "met_not_include = [\"ATP[c]\", \"WATER[c]\", \"PROTON[p]\", \"Pi[c]\", \"PROTON[c]\", \"ADP[c]\"]\n",
+    "# met_not_include = []\n",
+    "do_not_include = []\n",
+    "\n",
+    "for met in met_not_include:\n",
+    "    do_not_include.append(metabolites_idx[met])\n",
+    "\n",
+    "def plot_reaction(G, reaction, flux, color):\n",
+    "    index = reaction_idx[reaction]\n",
+    "    reactants = []\n",
+    "    products = []\n",
+    "    clean_name = clean_reaction_name(reaction)\n",
+    "    for k in range(met_length):\n",
+    "        if stoichiometry[k, index] > 0 and index_to_met[k] not in met_not_include:\n",
+    "            products.append(index_to_met[k])\n",
+    "        elif stoichiometry[k, index] < 0 and index_to_met[k] not in met_not_include:\n",
+    "            reactants.append(index_to_met[k])\n",
+    "\n",
+    "    for reactant in reactants:\n",
+    "        # nt.add_node(reactant, label=reactant, color='lightblue', shape='star', size=10)\n",
+    "        # nt.add_node(clean_name, label=clean_name, color='lightblue', shape='dot', size=10)\n",
+    "        # nt.add_edge(reactant, clean_name, color=color, title=flux, arrowStrikethrough=False)\n",
+    "\n",
+    "        G.add_edge(reactant, clean_name, color=color, title=flux)\n",
+    "\n",
+    "    for product in products:\n",
+    "        # nt.add_node(product, label=product, color='lightblue', shape='star', size=10)\n",
+    "        # nt.add_node(clean_name, label=clean_name, color='lightblue', shape='dot', size=10)\n",
+    "        # nt.add_edge(clean_name, product, color=color, title=flux, arrowStrikethrough=False)\n",
+    "\n",
+    "        G.add_edge(clean_name, product, color=color, title=flux)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:56.823990Z",
+     "start_time": "2023-08-03T00:33:56.792026Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 593,
+   "outputs": [],
+   "source": [
+    "# Not currently used in this analysis but can be useful later\n",
+    "\n",
+    "def clean_reaction_name(reaction):\n",
+    "    if '/' in reaction:\n",
+    "        index = reaction.find('RXN')\n",
+    "        if (reaction[index+3] == '-' and reaction[index+4].isdigit()) or (reaction[index+4] == '-' and reaction[index+5].isdigit()):\n",
+    "            index = index + 4\n",
+    "            if reaction[index] == '-':\n",
+    "                index += 1\n",
+    "            while reaction[index].isdigit():\n",
+    "                index += 1\n",
+    "            reaction = reaction[:index]\n",
+    "        else:\n",
+    "            reaction = reaction[:index+3]\n",
+    "    elif '__' in reaction:\n",
+    "        reaction = reaction[:reaction.find('__')]\n",
+    "    elif '(reverse)' in reaction:\n",
+    "        reaction = reaction[:reaction.find('(reverse)') - 1]\n",
+    "    elif '[' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[') - 1]\n",
+    "    elif ' ' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[')]\n",
+    "    return reaction\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:33:56.865064Z",
+     "start_time": "2023-08-03T00:33:56.830643Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 594,
+   "outputs": [],
+   "source": [
+    "k = 30\n",
+    "heatmap_df.sort_values(by=[env_name], ascending=False, inplace=True)\n",
+    "top_increase = [heatmap_df.iloc[i, 0] for i in range(k)]\n",
+    "bottom_increase = [heatmap_df.iloc[i, 0] for i in range(-1, -1*k - 1, -1)]\n",
+    "\n",
+    "if \"Non-zero Values for each environment\" in top_increase:\n",
+    "    top_increase.remove(\"Non-zero Values for each environment\")\n",
+    "elif \"Non-zero Values for each environment\" in bottom_increase:\n",
+    "    bottom_increase.remove(\"Non-zero Values for each environment\")\n",
+    "\n",
+    "for reaction in top_increase:\n",
+    "    row_num = heatmap_df[heatmap_df['Reactions'] == reaction].index[0]\n",
+    "    plot_reaction(G, reaction, math.log(heatmap_df[env_name].loc[row_num], 10), 'green')\n",
+    "\n",
+    "for reaction in bottom_increase:\n",
+    "    row_num = heatmap_df[heatmap_df['Reactions'] == reaction].index[0]\n",
+    "    plot_reaction(G, reaction, math.log(heatmap_df[env_name].loc[row_num], 10), 'red')\n",
+    "\n",
+    "# nx.write_graphml(G, f'out/new_env_analysis/{env_name}_top10_graph.graphml')\n",
+    "\n",
+    "# nt = Network('1000px', '2000px', notebook=True, cdn_resources='in_line')\n",
+    "# nt.from_nx(G)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:34:01.851567Z",
+     "start_time": "2023-08-03T00:33:56.857635Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Graph Analysis using NetworkX"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 595,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Degree Centrality: [('R15-RXN', 0.06622516556291391), ('PYRUVATE[c]', 0.046357615894039736), ('PYFLAVOXRE-RXN', 0.039735099337748346), ('CARBON-DIOXIDE[c]', 0.039735099337748346), ('ACETYL-COA[c]', 0.033112582781456956), ('NADH[c]', 0.033112582781456956), ('NAD[c]', 0.033112582781456956), ('MALIC-NADP-RXN', 0.033112582781456956), ('GAP[c]', 0.033112582781456956), ('ACETALD-DEHYDROG-RXN', 0.033112582781456956), ('FUM[c]', 0.026490066225165563), ('R601-RXN', 0.026490066225165563), ('TRANS-RXN-106', 0.026490066225165563), ('FORMATE[c]', 0.026490066225165563), ('CO-A[c]', 0.026490066225165563), ('PYRUVFORMLY-RXN', 0.026490066225165563), ('NADPH[c]', 0.026490066225165563), ('FLAVONADPREDUCT-RXN', 0.026490066225165563), ('NADP[c]', 0.026490066225165563), ('MALATE-DEH-RXN', 0.026490066225165563), ('GAPOXNPHOSPHN-RXN', 0.026490066225165563), ('DIHYDROOROTATE-DEHYDROGENASE-RXN', 0.026490066225165563), ('THIOREDOXIN-REDUCT-NADPH-RXN', 0.026490066225165563), ('RXN0-5055', 0.026490066225165563), ('DXS-RXN', 0.026490066225165563), ('GTP-CYCLOHYDRO-II-RXN', 0.026490066225165563), ('RIBOFLAVINSYNREDUC-RXN', 0.026490066225165563), ('GLUC1PURIDYLTRANS-RXN', 0.026490066225165563), ('DHBDEHYD-RXN', 0.026490066225165563), ('GLYCOLALD-DEHYDROG-RXN', 0.026490066225165563), ('REDUCED-MENAQUINONE[i]', 0.019867549668874173), ('CPD-9728[i]', 0.019867549668874173), ('SUC[c]', 0.019867549668874173), ('RXN0-5256', 0.019867549668874173), ('PEPCARBOXYKIN-RXN', 0.019867549668874173), ('PHOSPHO-ENOL-PYRUVATE[c]', 0.019867549668874173), ('MAL[c]', 0.019867549668874173), ('ISOCIT-CLEAV-RXN', 0.019867549668874173), ('DIHYDROXY-ACETONE-PHOSPHATE[c]', 0.019867549668874173), ('RXN0-313', 0.019867549668874173), ('F16ALDOLASE-RXN', 0.019867549668874173), ('DAHPSYN-RXN', 0.019867549668874173), ('BIOTIN-CARBOXYL-RXN', 0.019867549668874173), ('RXN0-310', 0.019867549668874173), ('3.2.2.10-RXN', 0.019867549668874173), ('ACETYL-COA-ACETYLTRANSFER-RXN', 0.019867549668874173), ('RIBOFLAVIN-SYN-RXN', 0.019867549668874173), ('AMINO-RIBOSYLAMINO-1H-3H-PYR-DIONE[c]', 0.019867549668874173), ('RIBOFLAVINSYNDEAM-RXN', 0.019867549668874173), ('CHORPYRLY-RXN', 0.019867549668874173), ('LUMAZINESYN-RXN', 0.019867549668874173), ('DIOHBUTANONEPSYN-RXN', 0.019867549668874173), ('ISOCHORMAT-RXN', 0.019867549668874173), ('FUM[p]', 0.013245033112582781), ('TRANS-RXN-1', 0.013245033112582781), ('Reduced-flavodoxins[c]', 0.013245033112582781), ('Oxidized-flavodoxins[c]', 0.013245033112582781), ('TRANS-RXN0-517', 0.013245033112582781), ('OXALACETIC_ACID[c]', 0.013245033112582781), ('2PGADEHYDRAT-RXN', 0.013245033112582781), ('2-PG[c]', 0.013245033112582781), ('3PGAREARR-RXN', 0.013245033112582781), ('G3P[c]', 0.013245033112582781), ('FUMHYDR-RXN', 0.013245033112582781), ('GLYOX[c]', 0.013245033112582781), ('THREO-DS-ISO-CITRATE[c]', 0.013245033112582781), ('CPD-479[c]', 0.013245033112582781), ('MET[c]', 0.013245033112582781), ('PHOSGLYPHOS-RXN', 0.013245033112582781), ('DPG[c]', 0.013245033112582781), ('TRIOSEPISOMERIZATION-RXN', 0.013245033112582781), ('RXN0-7249', 0.013245033112582781), ('DIHYDROXYACETONE[c]', 0.013245033112582781), ('FRUCTOSE-6P[c]', 0.013245033112582781), ('FRUCTOSE-16-DIPHOSPHATE[c]', 0.013245033112582781), ('F16BDEPHOS-RXN', 0.013245033112582781), ('TRANS-RXN0-474', 0.013245033112582781), ('OXYGEN-MOLECULE[c]', 0.013245033112582781), ('biotin-L-lysine-in-BCCP-dimers[c]', 0.013245033112582781), ('carboxybiotin-L-lysine-in-BCCP-dimers[c]', 0.013245033112582781), ('METHYLMALONYL-COA-MUT-RXN', 0.013245033112582781), ('METHYL-MALONYL-COA[c]', 0.013245033112582781), ('CMP[c]', 0.013245033112582781), ('RXN-14026', 0.013245033112582781), ('NUCLEOSIDE-DIPHOSPHATASE-RXN', 0.013245033112582781), ('TRANS-RXN-141A', 0.013245033112582781), ('RXN0-5038', 0.013245033112582781), ('ACONITATEHYDR-RXN', 0.013245033112582781), ('RIBOFLAVIN[c]', 0.013245033112582781), ('RIBOFLAVINKIN-RXN', 0.013245033112582781), ('RXN0-5185', 0.013245033112582781), ('DIMETHYL-D-RIBITYL-LUMAZINE[c]', 0.013245033112582781), ('CPD-1086[c]', 0.013245033112582781), ('RIBOPHOSPHAT-RXN', 0.013245033112582781), ('DIAMINO-OH-PHOSPHORIBOSYLAMINO-PYR[c]', 0.013245033112582781), ('PPI[c]', 0.013245033112582781), ('CPD-602[c]', 0.013245033112582781), ('CATAL-RXN', 0.013245033112582781), ('GMKALT-RXN', 0.013245033112582781), ('TRANS-RXN0-545', 0.013245033112582781), ('DIHYDROXY-BUTANONE-P[c]', 0.013245033112582781), ('DIHYDRO-DIOH-BENZOATE[c]', 0.013245033112582781), ('HYDROGEN-MOLECULE[p]', 0.006622516556291391), ('SUC[p]', 0.006622516556291391), ('FORMATE[p]', 0.006622516556291391), ('GLY[c]', 0.006622516556291391), ('2-KETOGLUTARATE[c]', 0.006622516556291391), ('GLT[c]', 0.006622516556291391), ('ACETALD[c]', 0.006622516556291391), ('L-ALPHA-ALANINE[c]', 0.006622516556291391), ('DI-H-OROTATE[c]', 0.006622516556291391), ('OROTATE[c]', 0.006622516556291391), ('ERYTHROSE-4P[c]', 0.006622516556291391), ('3-DEOXY-D-ARABINO-HEPTULOSONATE-7-P[c]', 0.006622516556291391), ('Oxidized-CcmH-Proteins[c]', 0.006622516556291391), ('Reduced-CcmH-Proteins[c]', 0.006622516556291391), ('OXYGEN-MOLECULE[p]', 0.006622516556291391), ('HCO3[c]', 0.006622516556291391), ('MALONYL-COA[c]', 0.006622516556291391), ('SUC-COA[c]', 0.006622516556291391), ('PROPIONYL-COA[c]', 0.006622516556291391), ('CYTIDINE[c]', 0.006622516556291391), ('CYTOSINE[c]', 0.006622516556291391), ('RIBOSE-5P[c]', 0.006622516556291391), ('DADP[c]', 0.006622516556291391), ('DAMP[c]', 0.006622516556291391), ('CO+2[p]', 0.006622516556291391), ('CO+2[c]', 0.006622516556291391), ('CAMP[c]', 0.006622516556291391), ('AMP[c]', 0.006622516556291391), ('DEOXYXYLULOSE-5P[c]', 0.006622516556291391), ('CIS-ACONITATE[c]', 0.006622516556291391), ('ACETOACETYL-COA[c]', 0.006622516556291391), ('FMN[c]', 0.006622516556291391), ('CPD-2961[c]', 0.006622516556291391), ('GLUCONATE[c]', 0.006622516556291391), ('GTP[c]', 0.006622516556291391), ('AMMONIUM[c]', 0.006622516556291391), ('HYDROGEN-PEROXIDE[c]', 0.006622516556291391), ('CHORISMATE[c]', 0.006622516556291391), ('4-hydroxybenzoate[c]', 0.006622516556291391), ('DGDP[c]', 0.006622516556291391), ('DGMP[c]', 0.006622516556291391), ('CARBON-DIOXIDE[p]', 0.006622516556291391), ('GLC-1-P[c]', 0.006622516556291391), ('UTP[c]', 0.006622516556291391), ('CPD-12575[c]', 0.006622516556291391), ('RIBULOSE-5P[c]', 0.006622516556291391), ('2-3-DIHYDROXYBENZOATE[c]', 0.006622516556291391), ('ISOCHORISMATE[c]', 0.006622516556291391), ('GLYCOLALDEHYDE[c]', 0.006622516556291391), ('GLYCOLLATE[c]', 0.006622516556291391)]\n",
+      "\n",
+      "Closeness Centrality: [('CARBON-DIOXIDE[c]', 0.08401634865473302), ('PYRUVATE[c]', 0.081332120263208), ('DXS-RXN', 0.07857084457525958), ('R15-RXN', 0.07823283373997908), ('PYFLAVOXRE-RXN', 0.0766775712120003), ('PYRUVFORMLY-RXN', 0.07315216563903479), ('ISOCIT-CLEAV-RXN', 0.07215057511327988), ('TRANS-RXN0-545', 0.07181630658994681), ('CO-A[c]', 0.07091073437990002), ('MALIC-NADP-RXN', 0.07071376011773363), ('FORMATE[c]', 0.07030184032746514), ('GLYOX[c]', 0.06835383159886471), ('GLT[c]', 0.06835383159886471), ('L-ALPHA-ALANINE[c]', 0.06835383159886471), ('DEOXYXYLULOSE-5P[c]', 0.06791929770522101), ('CPD-479[c]', 0.06665577762599115), ('MET[c]', 0.06665577762599115), ('FLAVONADPREDUCT-RXN', 0.06629415011037527), ('Oxidized-flavodoxins[c]', 0.06510729831811791), ('THREO-DS-ISO-CITRATE[c]', 0.0640045978813172), ('CARBON-DIOXIDE[p]', 0.06308332049899892), ('ACETALD-DEHYDROG-RXN', 0.06270185626202981), ('ACETYL-COA[c]', 0.0625477976471354), ('TRANS-RXN-1', 0.06217172870518384), ('RXN0-5055', 0.06206362593150741), ('NADP[c]', 0.06193905995713895), ('NADPH[c]', 0.060756452607121966), ('NAD[c]', 0.05989871445266848), ('ACONITATEHYDR-RXN', 0.05775796878938564), ('Reduced-flavodoxins[c]', 0.05746490664195058), ('ACETALD[c]', 0.05604428190174953), ('FORMATE[p]', 0.05598583205497562), ('MALONYL-COA[c]', 0.05598266280857659), ('RIBOFLAVINSYNREDUC-RXN', 0.05558286821481246), ('biotin-L-lysine-in-BCCP-dimers[c]', 0.054748576097959585), ('ACETYL-COA-ACETYLTRANSFER-RXN', 0.05462865588494443), ('DHBDEHYD-RXN', 0.0539342238186104), ('THIOREDOXIN-REDUCT-NADPH-RXN', 0.05370665325397491), ('GLYCOLALD-DEHYDROG-RXN', 0.053035320088300225), ('CIS-ACONITATE[c]', 0.05281711665817626), ('MAL[c]', 0.05081228271932955), ('CPD-1086[c]', 0.05045061077144056), ('AMINO-RIBOSYLAMINO-1H-3H-PYR-DIONE[c]', 0.04975276596164665), ('ACETOACETYL-COA[c]', 0.04968765257451896), ('BIOTIN-CARBOXYL-RXN', 0.049300956585724795), ('2-3-DIHYDROXYBENZOATE[c]', 0.04913040787274865), ('Reduced-CcmH-Proteins[c]', 0.04894742683784084), ('NADH[c]', 0.04867486356096387), ('GLYCOLLATE[c]', 0.04840657129267133), ('RIBOPHOSPHAT-RXN', 0.0463689364351616), ('GAPOXNPHOSPHN-RXN', 0.04611766964200019), ('LUMAZINESYN-RXN', 0.04592036583255144), ('carboxybiotin-L-lysine-in-BCCP-dimers[c]', 0.04457043001677969), ('FRUCTOSE-6P[c]', 0.0445121412803532), ('MALATE-DEH-RXN', 0.044043172391668), ('RXN0-313', 0.043977036717739726), ('GAP[c]', 0.042217170219542466), ('DIMETHYL-D-RIBITYL-LUMAZINE[c]', 0.04199258611354075), ('F16ALDOLASE-RXN', 0.04182381532187014), ('TRIOSEPISOMERIZATION-RXN', 0.03946661893681762), ('FRUCTOSE-16-DIPHOSPHATE[c]', 0.03924854710095959), ('RIBOFLAVIN-SYN-RXN', 0.038683784426726424), ('DIHYDROXY-ACETONE-PHOSPHATE[c]', 0.03715866823913635), ('F16BDEPHOS-RXN', 0.03706792621096266), ('RIBOFLAVIN[c]', 0.03671778163402628), ('RXN0-7249', 0.03519513515764921), ('RIBOFLAVINKIN-RXN', 0.03501130032586987), ('FMN[c]', 0.033516544050140165), ('DIHYDROXYACETONE[c]', 0.03350485728130696), ('R601-RXN', 0.03205298013245033), ('CPD-9728[i]', 0.024282560706401765), ('SUC[c]', 0.024282560706401765), ('REDUCED-MENAQUINONE[i]', 0.021657419008412386), ('RXN0-5256', 0.021657419008412386), ('TRANS-RXN-106', 0.021657419008412386), ('DIHYDROOROTATE-DEHYDROGENASE-RXN', 0.021657419008412386), ('PPI[c]', 0.020695364238410598), ('FUM[c]', 0.019544500080762396), ('SUC[p]', 0.01946208947155021), ('OROTATE[c]', 0.01946208947155021), ('FUMHYDR-RXN', 0.017993252530301138), ('OXYGEN-MOLECULE[c]', 0.017660044150110375), ('DAHPSYN-RXN', 0.015137180700094607), ('3-DEOXY-D-ARABINO-HEPTULOSONATE-7-P[c]', 0.013796909492273732), ('GLUC1PURIDYLTRANS-RXN', 0.013245033112582781), ('CPD-12575[c]', 0.011920529801324504), ('DPG[c]', 0.01177336276674025), ('PHOSGLYPHOS-RXN', 0.011589403973509934), ('G3P[c]', 0.011352885525070956), ('3PGAREARR-RXN', 0.011037527593818985), ('2-PG[c]', 0.010596026490066225), ('PROPIONYL-COA[c]', 0.010596026490066225), ('CPD-602[c]', 0.010596026490066225), ('AMMONIUM[c]', 0.010596026490066225), ('2PGADEHYDRAT-RXN', 0.009933774834437087), ('RXN0-310', 0.009933774834437087), ('RIBOFLAVINSYNDEAM-RXN', 0.009933774834437087), ('PHOSPHO-ENOL-PYRUVATE[c]', 0.008830022075055188), ('METHYL-MALONYL-COA[c]', 0.008830022075055188), ('CYTIDINE[c]', 0.008830022075055188), ('CYTOSINE[c]', 0.008830022075055188), ('RIBOSE-5P[c]', 0.008830022075055188), ('DAMP[c]', 0.008830022075055188), ('CO+2[c]', 0.008830022075055188), ('AMP[c]', 0.008830022075055188), ('GLUCONATE[c]', 0.008830022075055188), ('DIAMINO-OH-PHOSPHORIBOSYLAMINO-PYR[c]', 0.008830022075055188), ('4-hydroxybenzoate[c]', 0.008830022075055188), ('DGMP[c]', 0.008830022075055188), ('DIHYDROXY-BUTANONE-P[c]', 0.008830022075055188), ('DIHYDRO-DIOH-BENZOATE[c]', 0.008830022075055188), ('TRANS-RXN0-517', 0.006622516556291391), ('PEPCARBOXYKIN-RXN', 0.006622516556291391), ('TRANS-RXN0-474', 0.006622516556291391), ('METHYLMALONYL-COA-MUT-RXN', 0.006622516556291391), ('RXN-14026', 0.006622516556291391), ('3.2.2.10-RXN', 0.006622516556291391), ('NUCLEOSIDE-DIPHOSPHATASE-RXN', 0.006622516556291391), ('TRANS-RXN-141A', 0.006622516556291391), ('RXN0-5038', 0.006622516556291391), ('RXN0-5185', 0.006622516556291391), ('GTP-CYCLOHYDRO-II-RXN', 0.006622516556291391), ('CATAL-RXN', 0.006622516556291391), ('CHORPYRLY-RXN', 0.006622516556291391), ('GMKALT-RXN', 0.006622516556291391), ('DIOHBUTANONEPSYN-RXN', 0.006622516556291391), ('ISOCHORMAT-RXN', 0.006622516556291391), ('HYDROGEN-MOLECULE[p]', 0.0), ('FUM[p]', 0.0), ('OXALACETIC_ACID[c]', 0.0), ('GLY[c]', 0.0), ('2-KETOGLUTARATE[c]', 0.0), ('DI-H-OROTATE[c]', 0.0), ('ERYTHROSE-4P[c]', 0.0), ('Oxidized-CcmH-Proteins[c]', 0.0), ('OXYGEN-MOLECULE[p]', 0.0), ('HCO3[c]', 0.0), ('SUC-COA[c]', 0.0), ('CMP[c]', 0.0), ('DADP[c]', 0.0), ('CO+2[p]', 0.0), ('CAMP[c]', 0.0), ('CPD-2961[c]', 0.0), ('GTP[c]', 0.0), ('HYDROGEN-PEROXIDE[c]', 0.0), ('CHORISMATE[c]', 0.0), ('DGDP[c]', 0.0), ('GLC-1-P[c]', 0.0), ('UTP[c]', 0.0), ('RIBULOSE-5P[c]', 0.0), ('ISOCHORISMATE[c]', 0.0), ('GLYCOLALDEHYDE[c]', 0.0)]\n",
+      "Average Clustering: 0.0\n",
+      "Transitivity: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "degree = nx.degree_centrality(G)\n",
+    "sort_degree = sorted(degree.items(), key=lambda x:x[1], reverse=True)\n",
+    "print(\"Degree Centrality: \"+ str(sort_degree) + \"\\n\")\n",
+    "\n",
+    "closeness = nx.closeness_centrality(G)\n",
+    "sort_closeness = sorted(closeness.items(), key=lambda x:x[1], reverse=True)\n",
+    "print(\"Closeness Centrality: \"+ str(sort_closeness))\n",
+    "\n",
+    "print(\"Average Clustering: \" + str(nx.average_clustering(G)))\n",
+    "print(\"Transitivity: \" + str(nx.transitivity(G)))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:34:01.907829Z",
+     "start_time": "2023-08-03T00:34:01.878167Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 596,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "out/new_env_analysis/flux_visualization/Hydrogen_fumarate_-O2_Glc_top30_network.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<IPython.lib.display.IFrame at 0x152a21f40>",
+      "text/html": "\n        <iframe\n            width=\"3000px\"\n            height=\"3000px\"\n            src=\"out/new_env_analysis/flux_visualization/Hydrogen_fumarate_-O2_Glc_top30_network.html\"\n            frameborder=\"0\"\n            allowfullscreen\n            \n        ></iframe>\n        "
+     },
+     "execution_count": 596,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sort_closeness = sort_closeness[:8]\n",
+    "nt = Network('1000px', '1500px', notebook=True, cdn_resources='in_line', directed=True)\n",
+    "nt.from_nx(G)\n",
+    "\n",
+    "for tup in sort_closeness:\n",
+    "    nt.get_node(tup[0])['color'] = 'blue'\n",
+    "\n",
+    "for n in nt.nodes:\n",
+    "    if n['label'] in metabolites_idx:\n",
+    "        n['shape'] = 'star'\n",
+    "    if n['label'] in important_add:\n",
+    "        n['color'] = 'orange'\n",
+    "    if n['label'] in important_remove:\n",
+    "        n['color'] = 'purple'\n",
+    "\n",
+    "\n",
+    "nt.toggle_physics(True)\n",
+    "nt.show_buttons(filter_=['physics'])\n",
+    "nt.show(f'out/new_env_analysis/flux_visualization/{env_name}_top{k}_network.html')\n",
+    "\n",
+    "# plt.figure(figsize=(25, 25))\n",
+    "# nx.draw_spring(G, with_labels=True, font_size=10, node_size=1000, node_color='lightblue', edge_color='black', width=1.5)\n",
+    "# plt.savefig(f'out/new_env_analysis/{env_name}_top10_graph.png', dpi=300)\n",
+    "# plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:34:02.132013Z",
+     "start_time": "2023-08-03T00:34:01.908225Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 596,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T00:34:02.174970Z",
+     "start_time": "2023-08-03T00:34:02.133843Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ecoli/experiments/new_environments_test/metabolite_visualization.ipynb b/ecoli/experiments/new_environments_test/metabolite_visualization.ipynb
new file mode 100644
index 000000000..1b81c84cc
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/metabolite_visualization.ipynb
@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:29:54.719541Z",
+     "start_time": "2023-08-03T18:29:54.669794Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from pyvis.network import Network\n",
+    "import math\n",
+    "import community as community_louvain\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import xmltodict\n",
+    "import os\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "outputs": [],
+   "source": [
+    "# Change the environment name here\n",
+    "env_name = 'Nitrate_as_reduction_target'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:29:54.906026Z",
+     "start_time": "2023-08-03T18:29:54.676289Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "outputs": [],
+   "source": [
+    "condition_df = pd.read_excel(\"ecoli/experiments/new_environments_test/done.xlsx\")\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "\n",
+    "row_num = condition_df[condition_df['Environment'] == env_name].index[0]\n",
+    "important_add = condition_df[\"Add\"].loc[row_num]\n",
+    "important_remove = condition_df[\"Remove\"].loc[row_num]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:29:54.909544Z",
+     "start_time": "2023-08-03T18:29:54.684199Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-07-21'\n",
+    "experiment = 'fba_new_environments'\n",
+    "entry = f'{experiment}_{env_name}_{time}_{date}'\n",
+    "folder = f'out/fba_new_env/{entry}/'\n",
+    "\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry\n",
+    "\n",
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])\n",
+    "\n",
+    "sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "\n",
+    "metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "index_to_met = {v: k for k, v in metabolites_idx.items()}\n",
+    "\n",
+    "reaction_idx = {reaction: i for i, reaction in enumerate(metabolism.reaction_names)}\n",
+    "index_to_rxn = {v: k for k, v in reaction_idx.items()}\n",
+    "\n",
+    "metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:01.825262Z",
+     "start_time": "2023-08-03T18:29:54.736419Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "outputs": [],
+   "source": [
+    "heatmap_df = pd.read_excel(\"out/new_env_analysis/heatmap.xlsx\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:11.511426Z",
+     "start_time": "2023-08-03T18:30:01.831321Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Building the Graph"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "outputs": [],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "\n",
+    "met_length = len(metabolism.metabolite_names)\n",
+    "reaction_length = np.shape(stoichiometry)[1]\n",
+    "met_not_include = [\"ATP[c]\", \"WATER[c]\", \"PROTON[p]\", \"Pi[c]\", \"PROTON[c]\", \"ADP[c]\", \"Pi[p]\", \"WATER[p]\"]\n",
+    "# met_not_include = []\n",
+    "do_not_include = []\n",
+    "\n",
+    "for met in met_not_include:\n",
+    "    do_not_include.append(metabolites_idx[met])\n",
+    "\n",
+    "def plot_metabolite(G, metabolite, heatmap_df, mode):\n",
+    "    index = metabolites_idx[metabolite]\n",
+    "    to_return = []\n",
+    "    for k in range(reaction_length):\n",
+    "        if stoichiometry[index, k] < 0:\n",
+    "            reaction = index_to_rxn[k]\n",
+    "            clean_reaction = clean_reaction_name(reaction)\n",
+    "\n",
+    "            row_num = heatmap_df[heatmap_df['Reactions'] == reaction].index[0]\n",
+    "            if heatmap_df[env_name].loc[row_num] == 0:\n",
+    "                continue\n",
+    "            else:\n",
+    "                flux = math.log(heatmap_df[env_name].loc[row_num], 10)\n",
+    "\n",
+    "            if flux < 0 and mode == 'green':\n",
+    "                continue\n",
+    "            elif flux > 0 and mode == 'red':\n",
+    "                continue\n",
+    "\n",
+    "            if flux > 0:\n",
+    "                color = 'green'\n",
+    "            else:\n",
+    "                color = 'red'\n",
+    "\n",
+    "            G.add_edge(metabolite, clean_reaction, color=color, title=flux)\n",
+    "            for l in range(met_length):\n",
+    "                if stoichiometry[l, k] > 0:\n",
+    "                    if l != index and l not in do_not_include:\n",
+    "                        product = index_to_met[l]\n",
+    "                        to_return.append(product)\n",
+    "                        G.add_edge(clean_reaction, product, color=color, title=flux)\n",
+    "                elif stoichiometry[l, k] < 0:\n",
+    "                    if l != index and l not in do_not_include:\n",
+    "                        reactant = index_to_met[l]\n",
+    "                        G.add_edge(reactant, clean_reaction, color=color, title=flux)\n",
+    "\n",
+    "    return to_return"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:11.528447Z",
+     "start_time": "2023-08-03T18:30:11.511840Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "outputs": [],
+   "source": [
+    "def clean_reaction_name(reaction):\n",
+    "    if '/' in reaction:\n",
+    "        index = reaction.find('RXN')\n",
+    "        if (reaction[index+3] == '-' and reaction[index+4].isdigit()) or (reaction[index+4] == '-' and reaction[index+5].isdigit()):\n",
+    "            index = index + 4\n",
+    "            if reaction[index] == '-':\n",
+    "                index += 1\n",
+    "            while reaction[index].isdigit():\n",
+    "                index += 1\n",
+    "            reaction = reaction[:index]\n",
+    "        else:\n",
+    "            reaction = reaction[:index+3]\n",
+    "    elif '__' in reaction:\n",
+    "        reaction = reaction[:reaction.find('__')]\n",
+    "    elif '(reverse)' in reaction:\n",
+    "        reaction = reaction[:reaction.find('(reverse)') - 1]\n",
+    "    elif '[' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[') - 1]\n",
+    "    elif ' ' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[')]\n",
+    "    return reaction"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:11.544908Z",
+     "start_time": "2023-08-03T18:30:11.537718Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "outputs": [],
+   "source": [
+    "# Change Parameters Here\n",
+    "k = 10\n",
+    "mode = 'green'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:11.562115Z",
+     "start_time": "2023-08-03T18:30:11.544496Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "outputs": [],
+   "source": [
+    "if mode == 'red':\n",
+    "    return_met_1 = important_remove\n",
+    "else:\n",
+    "    return_met_1 = important_add\n",
+    "heatmap_df.sort_values(by=[env_name], ascending=False, inplace=True)\n",
+    "return_met_2 = []\n",
+    "\n",
+    "for i in range(k):\n",
+    "    for met in return_met_1:\n",
+    "        if met is None:\n",
+    "            continue\n",
+    "        return_met_2 += plot_metabolite(G, met, heatmap_df, mode)\n",
+    "    return_met_2 = list(set(return_met_2))\n",
+    "    return_met_1 = return_met_2\n",
+    "    return_met_2 = []"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:33.043406Z",
+     "start_time": "2023-08-03T18:30:11.555187Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "out/new_env_analysis/met_visualization/green/Nitrate_as_reduction_target_metabolite_10green_graph.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<IPython.lib.display.IFrame at 0x168abebb0>",
+      "text/html": "\n        <iframe\n            width=\"1500px\"\n            height=\"1000px\"\n            src=\"out/new_env_analysis/met_visualization/green/Nitrate_as_reduction_target_metabolite_10green_graph.html\"\n            frameborder=\"0\"\n            allowfullscreen\n            \n        ></iframe>\n        "
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closeness = nx.closeness_centrality(G)\n",
+    "sort_closeness = sorted(closeness.items(), key=lambda x:x[1], reverse=True)\n",
+    "\n",
+    "sort_closeness = sort_closeness[:8]\n",
+    "nt = Network('1000px', '1500px', notebook=True, cdn_resources='in_line', directed=True)\n",
+    "nt.from_nx(G)\n",
+    "\n",
+    "for tup in sort_closeness:\n",
+    "    nt.get_node(tup[0])['color'] = 'blue'\n",
+    "\n",
+    "for n in nt.nodes:\n",
+    "    if n['label'] in metabolites_idx:\n",
+    "        n['shape'] = 'star'\n",
+    "    if n['label'] in important_add:\n",
+    "        n['color'] = 'orange'\n",
+    "    if n['label'] in important_remove:\n",
+    "        n['color'] = 'purple'\n",
+    "\n",
+    "\n",
+    "# nt.toggle_physics(True)\n",
+    "nt.show_buttons(filter_=['physics'])\n",
+    "nt.show(f'out/new_env_analysis/met_visualization/{mode}/{env_name}_metabolite_{k}{mode}_graph.html')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:33.241452Z",
+     "start_time": "2023-08-03T18:30:33.067417Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-03T18:30:33.241731Z",
+     "start_time": "2023-08-03T18:30:33.235885Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ecoli/experiments/new_environments_test/new_env.xlsx b/ecoli/experiments/new_environments_test/new_env.xlsx
new file mode 100644
index 000000000..0d277eb64
Binary files /dev/null and b/ecoli/experiments/new_environments_test/new_env.xlsx differ
diff --git a/ecoli/experiments/new_environments_test/new_environments_sim.py b/ecoli/experiments/new_environments_test/new_environments_sim.py
new file mode 100644
index 000000000..3e4fa3d28
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/new_environments_sim.py
@@ -0,0 +1,214 @@
+"""
+==================================
+Metabolism using user-friendly FBA
+==================================
+"""
+import argparse
+
+# vivarium-core imports
+import pytest
+from vivarium.core.engine import Engine
+from vivarium.core.composer import Composer
+from vivarium.library.dict_utils import deep_merge
+
+# vivarium-ecoli imports
+from ecoli.experiments.ecoli_master_sim import EcoliSim, CONFIG_DIR_PATH
+from ecoli.library.sim_data import LoadSimData
+from ecoli.states.wcecoli_state import get_state_from_file
+from ecoli.composites.ecoli_master import SIM_DATA_PATH
+from ecoli.processes.metabolism_redux import MetabolismRedux
+from ecoli.processes.stubs.exchange_stub import Exchange
+from ecoli.processes.registries import topology_registry
+
+import numpy as np
+import pathlib, datetime
+import dill
+import pandas as pd
+
+# get topology from ecoli_master
+metabolism_topology = topology_registry.access('ecoli-metabolism')
+
+
+# make a composite with Exchange
+class MetabolismExchange(Composer):
+    defaults = {
+        'metabolism': {
+            'kinetic_rates': [],
+        },
+        'exchanger': {},
+        'sim_data_path': SIM_DATA_PATH,
+        'seed': 0,
+    }
+
+    def __init__(self, config=None):
+        super().__init__(config)
+        self.load_sim_data = LoadSimData(
+            sim_data_path=self.config['sim_data_path'],
+            seed=self.config['seed'])
+
+    def generate_processes(self, config):
+        # configure metabolism
+        metabolism_config = self.load_sim_data.get_metabolism_redux_config()
+        metabolism_config = deep_merge(metabolism_config, config['metabolism'])
+        metabolism_process = MetabolismRedux(metabolism_config)
+
+        example_update = {'ATP[c]': 9064, 'DATP[c]': 2222, 'DCTP[c]': 1649, 'DGTP[c]': 1647, 'FAD[c]': 171,
+                          'GTP[c]': 20122, 'LEU[c]': 325, 'METHYLENE-THF[c]': 223, 'NAD[c]': 769,
+                          'PHENYL-PYRUVATE[c]': 996, 'REDUCED-MENAQUINONE[c]': 240, 'UTP[c]': 14648}
+
+        # configure exchanger stub process
+        # TODO -- this needs a dictionary with {mol_id: exchanged counts/sec}
+        exchanger_config = {'exchanges': example_update, 'time_step': metabolism_config['time_step']}
+        exchanger_process = Exchange(exchanger_config)
+
+        return {
+            'metabolism': metabolism_process,
+            'exchanger': exchanger_process,
+        }
+
+    def generate_topology(self, config):
+        return {
+            'metabolism': metabolism_topology,
+            'exchanger': {
+                'bulk': ('bulk',),
+            }
+        }
+
+def run_ecoli_with_metabolism_redux(
+        filename='fba_new_environments',
+        total_time=50,
+        divide=True,
+        initial_state_file='wcecoli_t0',
+        progress_bar=True,
+        log_updates=False,
+        emitter='timeseries',
+        name='fba_new_environments',
+        raw_output=False,
+        save=False,        # save_times=4,
+):
+    condition_df = pd.read_excel("ecoli/experiments/new_environments_test/new_env.xlsx")
+    condition_df["Add"] = condition_df["Add"].apply(lambda x: set(str(x).split(", ") if x == x else set()))
+    condition_df["Remove"] = condition_df["Remove"].apply(lambda x: set(str(x).split(", ") if x == x else set()))
+    condition_df["Demand"] = condition_df["Demand"].apply(lambda x: set(str(x).split(", ") if x == x else set()))
+
+    for i in range(len(condition_df)):
+        env_name = f'{condition_df.loc[i, "Environment"]}'
+        if env_name[:4] == 'SKIP':
+            continue
+        # filename = 'default'
+        sim = EcoliSim.from_file(CONFIG_DIR_PATH + filename + '.json')
+        sim.total_time = total_time
+        sim.divide = divide
+        sim.progress_bar = progress_bar
+        sim.log_updates = log_updates
+        sim.emitter = emitter
+        sim.initial_state = get_state_from_file(path=f'data/{initial_state_file}.json')
+        sim.raw_output = raw_output
+        sim.save = save
+        # sim.save_times = [4]
+        to_add = condition_df.loc[i, "Add"]
+        to_remove = condition_df.loc[i, "Remove"]
+
+        sim.initial_state['environment']['exchange_data']['constrained'] = {}
+        sim.initial_state['environment']['exchange_data']['unconstrained'].add('GLC[p]')
+        sim.initial_state['environment']['exchange_data']['unconstrained'].add('AMMONIUM[p]')
+        sim.initial_state['environment']['exchange_data']['unconstrained'].remove('AMMONIUM[c]')
+
+        for mol in to_add:
+            sim.initial_state['environment']['exchange_data']['unconstrained'].add(mol)
+
+        for mol in to_remove:
+            sim.initial_state['environment']['exchange_data']['unconstrained'].remove(mol)
+
+        try:
+            sim.build_ecoli()
+            sim.run()
+
+            query = []
+            folder = f'out/fba_new_env/{name}_{env_name}_{total_time}_{datetime.date.today()}/'
+            save_sim_output(folder, query, sim, save_model=True)
+        except ValueError:
+            pass
+@pytest.mark.slow
+def test_new_environments(
+        filename='fba_new_environments',
+        total_time=4,
+        divide=True,
+        initial_state_file='wcecoli_t0',
+        progress_bar=True,
+        log_updates=False,
+        emitter='timeseries',
+        name='fba_new_environments',
+        raw_output=False,
+        save=False,
+):
+    sim = EcoliSim.from_file(CONFIG_DIR_PATH + filename + '.json')
+    sim.total_time = total_time
+    sim.divide = divide
+    sim.progress_bar = progress_bar
+    sim.log_updates = log_updates
+    sim.emitter = emitter
+    sim.initial_state = get_state_from_file(path=f'data/{initial_state_file}.json')
+    sim.raw_output = raw_output
+    sim.save = save
+
+    sim.initial_state['environment']['exchange_data']['constrained'] = {}
+    sim.initial_state['environment']['exchange_data']['unconstrained'].add('AMMONIUM[p]')
+    sim.initial_state['environment']['exchange_data']['unconstrained'].add('ACET[p]')
+    sim.initial_state['environment']['exchange_data']['unconstrained'].remove('AMMONIUM[c]')
+
+    sim.build_ecoli()
+    sim.run()
+
+    query = []
+    agents = sim.query()['agents'].keys()
+    for agent in agents:
+        query.extend([('agents', agent, 'listeners', 'fba_results'),
+                      ('agents', agent, 'listeners', 'mass'),
+                      ('agents', agent, 'bulk')])
+    output = sim.query(query)
+    metabolism = sim.ecoli['steps']['agents'][agent]['ecoli-metabolism-redux']
+
+    sim_fluxes = pd.DataFrame(output['agents']['0']['listeners']['fba_results']["estimated_fluxes"], columns=metabolism.reaction_names)
+    sim_fluxes = pd.DataFrame(sim_fluxes.loc[2, :].abs().sort_values(ascending=False))
+    print(sim_fluxes)
+    # test that acetate transport is occurring
+    assert sim_fluxes.loc['TRANS-RXN0-571'][2] > 0 or sim_fluxes.loc['RXN0-1981'][2] > 0, "New Environment - Acetate Not Transported"
+
+experiment_library = {
+    '0': run_ecoli_with_metabolism_redux,
+    '1': test_new_environments,
+}
+
+
+def save_sim_output(folder, query, sim, save_model=False):
+    agents = sim.query()['agents'].keys()
+    for agent in agents:
+        query.extend([('agents', agent, 'listeners', 'fba_results'),
+                      ('agents', agent, 'listeners', 'mass'),
+                      ('agents', agent, 'bulk')])
+    output = sim.query(query)
+    pathlib.Path(folder).mkdir(parents=True, exist_ok=True)
+    np.save(folder + 'output.npy', output)
+
+    if save_model:
+        f = open(folder + 'agent_processes.pkl', 'wb')
+        dill.dump(sim.ecoli['processes']['agents'][agent], f)
+        f.close()
+
+        f = open(folder + 'agent_steps.pkl', 'wb')
+        dill.dump(sim.ecoli['steps']['agents'][agent], f)
+        f.close()
+
+# run experiments with command line arguments: python ecoli/experiments/metabolism_redux_sim.py -n exp_id
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='user-friendly metabolism')
+    parser.add_argument('--name', '-n', default=[], nargs='+', help='test ids to run')
+    args = parser.parse_args()
+    run_all = not args.name
+
+    for name in args.name:
+        experiment_library[name]()
+    if run_all:
+        for name, test in experiment_library.items():
+            test()
diff --git a/ecoli/experiments/new_environments_test/pathway_heatmap_creation.ipynb b/ecoli/experiments/new_environments_test/pathway_heatmap_creation.ipynb
new file mode 100644
index 000000000..bd68b3aa7
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/pathway_heatmap_creation.ipynb
@@ -0,0 +1,449 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-08-11T18:08:17.222790Z",
+     "start_time": "2023-08-11T18:08:17.215825Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from pyvis.network import Network\n",
+    "import math\n",
+    "import community as community_louvain\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import xmltodict\n",
+    "import os\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-11T18:08:17.531352Z",
+     "start_time": "2023-08-11T18:08:17.226898Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [],
+   "source": [
+    "def clean_name(reaction):\n",
+    "    if '/' in reaction:\n",
+    "        index = reaction.find('RXN')\n",
+    "        if (reaction[index+3] == '-' and reaction[index+4].isdigit()) or (reaction[index+4] == '-' and reaction[index+5].isdigit()):\n",
+    "            index = index + 4\n",
+    "            if reaction[index] == '-':\n",
+    "                index += 1\n",
+    "            while reaction[index].isdigit():\n",
+    "                index += 1\n",
+    "            reaction = reaction[:index]\n",
+    "        else:\n",
+    "            reaction = reaction[:index+3]\n",
+    "    elif '__' in reaction:\n",
+    "        reaction = reaction[:reaction.find('__')]\n",
+    "    elif '(reverse)' in reaction:\n",
+    "        reaction = reaction[:reaction.find('(reverse)') - 1]\n",
+    "    elif '[' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[') - 1]\n",
+    "    elif ' ' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[')]\n",
+    "    return reaction\n",
+    "\n",
+    "def get_gene_from_reaction(reaction):\n",
+    "    function = \"genes-of-reaction\"\n",
+    "    req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{reaction}&detail=none&fmt=json\"\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    try:\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Gene']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for gene in o:\n",
+    "            if type(gene) is not str: # no rxns\n",
+    "                return gene['@frameid']\n",
+    "    except KeyError:\n",
+    "        pass\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "\n",
+    "    return None\n",
+    "\n",
+    "def get_pathway_from_gene(gene):\n",
+    "    function = \"pathways-of-gene\"\n",
+    "    req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{gene}&detail=none&fmt=json\"\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    try:\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Pathway']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for pathway in o:\n",
+    "            if type(pathway) is not str: # no rxns\n",
+    "                return pathway['@frameid']\n",
+    "    except KeyError:\n",
+    "        pass\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "\n",
+    "    return None\n",
+    "\n",
+    "def get_reactions_from_pathway(pathway):\n",
+    "    to_return = []\n",
+    "    try:\n",
+    "        req_str = f\"https://websvc.biocyc.org/getxml?id=ECOLI:{pathway}&detail=low\"\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Pathway']['reaction-list']['Reaction']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for rxn in o:\n",
+    "            if type(rxn) is not str:\n",
+    "                to_return.append(rxn['@frameid'])\n",
+    "    except KeyError:\n",
+    "        pass\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "\n",
+    "    return to_return"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-11T18:08:17.564016Z",
+     "start_time": "2023-08-11T18:08:17.531820Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "heatmap_df = pd.read_excel(\"out/new_env_analysis/heatmap.xlsx\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Pathways  Reactions  Hydrogen_fumarate_-O2_Glc  Ribose_C-source  \\\n",
+      "0       PWY0-1336        0.0                   2.651957         0.000000   \n",
+      "1        PWY-8291        0.0                  -0.004411        -0.000151   \n",
+      "2        PWY-5437        0.0                   0.000000         0.000000   \n",
+      "3    GLUCONEO-PWY        0.0                   1.734921        -0.008320   \n",
+      "4             TCA        0.0                  -0.051712        -0.096573   \n",
+      "..            ...        ...                        ...              ...   \n",
+      "294     PWY0-1602        0.0                   0.000000         0.000000   \n",
+      "295     PWY0-1600        0.0                   0.000000         0.000000   \n",
+      "296     PWY0-1599        0.0                   0.000000         0.000000   \n",
+      "297     PWY0-1353        0.0                   0.000000         0.032344   \n",
+      "298      PWY-8164        0.0                   0.000000         0.000000   \n",
+      "\n",
+      "     Thiamine_salvaging  Nitrate_as_reduction_target  \\\n",
+      "0              0.000000                     2.375909   \n",
+      "1             -0.000237                     0.000244   \n",
+      "2              0.000000                     0.000000   \n",
+      "3             -0.000013                     0.047075   \n",
+      "4             -0.141557                    -0.342820   \n",
+      "..                  ...                          ...   \n",
+      "294            0.000000                     0.000000   \n",
+      "295            0.000000                     0.000000   \n",
+      "296            0.000000                     0.000000   \n",
+      "297           -0.000063                    -0.332963   \n",
+      "298            0.000000                     0.000000   \n",
+      "\n",
+      "     3-Phenylpropionate_as_carbon_and_energy_source  \\\n",
+      "0                                          0.000000   \n",
+      "1                                          0.001824   \n",
+      "2                                          0.000000   \n",
+      "3                                          1.736985   \n",
+      "4                                          0.075986   \n",
+      "..                                              ...   \n",
+      "294                                        0.000000   \n",
+      "295                                        0.000000   \n",
+      "296                                        0.000000   \n",
+      "297                                        0.211619   \n",
+      "298                                        0.000000   \n",
+      "\n",
+      "     Molybdenum_cofactor_synthesis  Pre-Quenosine_synthesis  \\\n",
+      "0                         0.000000                 0.000000   \n",
+      "1                        -0.000151                -0.000151   \n",
+      "2                         0.000000                 0.000000   \n",
+      "3                        -0.000027                -0.000027   \n",
+      "4                        -0.141560                -0.141560   \n",
+      "..                             ...                      ...   \n",
+      "294                       0.000000                 0.000000   \n",
+      "295                       0.000000                 0.000000   \n",
+      "296                       0.000000                 0.000000   \n",
+      "297                      -0.000034                -0.000034   \n",
+      "298                       0.000000                 0.000000   \n",
+      "\n",
+      "     Acetate_Transport_+_Metabolism  ...  \\\n",
+      "0                          0.000000  ...   \n",
+      "1                         -0.001438  ...   \n",
+      "2                         -0.283162  ...   \n",
+      "3                          1.508775  ...   \n",
+      "4                          0.284005  ...   \n",
+      "..                              ...  ...   \n",
+      "294                        0.000000  ...   \n",
+      "295                        0.000000  ...   \n",
+      "296                        0.000000  ...   \n",
+      "297                        0.388479  ...   \n",
+      "298                        0.000000  ...   \n",
+      "\n",
+      "     Plant_Cell_Wall_Component_Catabolism  \\\n",
+      "0                                0.000000   \n",
+      "1                               -0.000151   \n",
+      "2                                0.000000   \n",
+      "3                               -0.000004   \n",
+      "4                               -0.141530   \n",
+      "..                                    ...   \n",
+      "294                              0.000000   \n",
+      "295                              0.000000   \n",
+      "296                              0.000000   \n",
+      "297                             -0.000034   \n",
+      "298                              0.000000   \n",
+      "\n",
+      "     Periplasmic_Phosphatase_to_Import_G6P  Cell_Wall_Component_Biosynthesis  \\\n",
+      "0                                 0.000000                          0.000000   \n",
+      "1                                -0.000151                         -0.000151   \n",
+      "2                                 0.000000                          0.000000   \n",
+      "3                                -0.000027                         -0.000027   \n",
+      "4                                -0.141560                         -0.141560   \n",
+      "..                                     ...                               ...   \n",
+      "294                               0.000000                          0.000000   \n",
+      "295                               0.000000                          0.000000   \n",
+      "296                               0.000000                          0.000000   \n",
+      "297                              -0.000034                         -0.000034   \n",
+      "298                               0.000000                          0.000000   \n",
+      "\n",
+      "     Galactose_Deg_Cell_Envelope_Biosynth  \\\n",
+      "0                                0.000000   \n",
+      "1                               -0.000151   \n",
+      "2                                0.000000   \n",
+      "3                               -0.067683   \n",
+      "4                               -0.189158   \n",
+      "..                                    ...   \n",
+      "294                              0.000000   \n",
+      "295                              0.000000   \n",
+      "296                              0.000000   \n",
+      "297                              0.006861   \n",
+      "298                              0.000000   \n",
+      "\n",
+      "     Outer_Membrane_Generic_Ion+Sugar_Transport_Lambda_Receptor  \\\n",
+      "0                                             0.000000            \n",
+      "1                                            -0.000151            \n",
+      "2                                             0.000000            \n",
+      "3                                            -0.001548            \n",
+      "4                                            -0.116069            \n",
+      "..                                                 ...            \n",
+      "294                                           0.000000            \n",
+      "295                                           0.000000            \n",
+      "296                                           0.000000            \n",
+      "297                                          -0.014094            \n",
+      "298                                           0.000000            \n",
+      "\n",
+      "     N-acetylglucosamine_degradation_I  \\\n",
+      "0                             0.000000   \n",
+      "1                            -0.000151   \n",
+      "2                             0.000000   \n",
+      "3                             0.014393   \n",
+      "4                            -0.122813   \n",
+      "..                                 ...   \n",
+      "294                           0.000000   \n",
+      "295                           0.000000   \n",
+      "296                           0.000000   \n",
+      "297                          -0.000034   \n",
+      "298                           0.000000   \n",
+      "\n",
+      "     putrescine_(ubiquitious_regulator)_biosynthesis  \\\n",
+      "0                                           0.000000   \n",
+      "1                                          -0.000151   \n",
+      "2                                           0.000000   \n",
+      "3                                          -0.028295   \n",
+      "4                                          -0.176768   \n",
+      "..                                               ...   \n",
+      "294                                         0.000000   \n",
+      "295                                         0.000000   \n",
+      "296                                         0.000000   \n",
+      "297                                        -0.002521   \n",
+      "298                                         0.000000   \n",
+      "\n",
+      "     anaerobic_growth_using_TMAO_as_acceptor  \\\n",
+      "0                                   2.393130   \n",
+      "1                                  -0.001338   \n",
+      "2                                   0.000000   \n",
+      "3                                   0.052692   \n",
+      "4                                  -0.025370   \n",
+      "..                                       ...   \n",
+      "294                                 0.000000   \n",
+      "295                                 0.000000   \n",
+      "296                                 0.000000   \n",
+      "297                                 0.000000   \n",
+      "298                                 0.000000   \n",
+      "\n",
+      "     gluconoride_transport_+_gluconoride_and_gluconorate_catabolism   CONTROL  \n",
+      "0                                             0.000000               0.000000  \n",
+      "1                                            -0.000151               1.660591  \n",
+      "2                                             0.000000               0.000000  \n",
+      "3                                            -0.000027               1.357405  \n",
+      "4                                            -0.141560               2.872178  \n",
+      "..                                                 ...                    ...  \n",
+      "294                                           0.000000               0.000000  \n",
+      "295                                           0.000000               0.000000  \n",
+      "296                                           0.000000               0.000000  \n",
+      "297                                          -0.000034               2.902197  \n",
+      "298                                           0.000000               0.000000  \n",
+      "\n",
+      "[299 rows x 73 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pathway_df = pd.DataFrame()\n",
+    "firstDone = False\n",
+    "\n",
+    "for index, row in heatmap_df.iterrows():\n",
+    "    rxn = clean_name(row['Reactions'])\n",
+    "    gne = get_gene_from_reaction(rxn)\n",
+    "    if gne is None:\n",
+    "        continue\n",
+    "\n",
+    "    pathway = get_pathway_from_gene(gne)\n",
+    "    if pathway is None:\n",
+    "        continue\n",
+    "\n",
+    "    if firstDone and pathway in pathway_df['Pathways'].values:\n",
+    "        continue\n",
+    "\n",
+    "    reactions_list = get_reactions_from_pathway(pathway)\n",
+    "    append_dict = {'Pathways': pathway}\n",
+    "    for col in heatmap_df.columns:\n",
+    "        sum = 0\n",
+    "        count = 0\n",
+    "        if col != 'Reactions':\n",
+    "            for reac in reactions_list:\n",
+    "                try:\n",
+    "                    row_number = heatmap_df[heatmap_df['Reactions'] == reac].index[0]\n",
+    "                except IndexError:\n",
+    "                    try:\n",
+    "                        row_number = heatmap_df[heatmap_df['Reactions'].str.contains(reac)].index[0]\n",
+    "                    except IndexError:\n",
+    "                        continue\n",
+    "\n",
+    "                count += 1\n",
+    "                if heatmap_df[col].loc[row_number] != 0:\n",
+    "                    sum += math.log(heatmap_df[col].loc[row_number], 10)\n",
+    "                else:\n",
+    "                    sum += 0\n",
+    "\n",
+    "        append_dict[col] = sum / count if count != 0 else 0\n",
+    "    pathway_df = pathway_df.append(append_dict, ignore_index=True)\n",
+    "    firstDone = True\n",
+    "\n",
+    "print(pathway_df)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-11T19:20:56.447587Z",
+     "start_time": "2023-08-11T18:47:59.485610Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [],
+   "source": [
+    "pathway_df.to_csv(rf'out/new_env_analysis/pathway_heatmap.csv')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:02:25.526579Z",
+     "start_time": "2023-08-11T20:02:25.268604Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ecoli/experiments/new_environments_test/pathway_visualization.ipynb b/ecoli/experiments/new_environments_test/pathway_visualization.ipynb
new file mode 100644
index 000000000..875ad56a9
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/pathway_visualization.ipynb
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:00.189747Z",
+     "start_time": "2023-08-14T23:14:59.999452Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from pyvis.network import Network\n",
+    "import math\n",
+    "import community as community_louvain\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import xmltodict\n",
+    "import os\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:00.642405Z",
+     "start_time": "2023-08-14T23:15:00.034913Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [],
+   "source": [
+    "env_name = 'Nitrate_as_reduction_target'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:00.643968Z",
+     "start_time": "2023-08-14T23:15:00.641548Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [],
+   "source": [
+    "condition_df = pd.read_excel(\"ecoli/experiments/new_environments_test/done.xlsx\")\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "\n",
+    "row_num = condition_df[condition_df['Environment'] == env_name].index[0]\n",
+    "important_add = condition_df[\"Add\"].loc[row_num]\n",
+    "important_remove = condition_df[\"Remove\"].loc[row_num]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:00.722598Z",
+     "start_time": "2023-08-14T23:15:00.649390Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-07-21'\n",
+    "experiment = 'fba_new_environments'\n",
+    "entry = f'{experiment}_{env_name}_{time}_{date}'\n",
+    "folder = f'out/fba_new_env/{entry}/'\n",
+    "\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry\n",
+    "\n",
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])\n",
+    "\n",
+    "sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "\n",
+    "metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "index_to_met = {v: k for k, v in metabolites_idx.items()}\n",
+    "\n",
+    "reaction_idx = {reaction: i for i, reaction in enumerate(metabolism.reaction_names)}\n",
+    "index_to_rxn = {v: k for k, v in reaction_idx.items()}\n",
+    "\n",
+    "metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:08.276493Z",
+     "start_time": "2023-08-14T23:15:00.737606Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [],
+   "source": [
+    "heatmap_df = pd.read_excel(\"out/new_env_analysis/heatmap.xlsx\")\n",
+    "heatmap_df = heatmap_df[heatmap_df[env_name] != 0]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:20.684632Z",
+     "start_time": "2023-08-14T23:15:08.284651Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [],
+   "source": [
+    "G = nx.DiGraph()\n",
+    "\n",
+    "met_length = len(metabolism.metabolite_names)\n",
+    "reaction_length = np.shape(stoichiometry)[1]\n",
+    "met_not_include = [\"ATP[c]\", \"WATER[c]\", \"PROTON[p]\", \"Pi[c]\", \"PROTON[c]\", \"ADP[c]\", \"Pi[p]\", \"WATER[p]\", \"NAD[c]\", \"NADH[c]\", \"NADP[c]\", \"NADPH[c]\", \"PPI[c]\", \"CARBON-DIOXIDE[c]\", \"CO-A[c]\", \"ACETYL-COA[c]\"\n",
+    "  ]\n",
+    "# met_not_include = []\n",
+    "do_not_include = []\n",
+    "\n",
+    "for met in met_not_include:\n",
+    "    do_not_include.append(metabolites_idx[met])\n",
+    "\n",
+    "def plot_pathway_from_reaction(G, reaction, heatmap_df, pathway_to_colors, completed_pathways):\n",
+    "    index = reaction_idx[reaction]\n",
+    "    reactants = []\n",
+    "    products = []\n",
+    "    pathways = []\n",
+    "    colors = ['purple', 'orange', 'green', 'lightblue', 'blue', 'pink', 'brown', 'red']\n",
+    "    color_index = 0\n",
+    "\n",
+    "    for k in range(met_length):\n",
+    "        if stoichiometry[k, index] > 0 and index_to_met[k] not in met_not_include:\n",
+    "            products.append(index_to_met[k])\n",
+    "        elif stoichiometry[k, index] < 0 and index_to_met[k] not in met_not_include:\n",
+    "            reactants.append(index_to_met[k])\n",
+    "\n",
+    "    for reactant in reactants:\n",
+    "        pathways += get_pathway_from_metabolite(reactant)\n",
+    "\n",
+    "    for product in products:\n",
+    "        pathways += get_pathway_from_metabolite(product)\n",
+    "\n",
+    "    weird_name = []\n",
+    "    pathways = list(set(pathways) - set(completed_pathways))\n",
+    "    for pathway in pathways:\n",
+    "        plot_reactions = []\n",
+    "        try:\n",
+    "            req_str = f\"https://websvc.biocyc.org/getxml?id=ECOLI:{pathway}&detail=low\"\n",
+    "            r = s.get(req_str)\n",
+    "            o = xmltodict.parse(r.content)['ptools-xml']['Pathway']['reaction-list']['Reaction']\n",
+    "            if type(o) is dict:\n",
+    "                o = [o]\n",
+    "\n",
+    "            for rxn in o:\n",
+    "                if type(rxn) is not str:\n",
+    "                    plot_reactions.append(rxn['@frameid'])\n",
+    "        except KeyError:\n",
+    "            pass\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "        has_node = False\n",
+    "        for reaction in plot_reactions:\n",
+    "            try:\n",
+    "                row_number = heatmap_df[heatmap_df['Reactions'] == reaction].index[0]\n",
+    "            except IndexError:\n",
+    "                try:\n",
+    "                    row_number = heatmap_df[heatmap_df['Reactions'].str.contains(reaction)].index[0]\n",
+    "                    weird_name.append(reaction)\n",
+    "                    reaction = heatmap_df['Reactions'].loc[row_number]\n",
+    "                except IndexError:\n",
+    "                    continue\n",
+    "\n",
+    "            if heatmap_df[env_name].loc[row_number] == 0:\n",
+    "                continue\n",
+    "            else:\n",
+    "                flux = math.log(heatmap_df[env_name].loc[row_number], 10)\n",
+    "\n",
+    "            if abs(flux) < 0.2:\n",
+    "                continue\n",
+    "\n",
+    "            has_node = True\n",
+    "            plot_reaction(G, reaction, flux, colors[color_index])\n",
+    "\n",
+    "        if pathway not in pathway_to_colors and has_node:\n",
+    "            pathway_to_colors[pathway] = colors[color_index]\n",
+    "            color_index = (color_index + 1) % len(colors)\n",
+    "\n",
+    "    completed_pathways += pathways\n",
+    "    return weird_name\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:20.713812Z",
+     "start_time": "2023-08-14T23:15:20.685178Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [],
+   "source": [
+    "def plot_reaction(G, reaction, flux, color):\n",
+    "    index = reaction_idx[reaction]\n",
+    "    reactants = []\n",
+    "    products = []\n",
+    "    clean_name = clean_reaction_name(reaction)\n",
+    "    for k in range(met_length):\n",
+    "        if stoichiometry[k, index] > 0 and index_to_met[k] not in met_not_include:\n",
+    "            products.append(index_to_met[k])\n",
+    "        elif stoichiometry[k, index] < 0 and index_to_met[k] not in met_not_include:\n",
+    "            reactants.append(index_to_met[k])\n",
+    "\n",
+    "    for reactant in reactants:\n",
+    "        G.add_edge(reactant, clean_name, color=color, title=flux)\n",
+    "\n",
+    "    for product in products:\n",
+    "        G.add_edge(clean_name, product, color=color, title=flux)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:20.739598Z",
+     "start_time": "2023-08-14T23:15:20.718809Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [],
+   "source": [
+    "def get_pathway_from_metabolite(met):\n",
+    "    to_return = []\n",
+    "    function = \"pathways-of-compound\"\n",
+    "    req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{met[:-3]}&detail=none&fmt=json\"\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    try:\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Pathway']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for pathway in o:\n",
+    "            if type(pathway) is not str: # no rxns\n",
+    "                to_return.append(pathway['@frameid'])\n",
+    "    except KeyError:\n",
+    "        pass\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "\n",
+    "    return to_return"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:20.775007Z",
+     "start_time": "2023-08-14T23:15:20.733394Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [],
+   "source": [
+    "def clean_reaction_name(reaction):\n",
+    "    if '/' in reaction:\n",
+    "        index = reaction.find('RXN')\n",
+    "        if (reaction[index+3] == '-' and reaction[index+4].isdigit()) or (reaction[index+4] == '-' and reaction[index+5].isdigit()):\n",
+    "            index = index + 4\n",
+    "            if reaction[index] == '-':\n",
+    "                index += 1\n",
+    "            while reaction[index].isdigit():\n",
+    "                index += 1\n",
+    "            reaction = reaction[:index]\n",
+    "        else:\n",
+    "            reaction = reaction[:index+3]\n",
+    "    elif '__' in reaction:\n",
+    "        reaction = reaction[:reaction.find('__')]\n",
+    "    elif '(reverse)' in reaction:\n",
+    "        reaction = reaction[:reaction.find('(reverse)') - 1]\n",
+    "    elif '[' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[') - 1]\n",
+    "    elif ' ' in reaction:\n",
+    "        reaction = reaction[:reaction.find('[')]\n",
+    "    return reaction"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:20.775775Z",
+     "start_time": "2023-08-14T23:15:20.750017Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [
+    "def plot_metabolite(G, metabolite, heatmap_df):\n",
+    "    index = metabolites_idx[metabolite]\n",
+    "    to_return = []\n",
+    "    for k in range(reaction_length):\n",
+    "        if stoichiometry[index, k] > 0:\n",
+    "            reaction = index_to_rxn[k]\n",
+    "            clean_reaction = clean_reaction_name(reaction)\n",
+    "\n",
+    "            try:\n",
+    "                row = heatmap_df[heatmap_df['Reactions'] == reaction].index[0]\n",
+    "                flux_num = math.log(heatmap_df[env_name].loc[row], 10)\n",
+    "            except IndexError:\n",
+    "                flux_num = 0\n",
+    "\n",
+    "            if abs(flux_num) < 0.2:\n",
+    "                continue\n",
+    "\n",
+    "            color = 'black'\n",
+    "\n",
+    "            G.add_edge(clean_reaction, metabolite, color=color, title=flux_num)\n",
+    "            for l in range(met_length):\n",
+    "                if stoichiometry[l, k] < 0:\n",
+    "                    if l != index and l not in do_not_include:\n",
+    "                        reactant = index_to_met[l]\n",
+    "                        to_return.append(reactant)\n",
+    "                        G.add_edge(reactant, clean_reaction, color=color, title=flux_num)\n",
+    "                elif stoichiometry[l, k] > 0:\n",
+    "                    if l != index and l not in do_not_include:\n",
+    "                        product = index_to_met[l]\n",
+    "                        G.add_edge(clean_reaction, product, color=color, title=flux_num)\n",
+    "\n",
+    "    return to_return"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:20.776121Z",
+     "start_time": "2023-08-14T23:15:20.765890Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [
+    "# Change K here to select the top k reactions/pathways to plot\n",
+    "\n",
+    "k = 6\n",
+    "green_offset = 0\n",
+    "heatmap_df.sort_values(by=[env_name], ascending=False, inplace=True)\n",
+    "top_increase = [heatmap_df.iloc[i, 0] for i in range(k + green_offset)]\n",
+    "bottom_increase = [heatmap_df.iloc[i, 0] for i in range(-1, -1*k - 1, -1)]\n",
+    "pathway_to_colors = {}\n",
+    "\n",
+    "failed_name = []\n",
+    "completed_pathways = []\n",
+    "\n",
+    "if \"Non-zero Values for each environment\" in top_increase:\n",
+    "    top_increase.remove(\"Non-zero Values for each environment\")\n",
+    "elif \"Non-zero Values for each environment\" in bottom_increase:\n",
+    "    bottom_increase.remove(\"Non-zero Values for each environment\")\n",
+    "\n",
+    "for reaction in top_increase:\n",
+    "    failed_name += plot_pathway_from_reaction(G, reaction, heatmap_df, pathway_to_colors, completed_pathways)\n",
+    "\n",
+    "for reaction in bottom_increase:\n",
+    "    failed_name += plot_pathway_from_reaction(G, reaction, heatmap_df, pathway_to_colors, completed_pathways)\n",
+    "\n",
+    "failed_name = list(set(failed_name))\n",
+    "\n",
+    "with open(f'out/new_env_analysis/pathway_visualization/{env_name}_{k}_legend.txt', 'w') as f:\n",
+    "    for reaction in (top_increase + bottom_increase):\n",
+    "        row_number = heatmap_df[heatmap_df['Reactions'] == reaction].index[0]\n",
+    "        flux = heatmap_df[env_name].loc[row_number]\n",
+    "        f.write('%s: %s\\n' % (reaction, flux))\n",
+    "\n",
+    "    for key, value in pathway_to_colors.items():\n",
+    "        f.write('%s: %s\\n' % (key, value))\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:34.557185Z",
+     "start_time": "2023-08-14T23:15:20.838942Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [],
+   "source": [
+    "# Add interesting extra metabolites to examine here\n",
+    "met_level = 1\n",
+    "extra_metabolites = [\"AMMONIUM[c]\"]\n",
+    "temp = []\n",
+    "\n",
+    "for i in range(met_level):\n",
+    "    for met in extra_metabolites:\n",
+    "        if met is None:\n",
+    "            continue\n",
+    "        temp += plot_metabolite(G, met, heatmap_df)\n",
+    "    temp = list(set(temp))\n",
+    "    extra_metabolites = temp\n",
+    "    temp = []"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:34.840125Z",
+     "start_time": "2023-08-14T23:15:34.657579Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "out/new_env_analysis/pathway_visualization/Nitrate_as_reduction_target_pathway_6_graph.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<IPython.lib.display.IFrame at 0x156695340>",
+      "text/html": "\n        <iframe\n            width=\"1500px\"\n            height=\"1000px\"\n            src=\"out/new_env_analysis/pathway_visualization/Nitrate_as_reduction_target_pathway_6_graph.html\"\n            frameborder=\"0\"\n            allowfullscreen\n            \n        ></iframe>\n        "
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closeness = nx.closeness_centrality(G)\n",
+    "sort_closeness = sorted(closeness.items(), key=lambda x:x[1], reverse=True)\n",
+    "\n",
+    "sort_closeness = sort_closeness[:8]\n",
+    "nt = Network('1000px', '1500px', notebook=True, cdn_resources='in_line', directed=True)\n",
+    "nt.from_nx(G)\n",
+    "\n",
+    "for tup in sort_closeness:\n",
+    "    nt.get_node(tup[0])['color'] = 'blue'\n",
+    "\n",
+    "for n in nt.nodes:\n",
+    "    if n['label'] in metabolites_idx:\n",
+    "        n['shape'] = 'star'\n",
+    "    if n['label'] in important_add:\n",
+    "        n['color'] = 'orange'\n",
+    "    if n['label'] in important_remove:\n",
+    "        n['color'] = 'purple'\n",
+    "\n",
+    "\n",
+    "nt.show_buttons(filter_=['physics'])\n",
+    "nt.show(f'out/new_env_analysis/pathway_visualization/{env_name}_pathway_{k}_graph.html')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:35.067489Z",
+     "start_time": "2023-08-14T23:15:34.852074Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-14T23:15:35.117234Z",
+     "start_time": "2023-08-14T23:15:35.069332Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ecoli/experiments/new_environments_test/plotting_pathways_no_reaction.ipynb b/ecoli/experiments/new_environments_test/plotting_pathways_no_reaction.ipynb
new file mode 100644
index 000000000..baa1744cc
--- /dev/null
+++ b/ecoli/experiments/new_environments_test/plotting_pathways_no_reaction.ipynb
@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:42.836924Z",
+     "start_time": "2023-08-16T22:14:42.809427Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from pyvis.network import Network\n",
+    "import math\n",
+    "import community as community_louvain\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import xmltodict\n",
+    "import os\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:43.123930Z",
+     "start_time": "2023-08-16T22:14:42.825869Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "outputs": [],
+   "source": [
+    "env_name = 'Nitrate_as_reduction_target'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:43.133255Z",
+     "start_time": "2023-08-16T22:14:43.127851Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "outputs": [],
+   "source": [
+    "condition_df = pd.read_excel(\"ecoli/experiments/new_environments_test/done.xlsx\")\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "\n",
+    "row_num = condition_df[condition_df['Environment'] == env_name].index[0]\n",
+    "important_add = condition_df[\"Add\"].loc[row_num]\n",
+    "important_remove = condition_df[\"Remove\"].loc[row_num]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:43.220612Z",
+     "start_time": "2023-08-16T22:14:43.137905Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-07-21'\n",
+    "experiment = 'fba_new_environments'\n",
+    "entry = f'{experiment}_{env_name}_{time}_{date}'\n",
+    "folder = f'out/fba_new_env/{entry}/'\n",
+    "\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry\n",
+    "\n",
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])\n",
+    "\n",
+    "sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "\n",
+    "metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "index_to_met = {v: k for k, v in metabolites_idx.items()}\n",
+    "\n",
+    "reaction_idx = {reaction: i for i, reaction in enumerate(metabolism.reaction_names)}\n",
+    "index_to_rxn = {v: k for k, v in reaction_idx.items()}\n",
+    "\n",
+    "metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:51.641749Z",
+     "start_time": "2023-08-16T22:14:43.238530Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "outputs": [],
+   "source": [
+    "heatmap_df = pd.read_excel(\"out/new_env_analysis/pathway_heatmap.xlsx\")\n",
+    "heatmap_df = heatmap_df[heatmap_df[env_name] != 0]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:52.316743Z",
+     "start_time": "2023-08-16T22:14:51.647040Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "outputs": [],
+   "source": [
+    "G = nx.DiGraph()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:52.334222Z",
+     "start_time": "2023-08-16T22:14:52.323572Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "outputs": [],
+   "source": [
+    "def find_connecting_reactions(one, two):\n",
+    "    one_reactions = []\n",
+    "    two_reactions = []\n",
+    "    try:\n",
+    "        req_str = f\"https://websvc.biocyc.org/getxml?id=ECOLI:{one}&detail=low\"\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Pathway']['reaction-list']['Reaction']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for rxn in o:\n",
+    "            one_reactions.append(rxn['@frameid'])\n",
+    "\n",
+    "        req_str = f\"https://websvc.biocyc.org/getxml?id=ECOLI:{two}&detail=low\"\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Pathway']['reaction-list']['Reaction']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for rxn in o:\n",
+    "            two_reactions.append(rxn['@frameid'])\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "\n",
+    "    intersection = set(one_reactions).intersection(set(two_reactions))\n",
+    "    print(intersection)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T23:13:44.746521Z",
+     "start_time": "2023-08-16T23:13:44.731300Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set()\n"
+     ]
+    }
+   ],
+   "source": [
+    "find_connecting_reactions(\"PWY0-1335\", \"PWY-7219\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T23:13:59.934754Z",
+     "start_time": "2023-08-16T23:13:59.482930Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "outputs": [],
+   "source": [
+    "def get_pathway_name(pathway):\n",
+    "    try:\n",
+    "        req_str = f\"https://websvc.biocyc.org/getxml?id=ECOLI:{pathway}&detail=low\"\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Pathway']['common-name']['#text']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        return o\n",
+    "    except KeyError:\n",
+    "        pass\n",
+    "    except Exception:\n",
+    "        pass"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T23:13:46.482540Z",
+     "start_time": "2023-08-16T23:13:46.475923Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "outputs": [],
+   "source": [
+    "# Change K here to select the top k reactions/pathways to plot\n",
+    "k = 100\n",
+    "heatmap_df.sort_values(by=[env_name], ascending=False, inplace=True)\n",
+    "top_increase = [heatmap_df.iloc[i, 1] for i in range(k)]\n",
+    "bottom_increase = [heatmap_df.iloc[i, 1] for i in range(-1, -1*k - 1, -1)]\n",
+    "\n",
+    "if \"Non-zero Values for each environment\" in top_increase:\n",
+    "    top_increase.remove(\"Non-zero Values for each environment\")\n",
+    "elif \"Non-zero Values for each environment\" in bottom_increase:\n",
+    "    bottom_increase.remove(\"Non-zero Values for each environment\")\n",
+    "\n",
+    "count = 0\n",
+    "for pathway in top_increase:\n",
+    "    row = heatmap_df[heatmap_df['Pathways'] == pathway].index[0]\n",
+    "    flux = heatmap_df[env_name].loc[row]\n",
+    "    name = get_pathway_name(pathway)\n",
+    "\n",
+    "    color = \"green\"\n",
+    "    if flux < 0 or abs(flux) < 0.1:\n",
+    "        break\n",
+    "\n",
+    "    G.add_node(count, label=name, size=abs(flux*30), color=color, title=pathway)\n",
+    "    count += 1\n",
+    "\n",
+    "for pathway in bottom_increase:\n",
+    "    row = heatmap_df[heatmap_df['Pathways'] == pathway].index[0]\n",
+    "    flux = heatmap_df[env_name].loc[row]\n",
+    "    name = get_pathway_name(pathway)\n",
+    "\n",
+    "    color = \"red\"\n",
+    "    if flux > 0 or abs(flux) < 0.1:\n",
+    "        break\n",
+    "\n",
+    "    G.add_node(count, label=pathway, size=abs(flux*30), color=color, title=pathway)\n",
+    "    count += 1\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:59.012437Z",
+     "start_time": "2023-08-16T22:14:52.386355Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'label': 'NADH to fumarate electron transfer', 'size': 71.2772610480168, 'color': 'green', 'title': 'PWY0-1336'}\n",
+      "{'label': 'NADH to dimethyl sulfoxide electron transfer', 'size': 71.2772610480168, 'color': 'green', 'title': 'PWY0-1348'}\n",
+      "{'label': 'nitrate reduction III (dissimilatory)', 'size': 61.8855509774106, 'color': 'green', 'title': 'PWY0-1321'}\n",
+      "{'label': 'L-alanine degradation I', 'size': 56.269925742461396, 'color': 'green', 'title': 'ALADEG-PWY'}\n",
+      "{'label': 'L-aspartate degradation III (anaerobic)', 'size': 38.9882754774837, 'color': 'green', 'title': 'PWY-8294'}\n",
+      "{'label': 'UMP biosynthesis I', 'size': 12.63840817311879, 'color': 'green', 'title': 'PWY-5686'}\n",
+      "{'label': 'glycolysis II (from fructose 6-phosphate)', 'size': 8.54316559689027, 'color': 'green', 'title': 'PWY-5484'}\n",
+      "{'label': 'superpathway of glycolysis, pyruvate dehydrogenase, TCA, and glyoxylate bypass', 'size': 8.15241469630458, 'color': 'green', 'title': 'GLYCOLYSIS-TCA-GLYOX-BYPASS'}\n",
+      "{'label': 'glycolysis I (from glucose 6-phosphate)', 'size': 7.2288324281379, 'color': 'green', 'title': 'GLYCOLYSIS'}\n",
+      "{'label': 'glycerol degradation V', 'size': 5.74187594311098, 'color': 'green', 'title': 'GLYCEROLMETAB-PWY'}\n",
+      "{'label': 'palmitate biosynthesis II (type II fatty acid synthase)', 'size': 5.73620381333175, 'color': 'green', 'title': 'PWY-5971'}\n",
+      "{'label': 'PWY0-1335', 'size': 44.2617922591245, 'color': 'red', 'title': 'PWY0-1335'}\n",
+      "{'label': 'PWY-6519', 'size': 37.4146384464336, 'color': 'red', 'title': 'PWY-6519'}\n",
+      "{'label': 'PWY-5755', 'size': 25.81014019712985, 'color': 'red', 'title': 'PWY-5755'}\n",
+      "{'label': 'ASPARTATESYN-PWY', 'size': 20.96667872774646, 'color': 'red', 'title': 'ASPARTATESYN-PWY'}\n",
+      "{'label': 'PWY-6890', 'size': 19.445751372008793, 'color': 'red', 'title': 'PWY-6890'}\n",
+      "{'label': 'PWY-6894', 'size': 19.445751372008793, 'color': 'red', 'title': 'PWY-6894'}\n",
+      "{'label': 'MENAQUINONESYN-PWY', 'size': 18.777288054644792, 'color': 'red', 'title': 'MENAQUINONESYN-PWY'}\n",
+      "{'label': 'ACETOACETATE-DEG-PWY', 'size': 18.70731922321689, 'color': 'red', 'title': 'ACETOACETATE-DEG-PWY'}\n",
+      "{'label': 'PWY0-1507', 'size': 18.70731922321689, 'color': 'red', 'title': 'PWY0-1507'}\n",
+      "{'label': 'PWY0-43', 'size': 17.07570176097936, 'color': 'red', 'title': 'PWY0-43'}\n",
+      "{'label': 'PWY-5785', 'size': 16.95500652742509, 'color': 'red', 'title': 'PWY-5785'}\n",
+      "{'label': 'PWY-6892', 'size': 16.52189630345277, 'color': 'red', 'title': 'PWY-6892'}\n",
+      "{'label': 'PWY-6614', 'size': 13.05687280174458, 'color': 'red', 'title': 'PWY-6614'}\n",
+      "{'label': 'PWY-6543', 'size': 13.05687280174458, 'color': 'red', 'title': 'PWY-6543'}\n",
+      "{'label': 'PWY-6147', 'size': 13.05687280174458, 'color': 'red', 'title': 'PWY-6147'}\n",
+      "{'label': 'PWY-5783', 'size': 12.513699751331309, 'color': 'red', 'title': 'PWY-5783'}\n",
+      "{'label': 'PWY-5852', 'size': 12.513699751331309, 'color': 'red', 'title': 'PWY-5852'}\n",
+      "{'label': 'HEMESYN2-PWY', 'size': 12.32389660614708, 'color': 'red', 'title': 'HEMESYN2-PWY'}\n",
+      "{'label': 'PWY-7220', 'size': 11.69181439176759, 'color': 'red', 'title': 'PWY-7220'}\n",
+      "{'label': 'PWY-5123', 'size': 11.67112841629254, 'color': 'red', 'title': 'PWY-5123'}\n",
+      "{'label': 'PWY0-1280', 'size': 11.04388633583199, 'color': 'red', 'title': 'PWY0-1280'}\n",
+      "{'label': 'PWY-5837', 'size': 10.72602835828398, 'color': 'red', 'title': 'PWY-5837'}\n",
+      "{'label': 'RIBOSYN2-PWY', 'size': 10.50688991739423, 'color': 'red', 'title': 'RIBOSYN2-PWY'}\n",
+      "{'label': 'GLUTDEG-PWY', 'size': 10.48333936387323, 'color': 'red', 'title': 'GLUTDEG-PWY'}\n",
+      "{'label': 'TCA', 'size': 10.2846064911531, 'color': 'red', 'title': 'TCA'}\n",
+      "{'label': 'PWY0-1353', 'size': 9.988891674811649, 'color': 'red', 'title': 'PWY0-1353'}\n",
+      "{'label': 'PWY0-1329', 'size': 9.988891674811649, 'color': 'red', 'title': 'PWY0-1329'}\n",
+      "{'label': 'PWY-5194', 'size': 9.38864402732238, 'color': 'red', 'title': 'PWY-5194'}\n",
+      "{'label': 'PWY0-1569', 'size': 9.09653645436471, 'color': 'red', 'title': 'PWY0-1569'}\n",
+      "{'label': 'PYRIDOXSYN-PWY', 'size': 8.75818505831154, 'color': 'red', 'title': 'PYRIDOXSYN-PWY'}\n",
+      "{'label': 'PWY-7222', 'size': 6.78931522186944, 'color': 'red', 'title': 'PWY-7222'}\n",
+      "{'label': 'PWY-6897', 'size': 6.48191712400293, 'color': 'red', 'title': 'PWY-6897'}\n",
+      "{'label': 'PWY-5188', 'size': 6.0964412681913895, 'color': 'red', 'title': 'PWY-5188'}\n",
+      "{'label': 'PWY-7346', 'size': 5.9712137137995, 'color': 'red', 'title': 'PWY-7346'}\n",
+      "{'label': 'PWY-5901', 'size': 4.81468187553459, 'color': 'red', 'title': 'PWY-5901'}\n",
+      "{'label': 'HEME-BIOSYNTHESIS-II-1', 'size': 4.69432201366119, 'color': 'red', 'title': 'HEME-BIOSYNTHESIS-II-1'}\n",
+      "{'label': 'PWY-6151', 'size': 4.647533268753749, 'color': 'red', 'title': 'PWY-6151'}\n",
+      "{'label': 'GLYCOLATEMET-PWY', 'size': 4.0320150157578, 'color': 'red', 'title': 'GLYCOLATEMET-PWY'}\n",
+      "{'label': 'PWY-6153', 'size': 3.63861458174586, 'color': 'red', 'title': 'PWY-6153'}\n",
+      "{'label': 'NADPHOS-DEPHOS-PWY', 'size': 3.52182518111361, 'color': 'red', 'title': 'NADPHOS-DEPHOS-PWY'}\n",
+      "{'label': 'PLPSAL-PWY', 'size': 3.30025969152822, 'color': 'red', 'title': 'PLPSAL-PWY'}\n",
+      "{'label': 'PWY-6703', 'size': 3.26421820043613, 'color': 'red', 'title': 'PWY-6703'}\n",
+      "{'label': 'PWY0-1433', 'size': 3.26421820043613, 'color': 'red', 'title': 'PWY0-1433'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_nodes = list(G.nodes(data=True))\n",
+    "for node in all_nodes:\n",
+    "    print(node[1])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:59.028993Z",
+     "start_time": "2023-08-16T22:14:59.018057Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "out/new_env_analysis/pathway_only/Nitrate_as_reduction_target_pathway_100_graph.html\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<IPython.lib.display.IFrame at 0x150e57be0>",
+      "text/html": "\n        <iframe\n            width=\"1500px\"\n            height=\"1000px\"\n            src=\"out/new_env_analysis/pathway_only/Nitrate_as_reduction_target_pathway_100_graph.html\"\n            frameborder=\"0\"\n            allowfullscreen\n            \n        ></iframe>\n        "
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nt = Network('1000px', '1500px', notebook=True, cdn_resources='in_line', directed=True)\n",
+    "nt.from_nx(G)\n",
+    "\n",
+    "nt.show_buttons(filter_=['physics'])\n",
+    "nt.show(f'out/new_env_analysis/pathway_only/{env_name}_pathway_{k}_graph.html')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:59.239306Z",
+     "start_time": "2023-08-16T22:14:59.033330Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-16T22:14:59.279971Z",
+     "start_time": "2023-08-16T22:14:59.240073Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ecoli/processes/metabolism_redux.py b/ecoli/processes/metabolism_redux.py
index 6a1fdf5a3..b8560f889 100644
--- a/ecoli/processes/metabolism_redux.py
+++ b/ecoli/processes/metabolism_redux.py
@@ -231,11 +231,11 @@ def next_update(self, timestep, states):
 
         # Initialize indices
         if self.homeostatic_metabolite_idx is None:
-            bulk_ids = states['bulk']['id']
-            self.homeostatic_metabolite_idx = bulk_name_to_idx(self.homeostatic_metabolites, bulk_ids)
-            self.catalyst_idx = bulk_name_to_idx(self.catalyst_ids, bulk_ids)
-            self.kinetics_enzymes_idx = bulk_name_to_idx(self.kinetic_constraint_enzymes, bulk_ids)
-            self.kinetics_substrates_idx = bulk_name_to_idx(self.kinetic_constraint_substrates, bulk_ids)
+            self.bulk_ids = states['bulk']['id']
+            self.homeostatic_metabolite_idx = bulk_name_to_idx(self.homeostatic_metabolites, self.bulk_ids)
+            self.catalyst_idx = bulk_name_to_idx(self.catalyst_ids, self.bulk_ids)
+            self.kinetics_enzymes_idx = bulk_name_to_idx(self.kinetic_constraint_enzymes, self.bulk_ids)
+            self.kinetics_substrates_idx = bulk_name_to_idx(self.kinetic_constraint_substrates, self.bulk_ids)
 
         # metabolites not in either set are constrained to zero uptake.
         exchange_data = states['environment']['exchange_data']
diff --git a/notebooks/fbagd/metal_binding.ipynb b/notebooks/fbagd/metal_binding.ipynb
new file mode 100644
index 000000000..486c61c3e
--- /dev/null
+++ b/notebooks/fbagd/metal_binding.ipynb
@@ -0,0 +1,1221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-06-24T00:11:31.734198Z",
+     "start_time": "2023-06-24T00:11:31.728052Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import pprint\n",
+    "import ast\n",
+    "import re\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import requests\n",
+    "import xmltodict\n",
+    "\n",
+    "pp = pprint.PrettyPrinter(depth=6)\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/vivarium-ecoli'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "outputs": [],
+   "source": [
+    "complexation_rxn_df = pd.read_csv('reconstruction/ecoli/flat/complexation_reactions.tsv', sep='\\t')\n",
+    "stoich_series = complexation_rxn_df.loc[:,['id', 'stoichiometry']].dropna().reset_index(drop=True)\n",
+    "\n",
+    "stoich_list = []\n",
+    "for i, stoich in enumerate(stoich_series.loc[:, 'stoichiometry']):\n",
+    "\n",
+    "    stoich = stoich.replace('null', '-1')\n",
+    "\n",
+    "    stoich = ast.literal_eval(stoich)\n",
+    "    for k,v in stoich.items():\n",
+    "        stoich_list.append([k, v, stoich_series.loc[i, 'id'], None, None, None])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-24T00:11:36.003825Z",
+     "start_time": "2023-06-24T00:11:35.970943Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                               complex  stoichiometry  \\\n0                                1-PFK              1   \n1                2OXOGLUTARATEDEH-CPLX              1   \n2          3-ISOPROPYLMALDEHYDROG-CPLX              1   \n3              3-ISOPROPYLMALISOM-CPLX              1   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX              1   \n...                                ...            ...   \n1096                        CPLX0-8053              1   \n1097                        CPLX0-8253              1   \n1098                          SRP-CPLX              1   \n1099                     CPLX0-7796APO              1   \n1100                         ARCB-CPLX              1   \n\n                                  reaction enzyme-reaction cofactors  \\\n0                                1-PFK_RXN            None      None   \n1                2OXOGLUTARATEDEH-CPLX_RXN            None      None   \n2          3-ISOPROPYLMALDEHYDROG-CPLX_RXN            None      None   \n3              3-ISOPROPYLMALISOM-CPLX_RXN            None      None   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX_RXN            None      None   \n...                                    ...             ...       ...   \n1096                        CPLX0-8053_RXN            None      None   \n1097                        CPLX0-8253_RXN            None      None   \n1098                          SRP-CPLX_RXN            None      None   \n1099                     CPLX0-7796APO_RXN            None      None   \n1100                         ARCB-CPLX_RXN            None      None   \n\n     metal_binding  \n0             None  \n1             None  \n2             None  \n3             None  \n4             None  \n...            ...  \n1096          None  \n1097          None  \n1098          None  \n1099          None  \n1100          None  \n\n[1101 rows x 6 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>stoichiometry</th>\n      <th>reaction</th>\n      <th>enzyme-reaction</th>\n      <th>cofactors</th>\n      <th>metal_binding</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>1</td>\n      <td>1-PFK_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>1</td>\n      <td>2OXOGLUTARATEDEH-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>1</td>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>1</td>\n      <td>3-ISOPROPYLMALISOM-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>1</td>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1096</th>\n      <td>CPLX0-8053</td>\n      <td>1</td>\n      <td>CPLX0-8053_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1097</th>\n      <td>CPLX0-8253</td>\n      <td>1</td>\n      <td>CPLX0-8253_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1098</th>\n      <td>SRP-CPLX</td>\n      <td>1</td>\n      <td>SRP-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1099</th>\n      <td>CPLX0-7796APO</td>\n      <td>1</td>\n      <td>CPLX0-7796APO_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1100</th>\n      <td>ARCB-CPLX</td>\n      <td>1</td>\n      <td>ARCB-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n<p>1101 rows × 6 columns</p>\n</div>"
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pre_complex_df = pd.DataFrame(stoich_list, columns=['complex', 'stoichiometry', 'reaction', 'enzyme-reaction', 'cofactors', 'metal_binding'])\n",
+    "pre_complex_df = pre_complex_df[pre_complex_df['stoichiometry'] > 0].reset_index(drop=True)\n",
+    "pre_complex_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-24T00:11:36.157834Z",
+     "start_time": "2023-06-24T00:11:36.151859Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Connect to EcoCyc to go from Complex -> Cofactor + Metal sites"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-24T00:11:37.445849Z",
+     "start_time": "2023-06-24T00:11:37.195264Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'@ptools-version': '27.0',\n",
+      " '@xml:base': 'http://BioCyc.org/apixml?fn=monomers-of-protein%26id=ECOLI:CPLX0-8167%26detail=NONE',\n",
+      " 'Protein': [{'@frameid': 'HYAC-MONOMER',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              '@resource': 'getxml?ECOLI:HYAC-MONOMER'},\n",
+      "             {'@frameid': 'HYAB-MONOMER',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              '@resource': 'getxml?ECOLI:HYAB-MONOMER'},\n",
+      "             {'@frameid': 'HYAA-MONOMER',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              '@resource': 'getxml?ECOLI:HYAA-MONOMER'}],\n",
+      " 'metadata': {'num_results': '3',\n",
+      "              'query': 'fn=monomers-of-protein&id=ECOLI:CPLX0-8167&detail=NONE',\n",
+      "              'service_name': 'apixml',\n",
+      "              'url': 'http://BioCyc.org/'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "protein = 'CPLX0-8167'\n",
+    "\n",
+    "# req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{protein}&detail=high'\n",
+    "req_str = f'https://websvc.biocyc.org/apixml?fn=monomers-of-protein&id=ECOLI:{protein}&detail=none'\n",
+    "\n",
+    "r = s.get(req_str)\n",
+    "\n",
+    "o = xmltodict.parse(r.content)['ptools-xml']\n",
+    "pp.pprint(o)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-24T00:15:02.784067Z",
+     "start_time": "2023-06-24T00:15:02.741949Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPLX0-2423 404\n",
+      "CPLX0-3976 404\n",
+      "RECFOR-CPLX 404\n",
+      "CPLX0-7796APO 404\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(pre_complex_df.index)): #\n",
+    "\n",
+    "    complex = pre_complex_df.loc[i, 'complex']\n",
+    "\n",
+    "    req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{complex}&detail=low'\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    if r.status_code != 200:\n",
+    "        print(complex, r.status_code)\n",
+    "        continue\n",
+    "\n",
+    "    o = xmltodict.parse(r.content)['ptools-xml']['Protein']\n",
+    "\n",
+    "    # don't allow non-top-level complexes that dont have enzymatic reactions.\n",
+    "    if 'component-of' in o and 'catalyzes' not in o:\n",
+    "        continue\n",
+    "\n",
+    "    # check if components have enzymatic-reaction:\n",
+    "    components = o['component']\n",
+    "    if type(components) is dict:\n",
+    "        components = [components]\n",
+    "\n",
+    "    for component in components:\n",
+    "\n",
+    "        if \"Protein\" in component:\n",
+    "            component_id = component['Protein']['@frameid']\n",
+    "\n",
+    "            req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{component_id}&detail=high'\n",
+    "            rc = s.get(req_str)\n",
+    "            oc = xmltodict.parse(rc.content)['ptools-xml']['Protein']\n",
+    "\n",
+    "            feature_set = set()\n",
+    "            if 'has-feature' in oc:\n",
+    "                features = oc['has-feature']\n",
+    "\n",
+    "                if type(features) is dict:\n",
+    "                    features = [features]\n",
+    "\n",
+    "                for feature in features:\n",
+    "                    if 'parent' not in feature['Feature']:\n",
+    "                        continue\n",
+    "\n",
+    "                    category = feature['Feature']['parent']['Feature']['@frameid']\n",
+    "                    if category == 'Metal-Binding-Sites' and 'comment' in feature['Feature']:\n",
+    "\n",
+    "                        # Detect match to any of the allowed metal names and allowed cofactor names and add to list\n",
+    "                        comment = feature['Feature']['comment']['#text']\n",
+    "                        feature_set.add(comment)\n",
+    "\n",
+    "                        pre_complex_df.at[i, 'metal_binding'] = list(feature_set)\n",
+    "\n",
+    "\n",
+    "    # if enzyme\n",
+    "    if 'catalyzes' in o:\n",
+    "        o = o['catalyzes']['Enzymatic-Reaction']\n",
+    "\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        cofactor_set = set()\n",
+    "        for enzrxn in o:\n",
+    "            enz_id = enzrxn['@frameid']\n",
+    "\n",
+    "            enz_req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{enz_id}&detail=high'\n",
+    "\n",
+    "            rz = s.get(enz_req_str)\n",
+    "            oe = xmltodict.parse(rz.content)['ptools-xml']['Enzymatic-Reaction']\n",
+    "\n",
+    "            pre_complex_df.at[i, 'enzyme-reaction'] = enz_id\n",
+    "\n",
+    "            if \"cofactor\" in oe:\n",
+    "                oe = oe['cofactor']\n",
+    "\n",
+    "                if type(oe) is dict:\n",
+    "                    oe = [oe]\n",
+    "\n",
+    "                for cofactor in oe:\n",
+    "                    cof = cofactor['Compound']['@frameid']\n",
+    "                    cofactor_set.add(cof)\n",
+    "\n",
+    "        pre_complex_df.at[i, 'cofactors'] = cofactor_set\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T17:35:47.859429Z",
+     "start_time": "2023-06-21T17:26:36.495196Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'UniProt: Calcium 1.',\n 'UniProt: Calcium 1; via carbonyl oxygen.',\n 'UniProt: Calcium 1; via carbonyl oxygen; in dimeric form.',\n 'UniProt: Calcium 1; via carbonyl oxygen; shared with dimeric partner.',\n 'UniProt: Calcium 2.',\n 'UniProt: Calcium 2; in dimeric form.',\n 'UniProt: Calcium 2; via carbonyl oxygen.',\n 'UniProt: Calcium 2; via carbonyl oxygen; in dimeric form.',\n 'UniProt: Calcium 3; in monomeric form.',\n 'UniProt: Calcium 3; shared with dimeric partner.',\n 'UniProt: Calcium 3; via carbonyl oxygen; shared with dimeric partner.',\n 'UniProt: Calcium.',\n 'UniProt: Calcium; via carbonyl oxygen.',\n 'UniProt: Cobalt (adenosylcob(III)alamin axial ligand).',\n 'UniProt: Cobalt.',\n 'UniProt: Cobalt; via tele nitrogen.',\n 'UniProt: Copper.',\n 'UniProt: Copper; via pros nitrogen.',\n 'UniProt: Copper; via tele nitrogen.',\n 'UniProt: Divalent metal cation 1.',\n 'UniProt: Divalent metal cation 1; via carbamate group.',\n 'UniProt: Divalent metal cation 1; via carbonyl oxygen.',\n 'UniProt: Divalent metal cation 2.',\n 'UniProt: Divalent metal cation 2; via carbamate group.',\n 'UniProt: Divalent metal cation 3.',\n 'UniProt: Divalent metal cation.',\n 'UniProt: Divalent metal cation; shared with dimeric partner.',\n 'UniProt: Divalent metal cation; via pros nitrogen.',\n 'UniProt: Divalent metal cation; via tele nitrogen.',\n 'UniProt: Fe(2+); catalytic.',\n 'UniProt: Iron (heme 1 axial ligand).',\n 'UniProt: Iron (heme 2 axial ligand).',\n 'UniProt: Iron (heme 3 axial ligand).',\n 'UniProt: Iron (heme 4 axial ligand).',\n 'UniProt: Iron (heme 5 axial ligand).',\n 'UniProt: Iron (heme B 1 axial ligand).',\n 'UniProt: Iron (heme B 2 axial ligand).',\n 'UniProt: Iron (heme axial ligand).',\n 'UniProt: Iron (heme b axial ligand).',\n 'UniProt: Iron (heme b axial ligand); shared with dimeric partner.',\n 'UniProt: Iron (heme b proximal ligand); via tele nitrogen.',\n 'UniProt: Iron (heme b558 axial ligand).',\n 'UniProt: Iron (heme b595 axial ligand).',\n 'UniProt: Iron (heme distal ligand).',\n 'UniProt: Iron (heme o axial ligand).',\n 'UniProt: Iron (heme proximal ligand).',\n 'UniProt: Iron (siroheme axial ligand).',\n 'UniProt: Iron 1.',\n 'UniProt: Iron 1; shared with dodecameric partner.',\n 'UniProt: Iron 2.',\n 'UniProt: Iron 3.',\n 'UniProt: Iron-sulfur (2Fe-2S).',\n 'UniProt: Iron-sulfur (2Fe-2S); shared with dimeric partner.',\n 'UniProt: Iron-sulfur (2Fe-2S); via persulfide group; shared with IscU.',\n 'UniProt: Iron-sulfur (2Fe-2S); via pros nitrogen.',\n 'UniProt: Iron-sulfur (3Fe-4S).',\n 'UniProt: Iron-sulfur (4Fe-4S) 1.',\n 'UniProt: Iron-sulfur (4Fe-4S) 2.',\n 'UniProt: Iron-sulfur (4Fe-4S) 2; 4Fe-4S-S-AdoMet.',\n 'UniProt: Iron-sulfur (4Fe-4S) 3.',\n 'UniProt: Iron-sulfur (4Fe-4S) 4.',\n 'UniProt: Iron-sulfur (4Fe-4S).',\n 'UniProt: Iron-sulfur (4Fe-4S-S-AdoMet).',\n 'UniProt: Iron-sulfur 1 (2Fe-2S).',\n 'UniProt: Iron-sulfur 2 (4Fe-4S).',\n 'UniProt: Iron-sulfur 3 (3Fe-4S).',\n 'UniProt: Iron-sulfur.',\n 'UniProt: Iron.',\n 'UniProt: Iron; catalytic.',\n 'UniProt: Iron; via tele nitrogen.',\n 'UniProt: Iron; via tele nitrogen; catalytic.',\n 'UniProt: Magnesium 1.',\n 'UniProt: Magnesium 1; catalytic.',\n 'UniProt: Magnesium 1; via carbonyl oxygen.',\n 'UniProt: Magnesium 1; via pros nitrogen.',\n 'UniProt: Magnesium 2.',\n 'UniProt: Magnesium 2; catalytic.',\n 'UniProt: Magnesium 3.',\n 'UniProt: Magnesium or cobalt.',\n 'UniProt: Magnesium or manganese.',\n 'UniProt: Magnesium.',\n 'UniProt: Magnesium; catalytic.',\n 'UniProt: Magnesium; via carbamate group.',\n 'UniProt: Magnesium; via carbonyl oxygen.',\n 'UniProt: Magnesium; via tele nitrogen.',\n 'UniProt: Manganese 1.',\n 'UniProt: Manganese 1; via pros nitrogen.',\n 'UniProt: Manganese 1; via tele nitrogen.',\n 'UniProt: Manganese 2.',\n 'UniProt: Manganese 2; via tele nitrogen.',\n 'UniProt: Manganese 3.',\n 'UniProt: Manganese 4.',\n 'UniProt: Manganese.',\n 'UniProt: Manganese; via pros nitrogen.',\n 'UniProt: Molybdate.',\n 'UniProt: Molybdate; via amide nitrogen.',\n 'UniProt: Molybdenum (Mo-bis(molybdopterin guanine dinucleotide) metal ligand).',\n 'UniProt: Molybdenum (Mo-molybdopterin metal ligand).',\n 'UniProt: Molybdenum (Mo-molybdopterin metal ligand); via amide nitrogen.',\n 'UniProt: Molybdenum (Mo-molybdopterin metal ligand); via carbonyl oxygen.',\n 'UniProt: Molybdenum.',\n 'UniProt: Nickel 1.',\n 'UniProt: Nickel 2.',\n 'UniProt: Nickel.',\n 'UniProt: Nickel; shared with dimeric partner.',\n 'UniProt: Nickel; via tele nitrogen.',\n 'UniProt: Nickel; via tele nitrogen; shared with dimeric partner.',\n 'UniProt: Potassium 1.',\n 'UniProt: Potassium 1; via carbonyl oxygen.',\n 'UniProt: Potassium 2; via carbonyl oxygen.',\n 'UniProt: Potassium.',\n 'UniProt: Potassium; via carbonyl oxygen.',\n 'UniProt: Potassium; via carbonyl oxygen; shared with tetrameric partner.',\n 'UniProt: Sodium.',\n 'UniProt: Sodium; via carbonyl oxygen.',\n 'UniProt: Zinc 1.',\n 'UniProt: Zinc 1; catalytic.',\n 'UniProt: Zinc 1; via carbamate group.',\n 'UniProt: Zinc 1; via carbamate group; catalytic.',\n 'UniProt: Zinc 1; via tele nitrogen.',\n 'UniProt: Zinc 1; via tele nitrogen; catalytic.',\n 'UniProt: Zinc 2.',\n 'UniProt: Zinc 2; catalytic.',\n 'UniProt: Zinc 2; via carbamate group.',\n 'UniProt: Zinc 2; via carbamate group; catalytic.',\n 'UniProt: Zinc 2; via pros nitrogen.',\n 'UniProt: Zinc 2; via pros nitrogen; catalytic.',\n 'UniProt: Zinc 2; via tele nitrogen.',\n 'UniProt: Zinc 2; via tele nitrogen; catalytic.',\n 'UniProt: Zinc 3.',\n 'UniProt: Zinc.',\n 'UniProt: Zinc; catalytic.',\n 'UniProt: Zinc; shared with EIIA-Glc.',\n 'UniProt: Zinc; shared with dimeric partner.',\n 'UniProt: Zinc; shared with glycerol kinase.',\n 'UniProt: Zinc; structural.',\n 'UniProt: Zinc; via pros nitrogen.',\n 'UniProt: Zinc; via pros nitrogen; catalytic.',\n 'UniProt: Zinc; via tele nitrogen.',\n 'UniProt: Zn(2+); catalytic.'}"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_features = set()\n",
+    "\n",
+    "for i in range(len(pre_complex_df.index)):\n",
+    "\n",
+    "        if pre_complex_df.loc[i, 'metal_binding'] is not None:\n",
+    "            # only include set elements that have Uniprot in string\n",
+    "            metal_binding = pre_complex_df.loc[i, 'metal_binding']\n",
+    "            for feature in metal_binding:\n",
+    "                if 'UniProt' in feature:\n",
+    "                    total_features.add(feature)\n",
+    "            metal_binding = [x for x in metal_binding if 'UniProt' in x]\n",
+    "\n",
+    "            pre_complex_df.at[i, 'metal_binding'] = metal_binding\n",
+    "\n",
+    "            total_features.update(metal_binding)\n",
+    "\n",
+    "total_features"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T17:35:47.893545Z",
+     "start_time": "2023-06-21T17:35:47.866154Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [],
+   "source": [
+    "ALLOWED_METAL_NAMES = {'Iron': 'FE+2', 'Cobalt': 'CO+2', 'Copper': 'CU+2', 'Manganese': 'MN+2', 'Molybdenum': 'MO+2', 'Nickel': 'NI+2', 'Tungsten': 'W', 'Zinc': 'ZN+2',\n",
+    "                       'Calcium': 'CA+2', 'Magnesium': 'MG+2', 'Sodium': 'NA+', 'Potassium': 'K+',\n",
+    "                       'Iron-sulfur \\(4Fe-4S\\)': 'CPD-7', 'Iron-sulfur \\(2Fe-2S\\)': 'CPD-6', 'heme': 'heme', 'Molybdate': 'CPD-3'}\n",
+    "\n",
+    "NON_REGEX_METAL = {'Iron': 'FE+2', 'Cobalt': 'CO+2', 'Copper': 'CU+2', 'Manganese': 'MN+2', 'Molybdenum': 'MO+2', 'Nickel': 'NI+2', 'Tungsten': 'W', 'Zinc': 'ZN+2',\n",
+    "                       'Calcium': 'CA+2', 'Magnesium': 'MG+2', 'Sodium': 'NA+', 'Potassium': 'K+',\n",
+    "                       'Iron-sulfur (4Fe-4S)': 'CPD-7', 'Iron-sulfur (2Fe-2S)': 'CPD-6', 'heme': 'heme', 'Molybdate': 'CPD-3'}\n",
+    "\n",
+    "pre_complex_df['metal_binding_processed'] = 0\n",
+    "pre_complex_df['metal_binding_processed'] = pre_complex_df['metal_binding_processed'].astype(object)\n",
+    "\n",
+    "metal_pattern = '|'.join(ALLOWED_METAL_NAMES.keys())\n",
+    "metal_regex = re.compile(f'(({metal_pattern})(\\s\\d[\\.,;]|[\\.,;]|\\s\\())')\n",
+    "\n",
+    "\n",
+    "for i in range(len(pre_complex_df.index)):\n",
+    "\n",
+    "    if pre_complex_df.loc[i, 'metal_binding'] is not None:\n",
+    "        metal_binding = pre_complex_df.loc[i, 'metal_binding']\n",
+    "\n",
+    "        metal_count_dict = {}\n",
+    "        existing_matches = set()\n",
+    "\n",
+    "        for feature in metal_binding:\n",
+    "            matches = metal_regex.search(feature)\n",
+    "            if matches:\n",
+    "                metal = matches.group(0)[:-1]\n",
+    "\n",
+    "                # eliminate duplicates\n",
+    "                if metal not in existing_matches:\n",
+    "\n",
+    "                    existing_matches.add(metal)\n",
+    "\n",
+    "                    if 'heme' in feature:\n",
+    "                        metal = metal.replace('Iron', 'heme')\n",
+    "\n",
+    "                    # check if last char of metal is a number, then crop\n",
+    "                    if metal[-1].isdigit():\n",
+    "                        metal = metal[:-2]\n",
+    "\n",
+    "                    metal = metal.strip()\n",
+    "\n",
+    "                    # replace metal name with allowed metal name\n",
+    "                    metal = NON_REGEX_METAL[metal]\n",
+    "\n",
+    "                    if metal in metal_count_dict:\n",
+    "                        metal_count_dict[metal] += 1\n",
+    "                    else:\n",
+    "                        metal_count_dict[metal] = 1\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "        pre_complex_df.at[i, 'metal_binding_processed'] = metal_count_dict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T17:35:47.907513Z",
+     "start_time": "2023-06-21T17:35:47.889213Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                               complex  stoichiometry  \\\n0                                1-PFK              1   \n1                2OXOGLUTARATEDEH-CPLX              1   \n2          3-ISOPROPYLMALDEHYDROG-CPLX              1   \n3              3-ISOPROPYLMALISOM-CPLX              1   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX              1   \n...                                ...            ...   \n1096                        CPLX0-8053              1   \n1097                        CPLX0-8253              1   \n1098                          SRP-CPLX              1   \n1099                     CPLX0-7796APO              1   \n1100                         ARCB-CPLX              1   \n\n                                  reaction  \\\n0                                1-PFK_RXN   \n1                2OXOGLUTARATEDEH-CPLX_RXN   \n2          3-ISOPROPYLMALDEHYDROG-CPLX_RXN   \n3              3-ISOPROPYLMALISOM-CPLX_RXN   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX_RXN   \n...                                    ...   \n1096                        CPLX0-8053_RXN   \n1097                        CPLX0-8253_RXN   \n1098                          SRP-CPLX_RXN   \n1099                     CPLX0-7796APO_RXN   \n1100                         ARCB-CPLX_RXN   \n\n                         enzyme-reaction  \\\n0                  1PFRUCTPHOSPHN-ENZRXN   \n1                2OXOGLUTARATEDEH-ENZRXN   \n2          3-ISOPROPYLMALDEHYDROG-ENZRXN   \n3              3-ISOPROPYLMALISOM-ENZRXN   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-ENZRXN   \n...                                  ...   \n1096                                None   \n1097                                None   \n1098                                None   \n1099                                None   \n1100                        ENZRXN0-8568   \n\n                                             cofactors  \\\n0                                               [MG+2]   \n1     [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]   \n2                                         [MG+2, MN+2]   \n3                                              [CPD-7]   \n4                                               [MG+2]   \n...                                                ...   \n1096                                              None   \n1097                                              None   \n1098                                              None   \n1099                                              None   \n1100                                              None   \n\n                                          metal_binding  \\\n0                                                  None   \n1                                                  None   \n2     [UniProt: Magnesium or manganese., UniProt: Ma...   \n3                      [UniProt: Iron-sulfur (4Fe-4S).]   \n4                                 [UniProt: Magnesium.]   \n...                                                 ...   \n1096                                               None   \n1097                                               None   \n1098                                               None   \n1099                                               None   \n1100                                               None   \n\n     metal_binding_processed  \n0                          0  \n1                          0  \n2                {'MG+2': 1}  \n3               {'CPD-7': 1}  \n4                {'MG+2': 1}  \n...                      ...  \n1096                       0  \n1097                       0  \n1098                       0  \n1099                       0  \n1100                       0  \n\n[1101 rows x 7 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>stoichiometry</th>\n      <th>reaction</th>\n      <th>enzyme-reaction</th>\n      <th>cofactors</th>\n      <th>metal_binding</th>\n      <th>metal_binding_processed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>1</td>\n      <td>1-PFK_RXN</td>\n      <td>1PFRUCTPHOSPHN-ENZRXN</td>\n      <td>[MG+2]</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>1</td>\n      <td>2OXOGLUTARATEDEH-CPLX_RXN</td>\n      <td>2OXOGLUTARATEDEH-ENZRXN</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>1</td>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX_RXN</td>\n      <td>3-ISOPROPYLMALDEHYDROG-ENZRXN</td>\n      <td>[MG+2, MN+2]</td>\n      <td>[UniProt: Magnesium or manganese., UniProt: Ma...</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>1</td>\n      <td>3-ISOPROPYLMALISOM-CPLX_RXN</td>\n      <td>3-ISOPROPYLMALISOM-ENZRXN</td>\n      <td>[CPD-7]</td>\n      <td>[UniProt: Iron-sulfur (4Fe-4S).]</td>\n      <td>{'CPD-7': 1}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>1</td>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX_RXN</td>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-ENZRXN</td>\n      <td>[MG+2]</td>\n      <td>[UniProt: Magnesium.]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1096</th>\n      <td>CPLX0-8053</td>\n      <td>1</td>\n      <td>CPLX0-8053_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1097</th>\n      <td>CPLX0-8253</td>\n      <td>1</td>\n      <td>CPLX0-8253_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1098</th>\n      <td>SRP-CPLX</td>\n      <td>1</td>\n      <td>SRP-CPLX_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1099</th>\n      <td>CPLX0-7796APO</td>\n      <td>1</td>\n      <td>CPLX0-7796APO_RXN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1100</th>\n      <td>ARCB-CPLX</td>\n      <td>1</td>\n      <td>ARCB-CPLX_RXN</td>\n      <td>ENZRXN0-8568</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1101 rows × 7 columns</p>\n</div>"
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pre_complex_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T17:35:47.913884Z",
+     "start_time": "2023-06-21T17:35:47.905437Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                               complex  \\\n0                                1-PFK   \n1                2OXOGLUTARATEDEH-CPLX   \n2          3-ISOPROPYLMALDEHYDROG-CPLX   \n3              3-ISOPROPYLMALISOM-CPLX   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX   \n...                                ...   \n1096                        CPLX0-8053   \n1097                        CPLX0-8253   \n1098                          SRP-CPLX   \n1099                     CPLX0-7796APO   \n1100                         ARCB-CPLX   \n\n                                             cofactors metal_binding_processed  \n0                                               [MG+2]                      {}  \n1     [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]                      {}  \n2                                         [MG+2, MN+2]             {'MG+2': 1}  \n3                                              [CPD-7]            {'CPD-7': 1}  \n4                                               [MG+2]             {'MG+2': 1}  \n...                                                ...                     ...  \n1096                                                []                      {}  \n1097                                                []                      {}  \n1098                                                []                      {}  \n1099                                                []                      {}  \n1100                                                []                      {}  \n\n[1101 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>cofactors</th>\n      <th>metal_binding_processed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>[CPD-7]</td>\n      <td>{'CPD-7': 1}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1096</th>\n      <td>CPLX0-8053</td>\n      <td>[]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1097</th>\n      <td>CPLX0-8253</td>\n      <td>[]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1098</th>\n      <td>SRP-CPLX</td>\n      <td>[]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1099</th>\n      <td>CPLX0-7796APO</td>\n      <td>[]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1100</th>\n      <td>ARCB-CPLX</td>\n      <td>[]</td>\n      <td>{}</td>\n    </tr>\n  </tbody>\n</table>\n<p>1101 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for row of pre_complex_df, if metal_binding_processed is 0, set as empty dict\n",
+    "for i in range(len(pre_complex_df.index)):\n",
+    "    if pre_complex_df.loc[i, 'metal_binding_processed'] == 0:\n",
+    "        pre_complex_df.at[i, 'metal_binding_processed'] = {}\n",
+    "\n",
+    "    if pre_complex_df.loc[i, 'cofactors'] == None:\n",
+    "        pre_complex_df.at[i, 'cofactors'] = []\n",
+    "\n",
+    "pre_complex_df.loc[:, ['complex', 'cofactors', 'metal_binding_processed']]\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T17:35:47.984684Z",
+     "start_time": "2023-06-21T17:35:47.920942Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                               complex  \\\n0                                1-PFK   \n1                2OXOGLUTARATEDEH-CPLX   \n2          3-ISOPROPYLMALDEHYDROG-CPLX   \n3              3-ISOPROPYLMALISOM-CPLX   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX   \n...                                ...   \n1083                       UPPSYN-CPLX   \n1084         URACIL-PRIBOSYLTRANS-CPLX   \n1085                       URPHOS-CPLX   \n1088                      XYLISOM-CPLX   \n1091                        CPLX0-1382   \n\n                                             cofactors metal_binding_processed  \n0                                               [MG+2]                      {}  \n1     [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]                      {}  \n2                                         [MG+2, MN+2]             {'MG+2': 1}  \n3                                              [CPD-7]            {'CPD-7': 1}  \n4                                               [MG+2]             {'MG+2': 1}  \n...                                                ...                     ...  \n1083                                            [MG+2]             {'MG+2': 1}  \n1084                                            [MG+2]                      {}  \n1085                                              [K+]                      {}  \n1088                                      [MG+2, MN+2]             {'MG+2': 2}  \n1091                                            [MG+2]                      {}  \n\n[447 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>cofactors</th>\n      <th>metal_binding_processed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>[CPD-7]</td>\n      <td>{'CPD-7': 1}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1083</th>\n      <td>UPPSYN-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>1084</th>\n      <td>URACIL-PRIBOSYLTRANS-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1085</th>\n      <td>URPHOS-CPLX</td>\n      <td>[K+]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1088</th>\n      <td>XYLISOM-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 2}</td>\n    </tr>\n    <tr>\n      <th>1091</th>\n      <td>CPLX0-1382</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only include rows that have cofactors or metal binding\n",
+    "pre_complex_df.loc[(pre_complex_df['cofactors'].map(len) > 0) | (pre_complex_df['metal_binding_processed'].map(len) > 0),['complex', 'cofactors', 'metal_binding_processed']]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T17:55:29.879045Z",
+     "start_time": "2023-06-21T17:55:29.874924Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                              complex  \\\n0                               1-PFK   \n1               2OXOGLUTARATEDEH-CPLX   \n2         3-ISOPROPYLMALDEHYDROG-CPLX   \n3             3-ISOPROPYLMALISOM-CPLX   \n4    3-METHYL-2-OXOBUT-OHCH3XFER-CPLX   \n..                                ...   \n442                       UPPSYN-CPLX   \n443         URACIL-PRIBOSYLTRANS-CPLX   \n444                       URPHOS-CPLX   \n445                      XYLISOM-CPLX   \n446                        CPLX0-1382   \n\n                                            cofactors metal_binding_processed  \n0                                              [MG+2]                      {}  \n1    [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]                      {}  \n2                                        [MG+2, MN+2]             {'MG+2': 1}  \n3                                             [CPD-7]            {'CPD-7': 1}  \n4                                              [MG+2]             {'MG+2': 1}  \n..                                                ...                     ...  \n442                                            [MG+2]             {'MG+2': 1}  \n443                                            [MG+2]                      {}  \n444                                              [K+]                      {}  \n445                                      [MG+2, MN+2]             {'MG+2': 2}  \n446                                            [MG+2]                      {}  \n\n[447 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>cofactors</th>\n      <th>metal_binding_processed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>[CPD-7]</td>\n      <td>{'CPD-7': 1}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <td>UPPSYN-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>URACIL-PRIBOSYLTRANS-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>URPHOS-CPLX</td>\n      <td>[K+]</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>XYLISOM-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 2}</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>CPLX0-1382</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_complex_df =  pre_complex_df.loc[(pre_complex_df['cofactors'].map(len) > 0) | (pre_complex_df['metal_binding_processed'].map(len) > 0),\n",
+    "                                          ['complex', 'cofactors', 'metal_binding_processed']].reset_index(drop=True)\n",
+    "\n",
+    "filtered_complex_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T23:50:55.663323Z",
+     "start_time": "2023-06-21T23:50:55.654291Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                              complex  \\\n0                               1-PFK   \n1               2OXOGLUTARATEDEH-CPLX   \n2         3-ISOPROPYLMALDEHYDROG-CPLX   \n3             3-ISOPROPYLMALISOM-CPLX   \n4    3-METHYL-2-OXOBUT-OHCH3XFER-CPLX   \n..                                ...   \n442                       UPPSYN-CPLX   \n443         URACIL-PRIBOSYLTRANS-CPLX   \n444                       URPHOS-CPLX   \n445                      XYLISOM-CPLX   \n446                        CPLX0-1382   \n\n                                            cofactors metal_binding_processed  \\\n0                                              [MG+2]                      {}   \n1    [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]                      {}   \n2                                        [MG+2, MN+2]             {'MG+2': 1}   \n3                                             [CPD-7]            {'CPD-7': 1}   \n4                                              [MG+2]             {'MG+2': 1}   \n..                                                ...                     ...   \n442                                            [MG+2]             {'MG+2': 1}   \n443                                            [MG+2]                      {}   \n444                                              [K+]                      {}   \n445                                      [MG+2, MN+2]             {'MG+2': 2}   \n446                                            [MG+2]                      {}   \n\n                                              combined  \n0                                          {'MG+2': 1}  \n1    {'LIPOIC-ACID': 1, 'THIAMINE-PYROPHOSPHATE': 1...  \n2                               {'MG+2': 1, 'MN+2': 1}  \n3                                         {'CPD-7': 1}  \n4                                          {'MG+2': 1}  \n..                                                 ...  \n442                                        {'MG+2': 1}  \n443                                        {'MG+2': 1}  \n444                                          {'K+': 1}  \n445                             {'MG+2': 2, 'MN+2': 1}  \n446                                        {'MG+2': 1}  \n\n[447 rows x 4 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>cofactors</th>\n      <th>metal_binding_processed</th>\n      <th>combined</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>{}</td>\n      <td>{'LIPOIC-ACID': 1, 'THIAMINE-PYROPHOSPHATE': 1...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 1}</td>\n      <td>{'MG+2': 1, 'MN+2': 1}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>[CPD-7]</td>\n      <td>{'CPD-7': 1}</td>\n      <td>{'CPD-7': 1}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <td>UPPSYN-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{'MG+2': 1}</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>URACIL-PRIBOSYLTRANS-CPLX</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>URPHOS-CPLX</td>\n      <td>[K+]</td>\n      <td>{}</td>\n      <td>{'K+': 1}</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>XYLISOM-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>{'MG+2': 2}</td>\n      <td>{'MG+2': 2, 'MN+2': 1}</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>CPLX0-1382</td>\n      <td>[MG+2]</td>\n      <td>{}</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 4 columns</p>\n</div>"
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_complex_df['combined'] = 0\n",
+    "filtered_complex_df['combined'] = filtered_complex_df['combined'].astype(object)\n",
+    "\n",
+    "# for each complex, if there are both cofactors and metal binding, combine them\n",
+    "for i in range(len(filtered_complex_df.index)):\n",
+    "    cofactors = filtered_complex_df.at[i, 'cofactors']\n",
+    "    metal_binding = filtered_complex_df.at[i, 'metal_binding_processed']\n",
+    "\n",
+    "    # if both are not empty\n",
+    "    if len(cofactors) > 0 and len(metal_binding) > 0:\n",
+    "\n",
+    "        new_cofactors = set(cofactors) - set(metal_binding)\n",
+    "\n",
+    "        # create new dict that adds new cofactors to metal binding with stoichiometry of 1\n",
+    "        new_metal_binding = metal_binding.copy()\n",
+    "        for cofactor in new_cofactors:\n",
+    "            new_metal_binding[cofactor] = 1\n",
+    "\n",
+    "\n",
+    "        filtered_complex_df.at[i, 'combined'] = new_metal_binding\n",
+    "\n",
+    "    elif len(cofactors) > 0:\n",
+    "        # create dict with stoichiometry of 1 for each cofactor\n",
+    "        new_cofactors = {}\n",
+    "        for cofactor in cofactors:\n",
+    "            new_cofactors[cofactor] = 1\n",
+    "\n",
+    "        filtered_complex_df.at[i, 'combined'] = new_cofactors\n",
+    "\n",
+    "    elif len(metal_binding) > 0:\n",
+    "        filtered_complex_df.at[i, 'combined'] = metal_binding\n",
+    "\n",
+    "filtered_complex_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-21T23:52:07.278056Z",
+     "start_time": "2023-06-21T23:52:07.275285Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "outputs": [],
+   "source": [
+    "filtered_complex_df.to_csv('cofactors_new.csv', index=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:27:50.919277Z",
+     "start_time": "2023-06-22T16:27:50.907076Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Experiment with UniProt API"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'Mn(2+)', 'dbReference': {'type': 'CHEBI', 'id': 'CHEBI:29035'}}]\n",
+      "[{'value': 'Binds 1 Mn(2+) ion per subunit'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use UniProt api to fetch cofactor for protein P00448\n",
+    "import urllib.request\n",
+    "import json\n",
+    "\n",
+    "url = 'https://www.ebi.ac.uk/proteins/api/proteins/P00448'\n",
+    "\n",
+    "req = urllib.request.Request(url)\n",
+    "with urllib.request.urlopen(req) as f:\n",
+    "    response = f.read()\n",
+    "    data = json.loads(response.decode('utf-8'))\n",
+    "\n",
+    "    # get cofactors\n",
+    "    for comment in data['comments']:\n",
+    "        if 'cofactors' in comment:\n",
+    "            print(comment['cofactors'], comment['text'], sep='\\n')\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T21:25:21.915988Z",
+     "start_time": "2023-06-22T21:25:21.100732Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'text': [{'value': 'Destroys superoxide anion radicals which are normally '\n",
+      "                     'produced within the cells and which are toxic to '\n",
+      "                     'biological systems'}],\n",
+      "  'type': 'FUNCTION'},\n",
+      " {'reaction': {'dbReferences': [{'id': 'RHEA:20696', 'type': 'Rhea'},\n",
+      "                                {'id': 'CHEBI:15378', 'type': 'ChEBI'},\n",
+      "                                {'id': 'CHEBI:15379', 'type': 'ChEBI'},\n",
+      "                                {'id': 'CHEBI:16240', 'type': 'ChEBI'},\n",
+      "                                {'id': 'CHEBI:18421', 'type': 'ChEBI'}],\n",
+      "               'ecNumber': '1.15.1.1',\n",
+      "               'name': '2 H(+) + 2 superoxide = H2O2 + O2'},\n",
+      "  'type': 'CATALYTIC_ACTIVITY'},\n",
+      " {'cofactors': [{'dbReference': {'id': 'CHEBI:29035', 'type': 'CHEBI'},\n",
+      "                 'name': 'Mn(2+)'}],\n",
+      "  'text': [{'value': 'Binds 1 Mn(2+) ion per subunit'}],\n",
+      "  'type': 'COFACTOR'},\n",
+      " {'text': [{'value': 'Homodimer'}], 'type': 'SUBUNIT'},\n",
+      " {'text': [{'evidences': [{'code': 'ECO:0000305'}],\n",
+      "            'value': 'Belongs to the iron/manganese superoxide dismutase '\n",
+      "                     'family'}],\n",
+      "  'type': 'SIMILARITY'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pp.pprint(data['comments'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T21:23:33.189130Z",
+     "start_time": "2023-06-22T21:23:33.186586Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Annotate table with counts"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                              complex  \\\n0                               1-PFK   \n1               2OXOGLUTARATEDEH-CPLX   \n2         3-ISOPROPYLMALDEHYDROG-CPLX   \n3             3-ISOPROPYLMALISOM-CPLX   \n4    3-METHYL-2-OXOBUT-OHCH3XFER-CPLX   \n..                                ...   \n442                       UPPSYN-CPLX   \n443         URACIL-PRIBOSYLTRANS-CPLX   \n444                       URPHOS-CPLX   \n445                      XYLISOM-CPLX   \n446                        CPLX0-1382   \n\n                                              combined  \n0                                          {'MG+2': 1}  \n1    {'LIPOIC-ACID': 1, 'THIAMINE-PYROPHOSPHATE': 1...  \n2                               {'MG+2': 1, 'MN+2': 1}  \n3                                         {'CPD-7': 1}  \n4                                          {'MG+2': 1}  \n..                                                 ...  \n442                                        {'MG+2': 1}  \n443                                        {'MG+2': 1}  \n444                                          {'K+': 1}  \n445                             {'MG+2': 2, 'MN+2': 1}  \n446                                        {'MG+2': 1}  \n\n[447 rows x 2 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>combined</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>{'LIPOIC-ACID': 1, 'THIAMINE-PYROPHOSPHATE': 1...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>{'MG+2': 1, 'MN+2': 1}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>{'CPD-7': 1}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <td>UPPSYN-CPLX</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>URACIL-PRIBOSYLTRANS-CPLX</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>URPHOS-CPLX</td>\n      <td>{'K+': 1}</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>XYLISOM-CPLX</td>\n      <td>{'MG+2': 2, 'MN+2': 1}</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>CPLX0-1382</td>\n      <td>{'MG+2': 1}</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 2 columns</p>\n</div>"
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex_df = pd.read_csv('notebooks/fbagd/cofactors_new.csv', sep=',').fillna('None').loc[:, ['complex', 'combined']]\n",
+    "complex_df.combined = complex_df.combined.apply(ast.literal_eval)\n",
+    "\n",
+    "\n",
+    "complex_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:45:44.133846Z",
+     "start_time": "2023-06-22T16:45:44.121156Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-06-13'\n",
+    "experiment = 'fba-redux'\n",
+    "entry = f'{experiment}_{time}_{date}'\n",
+    "folder = f'out/fbagd/{entry}/'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:47:32.088129Z",
+     "start_time": "2023-06-22T16:47:32.081925Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [],
+   "source": [
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "# output = np.load(r\"out/geneRxnVerifData/output_glc.npy\", allow_pickle=True, encoding='ASCII').tolist()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:47:33.369506Z",
+     "start_time": "2023-06-22T16:47:33.328879Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "outputs": [],
+   "source": [
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:47:35.889210Z",
+     "start_time": "2023-06-22T16:47:33.820310Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "outputs": [],
+   "source": [
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry\n",
+    "bulk_ids = metabolism.bulk_ids"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:47:36.466907Z",
+     "start_time": "2023-06-22T16:47:36.458720Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "outputs": [],
+   "source": [
+    "bulk = pd.DataFrame(output['bulk'], columns=bulk_ids)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T16:47:37.266100Z",
+     "start_time": "2023-06-22T16:47:36.725500Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPLX0-7533[o]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name in bulk.keys():\n",
+    "    if 'CPLX0-7533' in name:\n",
+    "        print(name)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T17:12:24.632577Z",
+     "start_time": "2023-06-22T17:12:24.628917Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "outputs": [],
+   "source": [
+    "for complex_name in complex_df['complex']:\n",
+    "    bulk_name = complex_name + '[c]'\n",
+    "\n",
+    "    if bulk_name in bulk_ids:\n",
+    "        complex_df.loc[complex_df['complex'] == complex_name, 'counts'] = bulk.at[24, bulk_name]\n",
+    "\n",
+    "    elif bulk_name not in bulk_ids:\n",
+    "        for cpd in list(bulk_ids):\n",
+    "            if complex_name in cpd and len(cpd) < len(complex_name) + 4:\n",
+    "                bulk_name = cpd\n",
+    "                complex_df.loc[complex_df['complex'] == complex_name, 'counts'] = bulk.at[24, bulk_name]\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T19:13:36.412491Z",
+     "start_time": "2023-06-22T19:13:35.481669Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                    complex                      combined   counts\n359         ISOCITHASE-CPLX        {'MG+2': 1, 'MN+2': 1}  12315.0\n427   SUPEROX-DISMUTMN-CPLX                   {'MN+2': 1}  10096.0\n399          PHOSGLYCMUTASE  {'23-DIPHOSPHOGLYCERATE': 1}  10058.0\n305            ENOLASE-CPLX                   {'MG+2': 1}   6645.0\n18            ACSERLYA-CPLX    {'PYRIDOXAL_PHOSPHATE': 1}   6269.0\n..                      ...                           ...      ...\n307           ENTMULTI-CPLX                   {'MG+2': 1}      NaN\n321           GCVMULTI-CPLX            {'LIPOIC-ACID': 1}      NaN\n383               NQOR-CPLX                    {'FMN': 1}      NaN\n400  PHOSPHASERDECARB-DIMER              {'CPD0-2654': 1}      NaN\n420          SAMDECARB-CPLX   {'CPD0-2654': 1, 'MG+2': 1}      NaN\n\n[447 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>combined</th>\n      <th>counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>359</th>\n      <td>ISOCITHASE-CPLX</td>\n      <td>{'MG+2': 1, 'MN+2': 1}</td>\n      <td>12315.0</td>\n    </tr>\n    <tr>\n      <th>427</th>\n      <td>SUPEROX-DISMUTMN-CPLX</td>\n      <td>{'MN+2': 1}</td>\n      <td>10096.0</td>\n    </tr>\n    <tr>\n      <th>399</th>\n      <td>PHOSGLYCMUTASE</td>\n      <td>{'23-DIPHOSPHOGLYCERATE': 1}</td>\n      <td>10058.0</td>\n    </tr>\n    <tr>\n      <th>305</th>\n      <td>ENOLASE-CPLX</td>\n      <td>{'MG+2': 1}</td>\n      <td>6645.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>ACSERLYA-CPLX</td>\n      <td>{'PYRIDOXAL_PHOSPHATE': 1}</td>\n      <td>6269.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>307</th>\n      <td>ENTMULTI-CPLX</td>\n      <td>{'MG+2': 1}</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>321</th>\n      <td>GCVMULTI-CPLX</td>\n      <td>{'LIPOIC-ACID': 1}</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>383</th>\n      <td>NQOR-CPLX</td>\n      <td>{'FMN': 1}</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>400</th>\n      <td>PHOSPHASERDECARB-DIMER</td>\n      <td>{'CPD0-2654': 1}</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>420</th>\n      <td>SAMDECARB-CPLX</td>\n      <td>{'CPD0-2654': 1, 'MG+2': 1}</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex_df.sort_values(by='counts', ascending=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T19:15:31.092276Z",
+     "start_time": "2023-06-22T19:15:31.090307Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                              complex  \\\n0                               1-PFK   \n1               2OXOGLUTARATEDEH-CPLX   \n2         3-ISOPROPYLMALDEHYDROG-CPLX   \n3             3-ISOPROPYLMALISOM-CPLX   \n4    3-METHYL-2-OXOBUT-OHCH3XFER-CPLX   \n..                                ...   \n442                       UPPSYN-CPLX   \n443         URACIL-PRIBOSYLTRANS-CPLX   \n444                       URPHOS-CPLX   \n445                      XYLISOM-CPLX   \n446                        CPLX0-1382   \n\n                                             combined  counts  \n0                                              [MG+2]    31.0  \n1    [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]    86.0  \n2                                        [MG+2, MN+2]  1065.0  \n3                                             [CPD-7]  2631.0  \n4                                              [MG+2]   174.0  \n..                                                ...     ...  \n442                                            [MG+2]    56.0  \n443                                            [MG+2]  1068.0  \n444                                              [K+]   176.0  \n445                                [MG+2, MG+2, MN+2]     1.0  \n446                                            [MG+2]     0.0  \n\n[447 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>combined</th>\n      <th>counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>[MG+2]</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>86.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>[MG+2, MN+2]</td>\n      <td>1065.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>[CPD-7]</td>\n      <td>2631.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>[MG+2]</td>\n      <td>174.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <td>UPPSYN-CPLX</td>\n      <td>[MG+2]</td>\n      <td>56.0</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>URACIL-PRIBOSYLTRANS-CPLX</td>\n      <td>[MG+2]</td>\n      <td>1068.0</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>URPHOS-CPLX</td>\n      <td>[K+]</td>\n      <td>176.0</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>XYLISOM-CPLX</td>\n      <td>[MG+2, MG+2, MN+2]</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>CPLX0-1382</td>\n      <td>[MG+2]</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a new column in complex_df that is a list with each cofactor multiplied by its stoichiometry\n",
+    "\n",
+    "for i in range(len(complex_df.index)):\n",
+    "    cofactor_dict = complex_df.at[i, 'combined']\n",
+    "    cofactor_list = []\n",
+    "\n",
+    "    for cofactor in cofactor_dict:\n",
+    "        for j in range(cofactor_dict[cofactor]):\n",
+    "            cofactor_list.append(cofactor)\n",
+    "\n",
+    "    complex_df.at[i, 'combined'] = cofactor_list\n",
+    "\n",
+    "complex_df\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T19:20:47.535087Z",
+     "start_time": "2023-06-22T19:20:47.532120Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                   combined  counts\n0                      MG+2    31.0\n1               LIPOIC-ACID    86.0\n1    THIAMINE-PYROPHOSPHATE    86.0\n1                       FAD    86.0\n1                      MG+2    86.0\n..                      ...     ...\n443                    MG+2  1068.0\n444                      K+   176.0\n445                    MG+2     1.0\n445                    MN+2     1.0\n446                    MG+2     0.0\n\n[613 rows x 2 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>combined</th>\n      <th>counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>MG+2</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>LIPOIC-ACID</td>\n      <td>86.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>THIAMINE-PYROPHOSPHATE</td>\n      <td>86.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>FAD</td>\n      <td>86.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>MG+2</td>\n      <td>86.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>MG+2</td>\n      <td>1068.0</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>K+</td>\n      <td>176.0</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>MG+2</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>MN+2</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>MG+2</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>613 rows × 2 columns</p>\n</div>"
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cofactor_df = complex_df[['combined', 'counts']].explode('combined').groupby('combined').sum().sort_values(by='counts', ascending=False)\n",
+    "cofactor_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T19:14:54.615960Z",
+     "start_time": "2023-06-22T19:14:54.610137Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                        counts molecular_composition\ncofactors                                           \nMG+2                     55738                     0\nMN+2                     27211                     0\nPYRIDOXAL_PHOSPHATE      24413                     0\nZN+2                     12807                     0\n23-DIPHOSPHOGLYCERATE    10058                     0\nFAD                       8954                     0\nK+                        5157                     0\nCPD-7                     4556                     0\nFE+2                      3256                     0\nTHIAMINE-PYROPHOSPHATE    1868                     0\nNI+2                      1375                     0\nFMN                       1256                     0\nHeme-b                    1025                     0\nCU+2                       817                     0\nHEME_O                     814                     0\nNAD                        730                     0\nCPD-6                      635                     0\nHCO3                       426                     0\nCO+2                       353                     0\nNADP                       253                     0\n3FE-4S                     212                     0\nLIPOIC-ACID                180                     0\nFE+3                       161                     0\nCA+2                       155                     0\nPROTOHEME                  139                     0\nLYS                        116                     0\nFMNH2                      110                     0\nSIROHEME                    98                     0\nFeS-Centers                 76                     0\nAMMONIUM                    52                     0\nCPD-23429                   46                     0\nNADPH                       40                     0\nCPD-15873                   40                     0\nCPD-18260                   23                     0\nCPD-8123                    17                     0\nCPD0-1882                   15                     0\nHEME_D                      13                     0\nHEME_C                      13                     0\nADENOSYLCOBALAMIN            9                     0\nTOPAQUINONE                  3                     0\nCPD0-2654                    0                     0\nCPD-24862                    0                     0\nCPD-17649                    0                     0\nGTP                          0                     0",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>counts</th>\n      <th>molecular_composition</th>\n    </tr>\n    <tr>\n      <th>cofactors</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MG+2</th>\n      <td>55738</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>MN+2</th>\n      <td>27211</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>PYRIDOXAL_PHOSPHATE</th>\n      <td>24413</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>ZN+2</th>\n      <td>12807</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>23-DIPHOSPHOGLYCERATE</th>\n      <td>10058</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>FAD</th>\n      <td>8954</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>K+</th>\n      <td>5157</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-7</th>\n      <td>4556</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>FE+2</th>\n      <td>3256</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>THIAMINE-PYROPHOSPHATE</th>\n      <td>1868</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>NI+2</th>\n      <td>1375</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>FMN</th>\n      <td>1256</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Heme-b</th>\n      <td>1025</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CU+2</th>\n      <td>817</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>HEME_O</th>\n      <td>814</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>NAD</th>\n      <td>730</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-6</th>\n      <td>635</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>HCO3</th>\n      <td>426</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CO+2</th>\n      <td>353</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>NADP</th>\n      <td>253</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3FE-4S</th>\n      <td>212</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>LIPOIC-ACID</th>\n      <td>180</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>FE+3</th>\n      <td>161</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CA+2</th>\n      <td>155</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>PROTOHEME</th>\n      <td>139</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>LYS</th>\n      <td>116</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>FMNH2</th>\n      <td>110</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>SIROHEME</th>\n      <td>98</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>FeS-Centers</th>\n      <td>76</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>AMMONIUM</th>\n      <td>52</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-23429</th>\n      <td>46</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>NADPH</th>\n      <td>40</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-15873</th>\n      <td>40</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-18260</th>\n      <td>23</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-8123</th>\n      <td>17</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD0-1882</th>\n      <td>15</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>HEME_D</th>\n      <td>13</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>HEME_C</th>\n      <td>13</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>ADENOSYLCOBALAMIN</th>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>TOPAQUINONE</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD0-2654</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-24862</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CPD-17649</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>GTP</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# change name of index value FeS-Centers to CPD-7\n",
+    "\n",
+    "cofactor_df['molecular_composition'] = 0\n",
+    "cofactor_df['molecular_composition'] = cofactor_df['molecular_composition'].astype(object)\n",
+    "\n",
+    "cofactor_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:12:45.786557Z",
+     "start_time": "2023-06-14T21:12:45.782639Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No cml for FeS-Centers\n",
+      "No cml for CPD-17649\n"
+     ]
+    }
+   ],
+   "source": [
+    "for cofactor_name in cofactor_df.index:\n",
+    "    mol = cofactor_name\n",
+    "    mol = mol.replace('+', '%2b')\n",
+    "    mol_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{mol}&detail=low'\n",
+    "\n",
+    "    r = s.get(mol_str)\n",
+    "    o = xmltodict.parse(r.content)['ptools-xml']['Compound']\n",
+    "\n",
+    "    if 'cml' not in o:\n",
+    "        print(f'No cml for {cofactor_name}')\n",
+    "        cofactor_df.at[cofactor_name, 'molecular_composition'] = {}\n",
+    "        continue\n",
+    "    else:\n",
+    "        atom_array = o['cml']['molecule']['atomArray']['atom']\n",
+    "    #pp.pprint(o)\n",
+    "\n",
+    "    if type(atom_array) is dict:\n",
+    "        atom_array = [atom_array]\n",
+    "\n",
+    "    element_dict = {}\n",
+    "    # go through entries of atom array and add 1 to key of element_dict\n",
+    "    for atom in atom_array:\n",
+    "        element = atom['@elementType']\n",
+    "        if element in element_dict:\n",
+    "            element_dict[element] += 1\n",
+    "        else:\n",
+    "            element_dict[element] = 1\n",
+    "\n",
+    "    cofactor_df.at[cofactor_name, 'molecular_composition'] = element_dict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:12:56.625228Z",
+     "start_time": "2023-06-14T21:12:54.239650Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "outputs": [],
+   "source": [
+    "for cofactor_name in ['FeS-Centers', 'CPD-17649']:\n",
+    "    cofactor_df.at[cofactor_name, 'molecular_composition'] = {'FE': 4, 'S': 4}"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:12:58.850805Z",
+     "start_time": "2023-06-14T21:12:58.848417Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         MG   MN     C     N     O    P   ZN    K   FE    S  \\\ncofactors                                                                     \nMG+2                    1.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nMN+2                    0.0  1.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nPYRIDOXAL_PHOSPHATE     0.0  0.0   8.0   1.0   6.0  1.0  0.0  0.0  0.0  0.0   \nZN+2                    0.0  0.0   0.0   0.0   0.0  0.0  1.0  0.0  0.0  0.0   \n23-DIPHOSPHOGLYCERATE   0.0  0.0   3.0   0.0  10.0  2.0  0.0  0.0  0.0  0.0   \nFAD                     0.0  0.0  27.0   9.0  15.0  2.0  0.0  0.0  0.0  0.0   \nK+                      0.0  0.0   0.0   0.0   0.0  0.0  0.0  1.0  0.0  0.0   \nCPD-7                   0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  4.0  4.0   \nFE+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  1.0  0.0   \nTHIAMINE-PYROPHOSPHATE  0.0  0.0  12.0   4.0   7.0  2.0  0.0  0.0  0.0  1.0   \nNI+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nFMN                     0.0  0.0  17.0   4.0   9.0  1.0  0.0  0.0  0.0  0.0   \nHeme-b                  0.0  0.0  34.0   4.0   4.0  0.0  0.0  0.0  1.0  0.0   \nCU+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nHEME_O                  0.0  0.0  49.0   4.0   5.0  0.0  0.0  0.0  1.0  0.0   \nNAD                     0.0  0.0  21.0   7.0  14.0  2.0  0.0  0.0  0.0  0.0   \nCPD-6                   0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  2.0  6.0   \nHCO3                    0.0  0.0   1.0   0.0   3.0  0.0  0.0  0.0  0.0  0.0   \nCO+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nNADP                    0.0  0.0  21.0   7.0  17.0  3.0  0.0  0.0  0.0  0.0   \n3FE-4S                  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  3.0  4.0   \nLIPOIC-ACID             0.0  0.0   8.0   0.0   2.0  0.0  0.0  0.0  0.0  2.0   \nFE+3                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  1.0  0.0   \nCA+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nPROTOHEME               0.0  0.0  34.0   4.0   4.0  0.0  0.0  0.0  1.0  0.0   \nLYS                     0.0  0.0   6.0   2.0   2.0  0.0  0.0  0.0  0.0  0.0   \nFMNH2                   0.0  0.0  17.0   4.0   9.0  1.0  0.0  0.0  0.0  0.0   \nSIROHEME                0.0  0.0  42.0   4.0  16.0  0.0  0.0  0.0  1.0  0.0   \nFeS-Centers             0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  4.0  4.0   \nAMMONIUM                0.0  0.0   0.0   1.0   0.0  0.0  0.0  0.0  0.0  0.0   \nCPD-23429               0.0  0.0  34.0   4.0   6.0  0.0  0.0  0.0  1.0  0.0   \nNADPH                   0.0  0.0  21.0   7.0  17.0  3.0  0.0  0.0  0.0  0.0   \nCPD-15873               0.0  0.0  40.0  20.0  27.0  4.0  0.0  0.0  0.0  4.0   \nCPD-18260               0.0  0.0  22.0   4.0   9.0  1.0  0.0  0.0  0.0  0.0   \nCPD-8123                0.0  0.0  10.0   5.0   8.0  1.0  0.0  0.0  0.0  2.0   \nCPD0-1882               0.0  0.0  19.0   8.0  15.0  2.0  0.0  0.0  0.0  2.0   \nHEME_D                  0.0  0.0  34.0   4.0   6.0  0.0  0.0  0.0  1.0  0.0   \nHEME_C                  0.0  0.0  34.0   4.0   4.0  0.0  0.0  0.0  1.0  2.0   \nADENOSYLCOBALAMIN       0.0  0.0  72.0  18.0  17.0  1.0  0.0  0.0  0.0  0.0   \nTOPAQUINONE             0.0  0.0   9.0   1.0   4.0  0.0  0.0  0.0  0.0  0.0   \nCPD0-2654               0.0  0.0   3.0   0.0   2.0  0.0  0.0  0.0  0.0  0.0   \nCPD-24862               0.0  0.0   3.0   2.0   1.0  0.0  0.0  0.0  1.0  4.0   \nCPD-17649               0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  4.0  4.0   \nGTP                     0.0  0.0  10.0   5.0  14.0  3.0  0.0  0.0  0.0  0.0   \n\n                         NI   CU    R   CO   CA    H   MO  Proteins  \ncofactors                                                            \nMG+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nMN+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nPYRIDOXAL_PHOSPHATE     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nZN+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \n23-DIPHOSPHOGLYCERATE   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFAD                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nK+                      0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-7                   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFE+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nTHIAMINE-PYROPHOSPHATE  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nNI+2                    1.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFMN                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nHeme-b                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCU+2                    0.0  1.0  0.0  0.0  0.0  0.0  0.0       0.0  \nHEME_O                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nNAD                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-6                   0.0  0.0  4.0  0.0  0.0  0.0  0.0       0.0  \nHCO3                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCO+2                    0.0  0.0  0.0  1.0  0.0  0.0  0.0       0.0  \nNADP                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \n3FE-4S                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nLIPOIC-ACID             0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFE+3                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCA+2                    0.0  0.0  0.0  0.0  1.0  0.0  0.0       0.0  \nPROTOHEME               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nLYS                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFMNH2                   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nSIROHEME                0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFeS-Centers             0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nAMMONIUM                0.0  0.0  0.0  0.0  0.0  4.0  0.0       0.0  \nCPD-23429               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nNADPH                   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-15873               0.0  0.0  0.0  0.0  0.0  6.0  1.0       0.0  \nCPD-18260               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-8123                0.0  0.0  0.0  0.0  0.0  3.0  1.0       0.0  \nCPD0-1882               0.0  0.0  0.0  0.0  0.0  3.0  1.0       0.0  \nHEME_D                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nHEME_C                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nADENOSYLCOBALAMIN       0.0  0.0  0.0  1.0  0.0  0.0  0.0       0.0  \nTOPAQUINONE             0.0  0.0  0.0  0.0  0.0  0.0  0.0       2.0  \nCPD0-2654               0.0  0.0  0.0  0.0  0.0  0.0  0.0       1.0  \nCPD-24862               1.0  0.0  0.0  0.0  0.0  0.0  0.0       4.0  \nCPD-17649               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nGTP                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MG</th>\n      <th>MN</th>\n      <th>C</th>\n      <th>N</th>\n      <th>O</th>\n      <th>P</th>\n      <th>ZN</th>\n      <th>K</th>\n      <th>FE</th>\n      <th>S</th>\n      <th>NI</th>\n      <th>CU</th>\n      <th>R</th>\n      <th>CO</th>\n      <th>CA</th>\n      <th>H</th>\n      <th>MO</th>\n      <th>Proteins</th>\n    </tr>\n    <tr>\n      <th>cofactors</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MG+2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>MN+2</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>PYRIDOXAL_PHOSPHATE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ZN+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>23-DIPHOSPHOGLYCERATE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FAD</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>27.0</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>K+</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-7</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FE+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>THIAMINE-PYROPHOSPHATE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NI+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FMN</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>17.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Heme-b</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CU+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HEME_O</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NAD</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>7.0</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-6</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HCO3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CO+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NADP</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>7.0</td>\n      <td>17.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3FE-4S</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>LIPOIC-ACID</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FE+3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CA+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>PROTOHEME</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>LYS</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FMNH2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>17.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>SIROHEME</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>4.0</td>\n      <td>16.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FeS-Centers</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>AMMONIUM</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-23429</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NADPH</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>7.0</td>\n      <td>17.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-15873</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>20.0</td>\n      <td>27.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-18260</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>22.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-8123</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>5.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD0-1882</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>19.0</td>\n      <td>8.0</td>\n      <td>15.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HEME_D</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HEME_C</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ADENOSYLCOBALAMIN</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>72.0</td>\n      <td>18.0</td>\n      <td>17.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>TOPAQUINONE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>CPD0-2654</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>CPD-24862</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>CPD-17649</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>GTP</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>5.0</td>\n      <td>14.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "element_matrix = cofactor_df['molecular_composition'].apply(pd.Series).fillna(0)\n",
+    "element_matrix"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:12:59.340568Z",
+     "start_time": "2023-06-14T21:12:59.273206Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "outputs": [],
+   "source": [
+    "metals = ['MG', 'MN', 'ZN', 'FE', 'NI', 'CU', 'CO', 'CA', 'MO']\n",
+    "metal_distribution = pd.DataFrame(index=element_matrix.index, columns=metals).fillna(0)\n",
+    "for element in metals:\n",
+    "    metal_distribution.loc[:, element] = cofactor_df['counts'] * element_matrix.loc[:, element]\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:13:01.747717Z",
+     "start_time": "2023-06-14T21:13:01.743268Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "cofactors\nCPD-7                     18224.0\nFE+2                       3256.0\nCPD-6                      1270.0\nHeme-b                     1025.0\nHEME_O                      814.0\n3FE-4S                      636.0\nFeS-Centers                 304.0\nFE+3                        161.0\nPROTOHEME                   139.0\nSIROHEME                     98.0\nCPD-23429                    46.0\nHEME_D                       13.0\nHEME_C                       13.0\nNADPH                         0.0\nCPD-17649                     0.0\nAMMONIUM                      0.0\nCPD-24862                     0.0\nADENOSYLCOBALAMIN             0.0\nCPD-15873                     0.0\nCPD-18260                     0.0\nTOPAQUINONE                   0.0\nCPD-8123                      0.0\nFMNH2                         0.0\nCPD0-1882                     0.0\nCPD0-2654                     0.0\nMG+2                          0.0\nLYS                           0.0\nCA+2                          0.0\nPYRIDOXAL_PHOSPHATE           0.0\nZN+2                          0.0\n23-DIPHOSPHOGLYCERATE         0.0\nFAD                           0.0\nK+                            0.0\nTHIAMINE-PYROPHOSPHATE        0.0\nNI+2                          0.0\nFMN                           0.0\nCU+2                          0.0\nNAD                           0.0\nHCO3                          0.0\nCO+2                          0.0\nNADP                          0.0\nLIPOIC-ACID                   0.0\nMN+2                          0.0\nGTP                           0.0\nName: FE, dtype: float64"
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metal_distribution.sort_values(by='FE', ascending=False)['FE']"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:13:02.119465Z",
+     "start_time": "2023-06-14T21:13:02.115059Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Chain iron content back to enzymes"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                               complex  stoichiometry  \\\n0                                1-PFK              1   \n1                2OXOGLUTARATEDEH-CPLX              1   \n2          3-ISOPROPYLMALDEHYDROG-CPLX              1   \n3              3-ISOPROPYLMALISOM-CPLX              1   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX              1   \n...                                ...            ...   \n1096                        CPLX0-8053              1   \n1097                        CPLX0-8253              1   \n1098                          SRP-CPLX              1   \n1099                     CPLX0-7796APO              1   \n1100                         ARCB-CPLX              1   \n\n                                  reaction  \\\n0                                1-PFK_RXN   \n1                2OXOGLUTARATEDEH-CPLX_RXN   \n2          3-ISOPROPYLMALDEHYDROG-CPLX_RXN   \n3              3-ISOPROPYLMALISOM-CPLX_RXN   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-CPLX_RXN   \n...                                    ...   \n1096                        CPLX0-8053_RXN   \n1097                        CPLX0-8253_RXN   \n1098                          SRP-CPLX_RXN   \n1099                     CPLX0-7796APO_RXN   \n1100                         ARCB-CPLX_RXN   \n\n                         enzyme-reaction  \\\n0                  1PFRUCTPHOSPHN-ENZRXN   \n1                2OXOGLUTARATEDEH-ENZRXN   \n2          3-ISOPROPYLMALDEHYDROG-ENZRXN   \n3              3-ISOPROPYLMALISOM-ENZRXN   \n4     3-METHYL-2-OXOBUT-OHCH3XFER-ENZRXN   \n...                                  ...   \n1096                                None   \n1097                                None   \n1098                                None   \n1099                                None   \n1100                        ENZRXN0-8568   \n\n                                             cofactors  counts  \n0                                               [MG+2]      31  \n1     [LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]      86  \n2                                         [MG+2, MN+2]    1065  \n3                                              [CPD-7]    2631  \n4                                               [MG+2]     174  \n...                                                ...     ...  \n1096                                                []     208  \n1097                                                []       0  \n1098                                                []       0  \n1099                                                []      50  \n1100                                                []       1  \n\n[1101 rows x 6 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>stoichiometry</th>\n      <th>reaction</th>\n      <th>enzyme-reaction</th>\n      <th>cofactors</th>\n      <th>counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>1</td>\n      <td>1-PFK_RXN</td>\n      <td>1PFRUCTPHOSPHN-ENZRXN</td>\n      <td>[MG+2]</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>1</td>\n      <td>2OXOGLUTARATEDEH-CPLX_RXN</td>\n      <td>2OXOGLUTARATEDEH-ENZRXN</td>\n      <td>[LIPOIC-ACID, THIAMINE-PYROPHOSPHATE, FAD, MG+2]</td>\n      <td>86</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX</td>\n      <td>1</td>\n      <td>3-ISOPROPYLMALDEHYDROG-CPLX_RXN</td>\n      <td>3-ISOPROPYLMALDEHYDROG-ENZRXN</td>\n      <td>[MG+2, MN+2]</td>\n      <td>1065</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>1</td>\n      <td>3-ISOPROPYLMALISOM-CPLX_RXN</td>\n      <td>3-ISOPROPYLMALISOM-ENZRXN</td>\n      <td>[CPD-7]</td>\n      <td>2631</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX</td>\n      <td>1</td>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-CPLX_RXN</td>\n      <td>3-METHYL-2-OXOBUT-OHCH3XFER-ENZRXN</td>\n      <td>[MG+2]</td>\n      <td>174</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1096</th>\n      <td>CPLX0-8053</td>\n      <td>1</td>\n      <td>CPLX0-8053_RXN</td>\n      <td>None</td>\n      <td>[]</td>\n      <td>208</td>\n    </tr>\n    <tr>\n      <th>1097</th>\n      <td>CPLX0-8253</td>\n      <td>1</td>\n      <td>CPLX0-8253_RXN</td>\n      <td>None</td>\n      <td>[]</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1098</th>\n      <td>SRP-CPLX</td>\n      <td>1</td>\n      <td>SRP-CPLX_RXN</td>\n      <td>None</td>\n      <td>[]</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1099</th>\n      <td>CPLX0-7796APO</td>\n      <td>1</td>\n      <td>CPLX0-7796APO_RXN</td>\n      <td>None</td>\n      <td>[]</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>1100</th>\n      <td>ARCB-CPLX</td>\n      <td>1</td>\n      <td>ARCB-CPLX_RXN</td>\n      <td>ENZRXN0-8568</td>\n      <td>[]</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1101 rows × 6 columns</p>\n</div>"
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex_df.loc[:]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:13:05.545868Z",
+     "start_time": "2023-06-14T21:13:05.541941Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                     complex    cofactors  counts  fe_counts  fe_counts_norm\n7    3-ISOPROPYLMALISOM-CPLX        CPD-7    2631    10524.0       40.478480\n31                 ADHE-CPLX         FE+2      93       93.0        0.357706\n43     ANGLYC3PDEHYDROG-CPLX  FeS-Centers      69      276.0        1.061579\n48            APP-UBIOX-CPLX       HEME_D      13       13.0        0.050002\n49            APP-UBIOX-CPLX       Heme-b      13       13.0        0.050002\n..                       ...          ...     ...        ...             ...\n415      SULFITE-REDUCT-CPLX        CPD-7      93      372.0        1.430824\n418      SULFITE-REDUCT-CPLX     SIROHEME      93       93.0        0.357706\n419    SUPEROX-DISMUTFE-CPLX         FE+3     161      161.0        0.619255\n424          THREODEHYD-CPLX         FE+2      33       33.0        0.126928\n429         TMAOREDUCTI-CPLX       HEME_C       3        3.0        0.011539\n\n[65 rows x 5 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>cofactors</th>\n      <th>counts</th>\n      <th>fe_counts</th>\n      <th>fe_counts_norm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7</th>\n      <td>3-ISOPROPYLMALISOM-CPLX</td>\n      <td>CPD-7</td>\n      <td>2631</td>\n      <td>10524.0</td>\n      <td>40.478480</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>ADHE-CPLX</td>\n      <td>FE+2</td>\n      <td>93</td>\n      <td>93.0</td>\n      <td>0.357706</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>ANGLYC3PDEHYDROG-CPLX</td>\n      <td>FeS-Centers</td>\n      <td>69</td>\n      <td>276.0</td>\n      <td>1.061579</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>APP-UBIOX-CPLX</td>\n      <td>HEME_D</td>\n      <td>13</td>\n      <td>13.0</td>\n      <td>0.050002</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>APP-UBIOX-CPLX</td>\n      <td>Heme-b</td>\n      <td>13</td>\n      <td>13.0</td>\n      <td>0.050002</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>415</th>\n      <td>SULFITE-REDUCT-CPLX</td>\n      <td>CPD-7</td>\n      <td>93</td>\n      <td>372.0</td>\n      <td>1.430824</td>\n    </tr>\n    <tr>\n      <th>418</th>\n      <td>SULFITE-REDUCT-CPLX</td>\n      <td>SIROHEME</td>\n      <td>93</td>\n      <td>93.0</td>\n      <td>0.357706</td>\n    </tr>\n    <tr>\n      <th>419</th>\n      <td>SUPEROX-DISMUTFE-CPLX</td>\n      <td>FE+3</td>\n      <td>161</td>\n      <td>161.0</td>\n      <td>0.619255</td>\n    </tr>\n    <tr>\n      <th>424</th>\n      <td>THREODEHYD-CPLX</td>\n      <td>FE+2</td>\n      <td>33</td>\n      <td>33.0</td>\n      <td>0.126928</td>\n    </tr>\n    <tr>\n      <th>429</th>\n      <td>TMAOREDUCTI-CPLX</td>\n      <td>HEME_C</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>0.011539</td>\n    </tr>\n  </tbody>\n</table>\n<p>65 rows × 5 columns</p>\n</div>"
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# filter cofactors in element matrix that have FE coefficient > 0\n",
+    "fe_cofactors = list(element_matrix[element_matrix['FE'] > 0].index)\n",
+    "\n",
+    "# filter complex_df to only include complexes where one of the cofactors is in fe_cofactors\n",
+    "exploded_complexes = complex_df.explode('cofactors').dropna().reset_index(drop=True)\n",
+    "fe_complexes = exploded_complexes.loc[exploded_complexes.cofactors.isin(fe_cofactors), ['complex', 'cofactors', 'counts']]\n",
+    "fe_complexes\n",
+    "\n",
+    "# add column that multiplies counts by number of FE atoms in cofactor based on element matrix\n",
+    "fe_complexes['fe_counts'] = 0\n",
+    "for cofactor in fe_cofactors:\n",
+    "    fe_complexes.loc[fe_complexes['cofactors'] == cofactor, 'fe_counts'] = fe_complexes.loc[fe_complexes['cofactors'] == cofactor, 'counts'] * element_matrix.loc[cofactor, 'FE']\n",
+    "\n",
+    "# add a column that divides the fe_counts by the total sum of fe_counts\n",
+    "fe_complexes['fe_counts_norm'] = 100 * fe_complexes['fe_counts'] / fe_complexes['fe_counts'].sum()\n",
+    "\n",
+    "fe_complexes"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:37:22.090616Z",
+     "start_time": "2023-06-14T21:37:22.071682Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         MG   MN     C     N     O    P   ZN    K   FE    S  \\\ncofactors                                                                     \nMG+2                    1.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nMN+2                    0.0  1.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nPYRIDOXAL_PHOSPHATE     0.0  0.0   8.0   1.0   6.0  1.0  0.0  0.0  0.0  0.0   \nZN+2                    0.0  0.0   0.0   0.0   0.0  0.0  1.0  0.0  0.0  0.0   \n23-DIPHOSPHOGLYCERATE   0.0  0.0   3.0   0.0  10.0  2.0  0.0  0.0  0.0  0.0   \nFAD                     0.0  0.0  27.0   9.0  15.0  2.0  0.0  0.0  0.0  0.0   \nK+                      0.0  0.0   0.0   0.0   0.0  0.0  0.0  1.0  0.0  0.0   \nCPD-7                   0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  4.0  4.0   \nFE+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  1.0  0.0   \nTHIAMINE-PYROPHOSPHATE  0.0  0.0  12.0   4.0   7.0  2.0  0.0  0.0  0.0  1.0   \nNI+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nFMN                     0.0  0.0  17.0   4.0   9.0  1.0  0.0  0.0  0.0  0.0   \nHeme-b                  0.0  0.0  34.0   4.0   4.0  0.0  0.0  0.0  1.0  0.0   \nCU+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nHEME_O                  0.0  0.0  49.0   4.0   5.0  0.0  0.0  0.0  1.0  0.0   \nNAD                     0.0  0.0  21.0   7.0  14.0  2.0  0.0  0.0  0.0  0.0   \nCPD-6                   0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  2.0  6.0   \nHCO3                    0.0  0.0   1.0   0.0   3.0  0.0  0.0  0.0  0.0  0.0   \nCO+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nNADP                    0.0  0.0  21.0   7.0  17.0  3.0  0.0  0.0  0.0  0.0   \n3FE-4S                  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  3.0  4.0   \nLIPOIC-ACID             0.0  0.0   8.0   0.0   2.0  0.0  0.0  0.0  0.0  2.0   \nFE+3                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  1.0  0.0   \nCA+2                    0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0   \nPROTOHEME               0.0  0.0  34.0   4.0   4.0  0.0  0.0  0.0  1.0  0.0   \nLYS                     0.0  0.0   6.0   2.0   2.0  0.0  0.0  0.0  0.0  0.0   \nFMNH2                   0.0  0.0  17.0   4.0   9.0  1.0  0.0  0.0  0.0  0.0   \nSIROHEME                0.0  0.0  42.0   4.0  16.0  0.0  0.0  0.0  1.0  0.0   \nFeS-Centers             0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  4.0  4.0   \nAMMONIUM                0.0  0.0   0.0   1.0   0.0  0.0  0.0  0.0  0.0  0.0   \nCPD-23429               0.0  0.0  34.0   4.0   6.0  0.0  0.0  0.0  1.0  0.0   \nNADPH                   0.0  0.0  21.0   7.0  17.0  3.0  0.0  0.0  0.0  0.0   \nCPD-15873               0.0  0.0  40.0  20.0  27.0  4.0  0.0  0.0  0.0  4.0   \nCPD-18260               0.0  0.0  22.0   4.0   9.0  1.0  0.0  0.0  0.0  0.0   \nCPD-8123                0.0  0.0  10.0   5.0   8.0  1.0  0.0  0.0  0.0  2.0   \nCPD0-1882               0.0  0.0  19.0   8.0  15.0  2.0  0.0  0.0  0.0  2.0   \nHEME_D                  0.0  0.0  34.0   4.0   6.0  0.0  0.0  0.0  1.0  0.0   \nHEME_C                  0.0  0.0  34.0   4.0   4.0  0.0  0.0  0.0  1.0  2.0   \nADENOSYLCOBALAMIN       0.0  0.0  72.0  18.0  17.0  1.0  0.0  0.0  0.0  0.0   \nTOPAQUINONE             0.0  0.0   9.0   1.0   4.0  0.0  0.0  0.0  0.0  0.0   \nCPD0-2654               0.0  0.0   3.0   0.0   2.0  0.0  0.0  0.0  0.0  0.0   \nCPD-24862               0.0  0.0   3.0   2.0   1.0  0.0  0.0  0.0  1.0  4.0   \nCPD-17649               0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  4.0  4.0   \nGTP                     0.0  0.0  10.0   5.0  14.0  3.0  0.0  0.0  0.0  0.0   \n\n                         NI   CU    R   CO   CA    H   MO  Proteins  \ncofactors                                                            \nMG+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nMN+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nPYRIDOXAL_PHOSPHATE     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nZN+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \n23-DIPHOSPHOGLYCERATE   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFAD                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nK+                      0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-7                   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFE+2                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nTHIAMINE-PYROPHOSPHATE  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nNI+2                    1.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFMN                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nHeme-b                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCU+2                    0.0  1.0  0.0  0.0  0.0  0.0  0.0       0.0  \nHEME_O                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nNAD                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-6                   0.0  0.0  4.0  0.0  0.0  0.0  0.0       0.0  \nHCO3                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCO+2                    0.0  0.0  0.0  1.0  0.0  0.0  0.0       0.0  \nNADP                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \n3FE-4S                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nLIPOIC-ACID             0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFE+3                    0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCA+2                    0.0  0.0  0.0  0.0  1.0  0.0  0.0       0.0  \nPROTOHEME               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nLYS                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFMNH2                   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nSIROHEME                0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nFeS-Centers             0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nAMMONIUM                0.0  0.0  0.0  0.0  0.0  4.0  0.0       0.0  \nCPD-23429               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nNADPH                   0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-15873               0.0  0.0  0.0  0.0  0.0  6.0  1.0       0.0  \nCPD-18260               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nCPD-8123                0.0  0.0  0.0  0.0  0.0  3.0  1.0       0.0  \nCPD0-1882               0.0  0.0  0.0  0.0  0.0  3.0  1.0       0.0  \nHEME_D                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nHEME_C                  0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nADENOSYLCOBALAMIN       0.0  0.0  0.0  1.0  0.0  0.0  0.0       0.0  \nTOPAQUINONE             0.0  0.0  0.0  0.0  0.0  0.0  0.0       2.0  \nCPD0-2654               0.0  0.0  0.0  0.0  0.0  0.0  0.0       1.0  \nCPD-24862               1.0  0.0  0.0  0.0  0.0  0.0  0.0       4.0  \nCPD-17649               0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  \nGTP                     0.0  0.0  0.0  0.0  0.0  0.0  0.0       0.0  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MG</th>\n      <th>MN</th>\n      <th>C</th>\n      <th>N</th>\n      <th>O</th>\n      <th>P</th>\n      <th>ZN</th>\n      <th>K</th>\n      <th>FE</th>\n      <th>S</th>\n      <th>NI</th>\n      <th>CU</th>\n      <th>R</th>\n      <th>CO</th>\n      <th>CA</th>\n      <th>H</th>\n      <th>MO</th>\n      <th>Proteins</th>\n    </tr>\n    <tr>\n      <th>cofactors</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MG+2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>MN+2</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>PYRIDOXAL_PHOSPHATE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ZN+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>23-DIPHOSPHOGLYCERATE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FAD</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>27.0</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>K+</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-7</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FE+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>THIAMINE-PYROPHOSPHATE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NI+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FMN</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>17.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Heme-b</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CU+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HEME_O</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NAD</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>7.0</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-6</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HCO3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CO+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NADP</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>7.0</td>\n      <td>17.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3FE-4S</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>LIPOIC-ACID</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FE+3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CA+2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>PROTOHEME</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>LYS</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FMNH2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>17.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>SIROHEME</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>4.0</td>\n      <td>16.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>FeS-Centers</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>AMMONIUM</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-23429</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>NADPH</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>7.0</td>\n      <td>17.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-15873</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>20.0</td>\n      <td>27.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-18260</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>22.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD-8123</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>5.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>CPD0-1882</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>19.0</td>\n      <td>8.0</td>\n      <td>15.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HEME_D</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>HEME_C</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ADENOSYLCOBALAMIN</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>72.0</td>\n      <td>18.0</td>\n      <td>17.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>TOPAQUINONE</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>CPD0-2654</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>CPD-24862</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>CPD-17649</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>GTP</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>5.0</td>\n      <td>14.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "element_matrix"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-14T21:35:45.816464Z",
+     "start_time": "2023-06-14T21:35:45.813292Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/fbagd/metal_binding_new.ipynb b/notebooks/fbagd/metal_binding_new.ipynb
new file mode 100644
index 000000000..680c93486
--- /dev/null
+++ b/notebooks/fbagd/metal_binding_new.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:05:41.168972Z",
+     "start_time": "2023-06-26T19:05:40.887373Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import pprint\n",
+    "import ast\n",
+    "import re\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import requests\n",
+    "import xmltodict\n",
+    "\n",
+    "pp = pprint.PrettyPrinter(depth=6)\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/vivarium-ecoli'))\n",
+    "\n",
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                            id  \\\n0      1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER   \n1                                1-PFK-MONOMER   \n2             2-DEHYDROPANTOATE-REDUCT-MONOMER   \n3                 2-ISOPROPYLMALATESYN-MONOMER   \n4     2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER   \n...                                        ...   \n4415                              YTFR-MONOMER   \n4416                              YTFT-MONOMER   \n4417                              ZNUA-MONOMER   \n4418                              ZNUB-MONOMER   \n4419                              ZNUC-MONOMER   \n\n                                            common_name metal_features  \\\n0     1-acylglycerol-3-phosphate <i>O</i>-acyltransf...              0   \n1                                 1-phosphofructokinase              0   \n2                         2-dehydropantoate 2-reductase              0   \n3                            2-isopropylmalate synthase              0   \n4     bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...              0   \n...                                                 ...            ...   \n4415  galactofuranose ABC transporter putative ATP b...              0   \n4416  galactofuranose ABC transporter putative membr...              0   \n4417  Zn<sup>2+</sup> ABC transporter periplasmic bi...              0   \n4418   Zn<sup>2+</sup> ABC transporter membrane subunit              0   \n4419  Zn<sup>2+</sup> ABC transporter ATP binding su...              0   \n\n     other_features  \n0                 0  \n1                 0  \n2                 0  \n3                 0  \n4                 0  \n...             ...  \n4415              0  \n4416              0  \n4417              0  \n4418              0  \n4419              0  \n\n[4420 rows x 4 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>common_name</th>\n      <th>metal_features</th>\n      <th>other_features</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER</td>\n      <td>1-acylglycerol-3-phosphate &lt;i&gt;O&lt;/i&gt;-acyltransf...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1-PFK-MONOMER</td>\n      <td>1-phosphofructokinase</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2-DEHYDROPANTOATE-REDUCT-MONOMER</td>\n      <td>2-dehydropantoate 2-reductase</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2-ISOPROPYLMALATESYN-MONOMER</td>\n      <td>2-isopropylmalate synthase</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER</td>\n      <td>bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4415</th>\n      <td>YTFR-MONOMER</td>\n      <td>galactofuranose ABC transporter putative ATP b...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4416</th>\n      <td>YTFT-MONOMER</td>\n      <td>galactofuranose ABC transporter putative membr...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4417</th>\n      <td>ZNUA-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter periplasmic bi...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4418</th>\n      <td>ZNUB-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter membrane subunit</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4419</th>\n      <td>ZNUC-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter ATP binding su...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4420 rows × 4 columns</p>\n</div>"
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get a set of all monomers with an associated uniprot id\n",
+    "proteins_df = pd.read_csv('reconstruction/ecoli/flat/proteins.tsv', sep='\\t').loc[:, [\"id\", \"common_name\"]]\n",
+    "\n",
+    "proteins_df[\"metal_features\"] = 0\n",
+    "proteins_df[\"metal_features\"] = proteins_df[\"metal_features\"].astype(object)\n",
+    "\n",
+    "proteins_df[\"other_features\"] = 0\n",
+    "proteins_df[\"other_features\"] = proteins_df[\"other_features\"].astype(object)\n",
+    "\n",
+    "proteins_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:05:41.211718Z",
+     "start_time": "2023-06-26T19:05:41.171666Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'Feature': {'@ID': 'ECOLI:FTR0-1606133',\n",
+      "              '@detail': 'full',\n",
+      "              '@frameid': 'FTR0-1606133',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              'accession-1': {'#text': 'PF01266.27', '@datatype': 'string'},\n",
+      "              'evidence': {'Evidence-Code': {'@ID': 'ECOLI:EV-COMP-AINF',\n",
+      "                                             '@class': 'true',\n",
+      "                                             '@detail': 'full',\n",
+      "                                             '@frameid': 'EV-COMP-AINF',\n",
+      "                                             '@orgid': 'ECOLI',\n",
+      "                                             'comment': {'#text': 'Automated '\n",
+      "                                                                  'inference.  '\n",
+      "                                                                  'A computer '\n",
+      "                                                                  'inferred '\n",
+      "                                                                  'this '\n",
+      "                                                                  'assertion '\n",
+      "                                                                  'through one '\n",
+      "                                                                  'of many '\n",
+      "                                                                  'possible '\n",
+      "                                                                  'methods '\n",
+      "                                                                  'such as \\n'\n",
+      "                                                                  'sequence '\n",
+      "                                                                  'similarity, '\n",
+      "                                                                  'recognized '\n",
+      "                                                                  'motifs or '\n",
+      "                                                                  'consensus '\n",
+      "                                                                  'sequence, '\n",
+      "                                                                  'etc.  When '\n",
+      "                                                                  'a person '\n",
+      "                                                                  'made the '\n",
+      "                                                                  'inference \\n'\n",
+      "                                                                  'from '\n",
+      "                                                                  'computational '\n",
+      "                                                                  'evidence, '\n",
+      "                                                                  'use '\n",
+      "                                                                  'EV-COMP-HINF.',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                             'common-name': {'#text': 'Inferred '\n",
+      "                                                                      'computationally '\n",
+      "                                                                      'without '\n",
+      "                                                                      'human '\n",
+      "                                                                      'oversight',\n",
+      "                                                             '@datatype': 'string'},\n",
+      "                                             'parent': {'Evidence-Code': {'@class': 'true',\n",
+      "                                                                          '@frameid': 'EV-COMP',\n",
+      "                                                                          '@orgid': 'ECOLI',\n",
+      "                                                                          '@resource': 'getxml?ECOLI:EV-COMP'}}},\n",
+      "                           'Publication': {'@ID': 'ECOLI:PUB-26673716',\n",
+      "                                           '@detail': 'full',\n",
+      "                                           '@frameid': 'PUB-26673716',\n",
+      "                                           '@orgid': 'ECOLI',\n",
+      "                                           'author': [{...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...}],\n",
+      "                                           'pubmed-id': {'#text': '26673716',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                           'source': {'#text': 'Nucleic Acids '\n",
+      "                                                               'Res '\n",
+      "                                                               '44(D1);D279-85',\n",
+      "                                                      '@datatype': 'string'},\n",
+      "                                           'title': {'#text': 'The Pfam '\n",
+      "                                                              'protein '\n",
+      "                                                              'families '\n",
+      "                                                              'database: '\n",
+      "                                                              'towards a more '\n",
+      "                                                              'sustainable '\n",
+      "                                                              'future.',\n",
+      "                                                     '@datatype': 'string'},\n",
+      "                                           'year': {'#text': '2016',\n",
+      "                                                    '@datatype': 'integer'}}},\n",
+      "              'feature-of': {'Protein': {'@frameid': 'DALADEHYDROGA-MONOMER',\n",
+      "                                         '@orgid': 'ECOLI',\n",
+      "                                         '@resource': '#ECOLI:DALADEHYDROGA-MONOMER'}},\n",
+      "              'left-end-position': {'#text': '2', '@datatype': 'integer'},\n",
+      "              'parent': {'Feature': {'@class': 'true',\n",
+      "                                     '@frameid': 'Pfam-Features',\n",
+      "                                     '@orgid': 'ECOLI',\n",
+      "                                     '@resource': 'getxml?ECOLI:Pfam-Features'}},\n",
+      "              'right-end-position': {'#text': '397', '@datatype': 'integer'}}},\n",
+      " {'Feature': {'@ID': 'ECOLI:FTR0-1586643',\n",
+      "              '@detail': 'full',\n",
+      "              '@frameid': 'FTR0-1586643',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              'attached-group': {'Compound': {'@frameid': 'FAD',\n",
+      "                                              '@orgid': 'ECOLI',\n",
+      "                                              '@resource': 'getxml?ECOLI:FAD'}},\n",
+      "              'comment': {'#text': 'UniProt: FAD.', '@datatype': 'string'},\n",
+      "              'evidence': {'Evidence-Code': {'@ID': 'ECOLI:EV-COMP-HINF-ISM',\n",
+      "                                             '@class': 'true',\n",
+      "                                             '@detail': 'full',\n",
+      "                                             '@frameid': 'EV-COMP-HINF-ISM',\n",
+      "                                             '@orgid': 'ECOLI',\n",
+      "                                             'comment': {'#text': 'Human '\n",
+      "                                                                  'inference '\n",
+      "                                                                  'based on '\n",
+      "                                                                  'evidence '\n",
+      "                                                                  'from some '\n",
+      "                                                                  'kind of '\n",
+      "                                                                  'statistical '\n",
+      "                                                                  'model of a '\n",
+      "                                                                  'sequence\\n'\n",
+      "                                                                  'or group of '\n",
+      "                                                                  'sequences, '\n",
+      "                                                                  'which is '\n",
+      "                                                                  'used to '\n",
+      "                                                                  'make a '\n",
+      "                                                                  'prediction '\n",
+      "                                                                  'about the '\n",
+      "                                                                  'function of '\n",
+      "                                                                  'a protein '\n",
+      "                                                                  'or RNA.\\n'\n",
+      "                                                                  '<p>\\n'\n",
+      "                                                                  '- '\n",
+      "                                                                  'Prediction '\n",
+      "                                                                  'methods for '\n",
+      "                                                                  'non-coding '\n",
+      "                                                                  'RNA genes '\n",
+      "                                                                  'such as '\n",
+      "                                                                  'tRNASCAN-SE, '\n",
+      "                                                                  'Snoscan, '\n",
+      "                                                                  'and Rfam\\n'\n",
+      "                                                                  '<p>\\n'\n",
+      "                                                                  '- Predicted '\n",
+      "                                                                  'presence of '\n",
+      "                                                                  'recognized '\n",
+      "                                                                  'functional '\n",
+      "                                                                  'domains or '\n",
+      "                                                                  'membership '\n",
+      "                                                                  'in protein '\n",
+      "                                                                  'families,\\n'\n",
+      "                                                                  '  as '\n",
+      "                                                                  'determined '\n",
+      "                                                                  'by tools '\n",
+      "                                                                  'such as '\n",
+      "                                                                  'profile '\n",
+      "                                                                  'Hidden '\n",
+      "                                                                  'Markov '\n",
+      "                                                                  'Models '\n",
+      "                                                                  '(HMMs), '\n",
+      "                                                                  'including '\n",
+      "                                                                  'Pfam and '\n",
+      "                                                                  'TIGRFAM\\n'\n",
+      "                                                                  '<p>\\n'\n",
+      "                                                                  '- Predicted '\n",
+      "                                                                  'protein '\n",
+      "                                                                  'features '\n",
+      "                                                                  'using tools '\n",
+      "                                                                  'such as '\n",
+      "                                                                  'TMHMM '\n",
+      "                                                                  '(transmembrane '\n",
+      "                                                                  'regions), '\n",
+      "                                                                  'SignalP '\n",
+      "                                                                  '(signal '\n",
+      "                                                                  'peptides on '\n",
+      "                                                                  'secreted '\n",
+      "                                                                  'proteins),\\n'\n",
+      "                                                                  '  and '\n",
+      "                                                                  'TargetP '\n",
+      "                                                                  '(subcellular '\n",
+      "                                                                  'localization)\\n'\n",
+      "                                                                  '<p>\\n'\n",
+      "                                                                  '- any other '\n",
+      "                                                                  'kind of '\n",
+      "                                                                  'domain '\n",
+      "                                                                  'modeling '\n",
+      "                                                                  'tool or '\n",
+      "                                                                  'collections '\n",
+      "                                                                  'of them '\n",
+      "                                                                  'such as '\n",
+      "                                                                  'SMART, '\n",
+      "                                                                  'PROSITE, '\n",
+      "                                                                  'PANTHER, '\n",
+      "                                                                  'InterPro, '\n",
+      "                                                                  'etc.\\n'\n",
+      "                                                                  '<p>\\n'\n",
+      "                                                                  '- An entry '\n",
+      "                                                                  'in the with '\n",
+      "                                                                  'field of a '\n",
+      "                                                                  'GO '\n",
+      "                                                                  'annotation '\n",
+      "                                                                  'is required '\n",
+      "                                                                  'when the '\n",
+      "                                                                  'model used '\n",
+      "                                                                  'is an '\n",
+      "                                                                  'object with '\n",
+      "                                                                  'an '\n",
+      "                                                                  'accession '\n",
+      "                                                                  'number\\n'\n",
+      "                                                                  '  (as found '\n",
+      "                                                                  'with Pfam, '\n",
+      "                                                                  'TIGRFAM, '\n",
+      "                                                                  'InterPro, '\n",
+      "                                                                  'PROSITE, '\n",
+      "                                                                  'Rfam, '\n",
+      "                                                                  'etc.)  The '\n",
+      "                                                                  'with field '\n",
+      "                                                                  'may be left '\n",
+      "                                                                  'blank for '\n",
+      "                                                                  'tools such '\n",
+      "                                                                  'as\\n'\n",
+      "                                                                  '  tRNAscan '\n",
+      "                                                                  'and Snoscan '\n",
+      "                                                                  'where there '\n",
+      "                                                                  'is not an '\n",
+      "                                                                  'object with '\n",
+      "                                                                  'an '\n",
+      "                                                                  'accession '\n",
+      "                                                                  'to point '\n",
+      "                                                                  'to.',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                             'common-name': {'#text': 'Inferred '\n",
+      "                                                                      'from '\n",
+      "                                                                      'Sequence '\n",
+      "                                                                      'Model',\n",
+      "                                                             '@datatype': 'string'},\n",
+      "                                             'parent': {'Evidence-Code': {'@class': 'true',\n",
+      "                                                                          '@frameid': 'EV-COMP-HINF-FN-FROM-SEQ',\n",
+      "                                                                          '@orgid': 'ECOLI',\n",
+      "                                                                          '@resource': 'getxml?ECOLI:EV-COMP-HINF-FN-FROM-SEQ'}}},\n",
+      "                           'Publication': {'@ID': 'ECOLI:PUB-UNIPROT-2021-03',\n",
+      "                                           '@detail': 'full',\n",
+      "                                           '@frameid': 'PUB-UNIPROT-2021-03',\n",
+      "                                           '@orgid': 'ECOLI',\n",
+      "                                           'author': {'#text': 'UniProt '\n",
+      "                                                               'Consortium',\n",
+      "                                                      '@datatype': 'string'},\n",
+      "                                           'source': {'#text': 'Database',\n",
+      "                                                      '@datatype': 'string'},\n",
+      "                                           'title': {'#text': 'UniProt version '\n",
+      "                                                              '2021-03 '\n",
+      "                                                              'released on '\n",
+      "                                                              '2021-06-16',\n",
+      "                                                     '@datatype': 'string'},\n",
+      "                                           'year': {'#text': '2021',\n",
+      "                                                    '@datatype': 'integer'}}},\n",
+      "              'feature-of': {'Protein': {'@frameid': 'DALADEHYDROGA-MONOMER',\n",
+      "                                         '@orgid': 'ECOLI',\n",
+      "                                         '@resource': '#ECOLI:DALADEHYDROGA-MONOMER'}},\n",
+      "              'left-end-position': {'#text': '3', '@datatype': 'integer'},\n",
+      "              'parent': {'Feature': {'@class': 'true',\n",
+      "                                     '@frameid': 'Nucleotide-Phosphate-Binding-Regions',\n",
+      "                                     '@orgid': 'ECOLI',\n",
+      "                                     '@resource': 'getxml?ECOLI:Nucleotide-Phosphate-Binding-Regions'}},\n",
+      "              'right-end-position': {'#text': '17', '@datatype': 'integer'}}},\n",
+      " {'Feature': {'@ID': 'ECOLI:FTR0-1552175',\n",
+      "              '@detail': 'full',\n",
+      "              '@frameid': 'FTR0-1552175',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              'comment': {'#text': 'N6-acetyllysine site upregulated '\n",
+      "                                   '(log<SUB>2</SUB>(fold-change) =  2.41) by '\n",
+      "                                   'YfiQ; digests by trypsin',\n",
+      "                          '@datatype': 'string'},\n",
+      "              'evidence': {'Evidence-Code': {'@ID': 'ECOLI:EV-EXP',\n",
+      "                                             '@class': 'true',\n",
+      "                                             '@detail': 'full',\n",
+      "                                             '@frameid': 'EV-EXP',\n",
+      "                                             '@orgid': 'ECOLI',\n",
+      "                                             'comment': {'#text': 'Inferred '\n",
+      "                                                                  'from '\n",
+      "                                                                  'experiment.  '\n",
+      "                                                                  'The '\n",
+      "                                                                  'evidence '\n",
+      "                                                                  'for an '\n",
+      "                                                                  'assertion '\n",
+      "                                                                  'comes from '\n",
+      "                                                                  'a\\n'\n",
+      "                                                                  'wet-lab '\n",
+      "                                                                  'experiment '\n",
+      "                                                                  'of some '\n",
+      "                                                                  'type.',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                             'common-name': {'#text': 'Inferred '\n",
+      "                                                                      'from '\n",
+      "                                                                      'experiment',\n",
+      "                                                             '@datatype': 'string'}},\n",
+      "                           'Publication': {'@ID': 'ECOLI:PUB-30352934',\n",
+      "                                           '@detail': 'full',\n",
+      "                                           '@frameid': 'PUB-30352934',\n",
+      "                                           '@orgid': 'ECOLI',\n",
+      "                                           'author': [{...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...},\n",
+      "                                                      {...}],\n",
+      "                                           'pubmed-id': {'#text': '30352934',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                           'source': {'#text': 'MBio 9(5)',\n",
+      "                                                      '@datatype': 'string'},\n",
+      "                                           'title': {'#text': 'Identification '\n",
+      "                                                              'of Novel '\n",
+      "                                                              'Protein Lysine '\n",
+      "                                                              'Acetyltransferases '\n",
+      "                                                              'in Escherichia '\n",
+      "                                                              'coli.',\n",
+      "                                                     '@datatype': 'string'},\n",
+      "                                           'year': {'#text': '2018',\n",
+      "                                                    '@datatype': 'integer'}}},\n",
+      "              'feature-of': {'Protein': {'@frameid': 'DALADEHYDROGA-MONOMER',\n",
+      "                                         '@orgid': 'ECOLI',\n",
+      "                                         '@resource': '#ECOLI:DALADEHYDROGA-MONOMER'}},\n",
+      "              'parent': {'Feature': {'@class': 'true',\n",
+      "                                     '@frameid': 'N6-acetyllysine-Modifications',\n",
+      "                                     '@orgid': 'ECOLI',\n",
+      "                                     '@resource': 'getxml?ECOLI:N6-acetyllysine-Modifications'}},\n",
+      "              'residue-number': {'#text': '217', '@datatype': 'integer'}}},\n",
+      " {'Feature': {'@ID': 'ECOLI:FTR0-1551738',\n",
+      "              '@detail': 'full',\n",
+      "              '@frameid': 'FTR0-1551738',\n",
+      "              '@orgid': 'ECOLI',\n",
+      "              'comment': {'#text': 'N6-acetyllysine site upregulated '\n",
+      "                                   '(log<SUB>2</SUB>(fold-change) =  3.61) by '\n",
+      "                                   'YiaC; digests by trypsin',\n",
+      "                          '@datatype': 'string'},\n",
+      "              'evidence': {'Evidence-Code': {'@class': 'true',\n",
+      "                                             '@frameid': 'EV-EXP',\n",
+      "                                             '@orgid': 'ECOLI',\n",
+      "                                             '@resource': '#ECOLI:EV-EXP'},\n",
+      "                           'Publication': {'@frameid': 'PUB-30352934',\n",
+      "                                           '@orgid': 'ECOLI',\n",
+      "                                           '@resource': '#ECOLI:PUB-30352934'}},\n",
+      "              'feature-of': {'Protein': {'@frameid': 'DALADEHYDROGA-MONOMER',\n",
+      "                                         '@orgid': 'ECOLI',\n",
+      "                                         '@resource': '#ECOLI:DALADEHYDROGA-MONOMER'}},\n",
+      "              'parent': {'Feature': {'@class': 'true',\n",
+      "                                     '@frameid': 'N6-acetyllysine-Modifications',\n",
+      "                                     '@orgid': 'ECOLI',\n",
+      "                                     '@resource': 'getxml?ECOLI:N6-acetyllysine-Modifications'}},\n",
+      "              'residue-number': {'#text': '66', '@datatype': 'integer'}}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "protein = 'DALADEHYDROGA-MONOMER'\n",
+    "\n",
+    "req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{protein}&detail=high'\n",
+    "\n",
+    "r = s.get(req_str)\n",
+    "\n",
+    "o = xmltodict.parse(r.content)['ptools-xml']\n",
+    "pp.pprint(o['Protein']['has-feature'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:05:41.456330Z",
+     "start_time": "2023-06-26T19:05:41.282145Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(50): # range(len(proteins_df.index)):\n",
+    "    if i % 100 == 0:\n",
+    "        print(i)\n",
+    "\n",
+    "    protein = proteins_df.loc[i, 'id']\n",
+    "\n",
+    "    req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{protein}&detail=high'\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    if r.status_code != 200:\n",
+    "        print(protein, r.status_code)\n",
+    "        proteins_df.loc[i, 'cofactors'] = 'NO ECOCYC ENTRY'\n",
+    "        continue\n",
+    "\n",
+    "    o = xmltodict.parse(r.content)['ptools-xml']\n",
+    "\n",
+    "    metal_set = set()\n",
+    "    other_feature_set = set()\n",
+    "    if 'Protein' in o and 'has-feature' in o['Protein']:\n",
+    "        features = o['Protein']['has-feature']\n",
+    "\n",
+    "        if type(features) is dict:\n",
+    "            features = [features]\n",
+    "\n",
+    "        for feature in features:\n",
+    "            if 'parent' not in feature['Feature']:\n",
+    "                continue\n",
+    "\n",
+    "            category = feature['Feature']['parent']['Feature']['@frameid']\n",
+    "            if category == 'Metal-Binding-Sites' and 'comment' in feature['Feature']:\n",
+    "\n",
+    "                # Detect match to any of the allowed metal names and allowed cofactor names and add to list\n",
+    "                comment = feature['Feature']['comment']['#text']\n",
+    "                metal_set.add(comment)\n",
+    "\n",
+    "                proteins_df.at[i, 'metal_features'] = list(metal_set)\n",
+    "\n",
+    "            if category == 'Nucleotide-Phosphate-Binding-Regions' and 'attached-group' in feature['Feature']:\n",
+    "                attached_group = feature['Feature']['attached-group']['Compound']['@frameid']\n",
+    "                other_feature_set.add(attached_group)\n",
+    "\n",
+    "            if category == 'N6-pyridoxal-phosphate-Lys-Modification':\n",
+    "                other_feature_set.add('PYRIDOXAL_PHOSPHATE')\n",
+    "\n",
+    "            proteins_df.at[i, 'metal_features'] = list(metal_set)\n",
+    "            proteins_df.at[i, 'other_features'] = list(other_feature_set)\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:05:53.090807Z",
+     "start_time": "2023-06-26T19:05:41.481717Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                            id  \\\n0      1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER   \n1                                1-PFK-MONOMER   \n2             2-DEHYDROPANTOATE-REDUCT-MONOMER   \n3                 2-ISOPROPYLMALATESYN-MONOMER   \n4     2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER   \n...                                        ...   \n4415                              YTFR-MONOMER   \n4416                              YTFT-MONOMER   \n4417                              ZNUA-MONOMER   \n4418                              ZNUB-MONOMER   \n4419                              ZNUC-MONOMER   \n\n                                            common_name metal_features  \\\n0     1-acylglycerol-3-phosphate <i>O</i>-acyltransf...             []   \n1                                 1-phosphofructokinase             []   \n2                         2-dehydropantoate 2-reductase             []   \n3                            2-isopropylmalate synthase             []   \n4     bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...             []   \n...                                                 ...            ...   \n4415  galactofuranose ABC transporter putative ATP b...              0   \n4416  galactofuranose ABC transporter putative membr...              0   \n4417  Zn<sup>2+</sup> ABC transporter periplasmic bi...              0   \n4418   Zn<sup>2+</sup> ABC transporter membrane subunit              0   \n4419  Zn<sup>2+</sup> ABC transporter ATP binding su...              0   \n\n     other_features  \n0                []  \n1             [ATP]  \n2            [NADP]  \n3                []  \n4                []  \n...             ...  \n4415              0  \n4416              0  \n4417              0  \n4418              0  \n4419              0  \n\n[4420 rows x 4 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>common_name</th>\n      <th>metal_features</th>\n      <th>other_features</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER</td>\n      <td>1-acylglycerol-3-phosphate &lt;i&gt;O&lt;/i&gt;-acyltransf...</td>\n      <td>[]</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1-PFK-MONOMER</td>\n      <td>1-phosphofructokinase</td>\n      <td>[]</td>\n      <td>[ATP]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2-DEHYDROPANTOATE-REDUCT-MONOMER</td>\n      <td>2-dehydropantoate 2-reductase</td>\n      <td>[]</td>\n      <td>[NADP]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2-ISOPROPYLMALATESYN-MONOMER</td>\n      <td>2-isopropylmalate synthase</td>\n      <td>[]</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER</td>\n      <td>bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...</td>\n      <td>[]</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4415</th>\n      <td>YTFR-MONOMER</td>\n      <td>galactofuranose ABC transporter putative ATP b...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4416</th>\n      <td>YTFT-MONOMER</td>\n      <td>galactofuranose ABC transporter putative membr...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4417</th>\n      <td>ZNUA-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter periplasmic bi...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4418</th>\n      <td>ZNUB-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter membrane subunit</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4419</th>\n      <td>ZNUC-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter ATP binding su...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4420 rows × 4 columns</p>\n</div>"
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "proteins_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:05:53.098486Z",
+     "start_time": "2023-06-26T19:05:53.095575Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:05:53.099951Z",
+     "start_time": "2023-06-26T19:05:53.098114Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/fbagd/metal_binding_uniprot.ipynb b/notebooks/fbagd/metal_binding_uniprot.ipynb
new file mode 100644
index 000000000..0f82361de
--- /dev/null
+++ b/notebooks/fbagd/metal_binding_uniprot.ipynb
@@ -0,0 +1,2253 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import pprint\n",
+    "import ast\n",
+    "import re\n",
+    "pp = pprint.PrettyPrinter(depth=6)\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import requests\n",
+    "import xmltodict\n",
+    "\n",
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T17:02:08.806238Z",
+     "start_time": "2023-06-23T17:02:08.524729Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                    complex  stoichiometry                   reaction\n0                     1-PFK              1                  1-PFK_RXN\n1             1-PFK-MONOMER             -2                  1-PFK_RXN\n2     2OXOGLUTARATEDEH-CPLX              1  2OXOGLUTARATEDEH-CPLX_RXN\n3                       E1O             -1  2OXOGLUTARATEDEH-CPLX_RXN\n4                       E2O             -1  2OXOGLUTARATEDEH-CPLX_RXN\n...                     ...            ...                        ...\n2811                FFS-RNA             -1               SRP-CPLX_RXN\n2812          CPLX0-7796APO              1          CPLX0-7796APO_RXN\n2813                PD04032             -2          CPLX0-7796APO_RXN\n2814              ARCB-CPLX              1              ARCB-CPLX_RXN\n2815           ARCB-MONOMER             -2              ARCB-CPLX_RXN\n\n[2816 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>complex</th>\n      <th>stoichiometry</th>\n      <th>reaction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-PFK</td>\n      <td>1</td>\n      <td>1-PFK_RXN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1-PFK-MONOMER</td>\n      <td>-2</td>\n      <td>1-PFK_RXN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2OXOGLUTARATEDEH-CPLX</td>\n      <td>1</td>\n      <td>2OXOGLUTARATEDEH-CPLX_RXN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>E1O</td>\n      <td>-1</td>\n      <td>2OXOGLUTARATEDEH-CPLX_RXN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>E2O</td>\n      <td>-1</td>\n      <td>2OXOGLUTARATEDEH-CPLX_RXN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2811</th>\n      <td>FFS-RNA</td>\n      <td>-1</td>\n      <td>SRP-CPLX_RXN</td>\n    </tr>\n    <tr>\n      <th>2812</th>\n      <td>CPLX0-7796APO</td>\n      <td>1</td>\n      <td>CPLX0-7796APO_RXN</td>\n    </tr>\n    <tr>\n      <th>2813</th>\n      <td>PD04032</td>\n      <td>-2</td>\n      <td>CPLX0-7796APO_RXN</td>\n    </tr>\n    <tr>\n      <th>2814</th>\n      <td>ARCB-CPLX</td>\n      <td>1</td>\n      <td>ARCB-CPLX_RXN</td>\n    </tr>\n    <tr>\n      <th>2815</th>\n      <td>ARCB-MONOMER</td>\n      <td>-2</td>\n      <td>ARCB-CPLX_RXN</td>\n    </tr>\n  </tbody>\n</table>\n<p>2816 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complexation_rxn_df = pd.read_csv('reconstruction/ecoli/flat/complexation_reactions.tsv', sep='\\t')\n",
+    "stoich_series = complexation_rxn_df.loc[:,['id', 'stoichiometry']].dropna().reset_index(drop=True)\n",
+    "\n",
+    "stoich_list = []\n",
+    "for i, stoich in enumerate(stoich_series.loc[:, 'stoichiometry']):\n",
+    "\n",
+    "    stoich = stoich.replace('null', '-1')\n",
+    "\n",
+    "    stoich = ast.literal_eval(stoich)\n",
+    "    for k,v in stoich.items():\n",
+    "        stoich_list.append([k, v, stoich_series.loc[i, 'id']])\n",
+    "\n",
+    "pre_complex_df = pd.DataFrame(stoich_list, columns=['complex', 'stoichiometry', 'reaction'])\n",
+    "pre_complex_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T17:02:10.131533Z",
+     "start_time": "2023-06-23T17:02:10.091418Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                            id  \\\n0      1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER   \n1                                1-PFK-MONOMER   \n2             2-DEHYDROPANTOATE-REDUCT-MONOMER   \n3                 2-ISOPROPYLMALATESYN-MONOMER   \n4     2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER   \n...                                        ...   \n4415                              YTFR-MONOMER   \n4416                              YTFT-MONOMER   \n4417                              ZNUA-MONOMER   \n4418                              ZNUB-MONOMER   \n4419                              ZNUC-MONOMER   \n\n                                            common_name uniprot_id  \n0     1-acylglycerol-3-phosphate <i>O</i>-acyltransf...             \n1                                 1-phosphofructokinase             \n2                         2-dehydropantoate 2-reductase             \n3                            2-isopropylmalate synthase             \n4     bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...             \n...                                                 ...        ...  \n4415  galactofuranose ABC transporter putative ATP b...             \n4416  galactofuranose ABC transporter putative membr...             \n4417  Zn<sup>2+</sup> ABC transporter periplasmic bi...             \n4418   Zn<sup>2+</sup> ABC transporter membrane subunit             \n4419  Zn<sup>2+</sup> ABC transporter ATP binding su...             \n\n[4420 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>common_name</th>\n      <th>uniprot_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER</td>\n      <td>1-acylglycerol-3-phosphate &lt;i&gt;O&lt;/i&gt;-acyltransf...</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1-PFK-MONOMER</td>\n      <td>1-phosphofructokinase</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2-DEHYDROPANTOATE-REDUCT-MONOMER</td>\n      <td>2-dehydropantoate 2-reductase</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2-ISOPROPYLMALATESYN-MONOMER</td>\n      <td>2-isopropylmalate synthase</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER</td>\n      <td>bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4415</th>\n      <td>YTFR-MONOMER</td>\n      <td>galactofuranose ABC transporter putative ATP b...</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4416</th>\n      <td>YTFT-MONOMER</td>\n      <td>galactofuranose ABC transporter putative membr...</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4417</th>\n      <td>ZNUA-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter periplasmic bi...</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4418</th>\n      <td>ZNUB-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter membrane subunit</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4419</th>\n      <td>ZNUC-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter ATP binding su...</td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p>4420 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get a set of all monomers with an associated uniprot id\n",
+    "proteins_df = pd.read_csv('reconstruction/ecoli/flat/proteins.tsv', sep='\\t').loc[:, [\"id\", \"common_name\"]]\n",
+    "proteins_df[\"uniprot_id\"] = \"\"\n",
+    "\n",
+    "proteins_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T17:02:10.663746Z",
+     "start_time": "2023-06-23T17:02:10.639437Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "no protein for AGAA-MONOMER\n",
+      "100\n",
+      "200\n",
+      "300\n",
+      "no protein for EG10010-MONOMER\n",
+      "400\n",
+      "500\n",
+      "no protein for EG10659-MONOMER\n",
+      "600\n",
+      "700\n",
+      "no protein for EG11092-MONOMER\n",
+      "800\n",
+      "900\n",
+      "no protein for EG11304-MONOMER\n",
+      "no protein for EG11386-MONOMER\n",
+      "1000\n",
+      "no protein for EG11496-MONOMER\n",
+      "1100\n",
+      "EG11708-MONOMER 404\n",
+      "no protein for EG11766-MONOMER\n",
+      "no protein for EG11770-MONOMER\n",
+      "1200\n",
+      "no protein for EG11778-MONOMER\n",
+      "no protein for EG11780-MONOMER\n",
+      "1300\n",
+      "no protein for EG11927-MONOMER\n",
+      "no protein for EG11986-MONOMER\n",
+      "no protein for EG12020-MONOMER\n",
+      "no protein for EG12051-MONOMER\n",
+      "1400\n",
+      "no protein for EG12161-MONOMER\n",
+      "no protein for EG12208-MONOMER\n",
+      "no protein for EG12227-MONOMER\n",
+      "no protein for EG12238-MONOMER\n",
+      "no protein for EG12261-MONOMER\n",
+      "1500\n",
+      "no protein for EG12364-MONOMER\n",
+      "1600\n",
+      "no protein for EG12881-MONOMER\n",
+      "1700\n",
+      "no protein for G0-16653-MONOMER\n",
+      "no protein for G0-16659-MONOMER\n",
+      "no protein for G0-16666-MONOMER\n",
+      "no protein for G0-16672-MONOMER\n",
+      "no protein for G0-16713-MONOMER\n",
+      "no protein for G0-16715-MONOMER\n",
+      "no protein for G0-16716-MONOMER\n",
+      "no protein for G0-16717-MONOMER\n",
+      "no protein for G0-16718-MONOMER\n",
+      "no protein for G0-16719-MONOMER\n",
+      "no protein for G0-16720-MONOMER\n",
+      "1800\n",
+      "no protein for G6102-MONOMER\n",
+      "no protein for G6113-MONOMER\n",
+      "no protein for G6114-MONOMER\n",
+      "no protein for G6119-MONOMER\n",
+      "no protein for G6130-MONOMER\n",
+      "no protein for G6137-MONOMER\n",
+      "no protein for G6170-MONOMER\n",
+      "no protein for G6214-MONOMER\n",
+      "no protein for G6222-MONOMER\n",
+      "1900\n",
+      "no protein for G6231-MONOMER\n",
+      "no protein for G6271-MONOMER\n",
+      "no protein for G6295-MONOMER\n",
+      "no protein for G6298-MONOMER\n",
+      "no protein for G6316-MONOMER\n",
+      "2000\n",
+      "no protein for G6376-MONOMER\n",
+      "no protein for G6377-MONOMER\n",
+      "2100\n",
+      "no protein for G6535-MONOMER\n",
+      "no protein for G6596-MONOMER\n",
+      "no protein for G6599-MONOMER\n",
+      "no protein for G6609-MONOMER\n",
+      "2200\n",
+      "no protein for G6632-MONOMER\n",
+      "no protein for G6686-MONOMER\n",
+      "no protein for G6692-MONOMER\n",
+      "no protein for G6722-MONOMER\n",
+      "2300\n",
+      "no protein for G6767-MONOMER\n",
+      "no protein for G6771-MONOMER\n",
+      "no protein for G6796-MONOMER\n",
+      "no protein for G6836-MONOMER\n",
+      "no protein for G6837-MONOMER\n",
+      "2400\n",
+      "no protein for G6926-MONOMER\n",
+      "2500\n",
+      "no protein for G7043-MONOMER\n",
+      "no protein for G7044-MONOMER\n",
+      "no protein for G7052-MONOMER\n",
+      "no protein for G7066-MONOMER\n",
+      "no protein for G7075-MONOMER\n",
+      "no protein for G7079-MONOMER\n",
+      "2600\n",
+      "no protein for G7120-MONOMER\n",
+      "no protein for G7155-MONOMER\n",
+      "no protein for G7209-MONOMER\n",
+      "no protein for G7224-MONOMER\n",
+      "2700\n",
+      "2800\n",
+      "no protein for G7376-MONOMER\n",
+      "no protein for G7383-MONOMER\n",
+      "no protein for G7389-MONOMER\n",
+      "no protein for G7470-MONOMER\n",
+      "no protein for G7474-MONOMER\n",
+      "no protein for G7475-MONOMER\n",
+      "no protein for G7476-MONOMER\n",
+      "no protein for G7478-MONOMER\n",
+      "no protein for G7479-MONOMER\n",
+      "no protein for G7482-MONOMER\n",
+      "2900\n",
+      "no protein for G7515-MONOMER\n",
+      "no protein for G7557-MONOMER\n",
+      "no protein for G7585-MONOMER\n",
+      "3000\n",
+      "no protein for G7633-MONOMER\n",
+      "no protein for G7671-MONOMER\n",
+      "3100\n",
+      "no protein for G7760-MONOMER\n",
+      "no protein for G7798-MONOMER\n",
+      "no protein for G7863-MONOMER\n",
+      "no protein for G7898-MONOMER\n",
+      "3200\n",
+      "no protein for G7902-MONOMER\n",
+      "no protein for G7903-MONOMER\n",
+      "no protein for G7904-MONOMER\n",
+      "no protein for G7907-MONOMER\n",
+      "no protein for G7918-MONOMER\n",
+      "no protein for G7934-MONOMER\n",
+      "no protein for G8205-MONOMER\n",
+      "no protein for G8221-MONOMER\n",
+      "no protein for GATC-MONOMER\n",
+      "3300\n",
+      "3400\n",
+      "3500\n",
+      "no dblink for MONOMER0-1241\n",
+      "no protein for MONOMER0-2653\n",
+      "no protein for MONOMER0-2666\n",
+      "no protein for MONOMER0-2667\n",
+      "no uniprot id for MONOMER0-2748\n",
+      "no uniprot id for MONOMER0-2755\n",
+      "no uniprot id for MONOMER0-2756\n",
+      "no uniprot id for MONOMER0-2757\n",
+      "no uniprot id for MONOMER0-2761\n",
+      "no protein for MONOMER0-2778\n",
+      "3600\n",
+      "no uniprot id for MONOMER0-2824\n",
+      "no uniprot id for MONOMER0-2825\n",
+      "no uniprot id for MONOMER0-2851\n",
+      "no dblink for MONOMER0-4223\n",
+      "no uniprot id for MONOMER0-4229\n",
+      "no uniprot id for MONOMER0-4230\n",
+      "no uniprot id for MONOMER0-4231\n",
+      "no uniprot id for MONOMER0-4246\n",
+      "no uniprot id for MONOMER0-4247\n",
+      "no uniprot id for MONOMER0-4248\n",
+      "no uniprot id for MONOMER0-4249\n",
+      "no uniprot id for MONOMER0-4255\n",
+      "no uniprot id for MONOMER0-4256\n",
+      "no uniprot id for MONOMER0-4257\n",
+      "no uniprot id for MONOMER0-4258\n",
+      "no uniprot id for MONOMER0-4259\n",
+      "no uniprot id for MONOMER0-4345\n",
+      "no uniprot id for MONOMER0-4346\n",
+      "no protein for MONOMER0-4386\n",
+      "no protein for MONOMER0-4395\n",
+      "3700\n",
+      "no dblink for MONOMER0-4509\n",
+      "no uniprot id for MONOMER0-4519\n",
+      "no uniprot id for MONOMER0-4521\n",
+      "3800\n",
+      "3900\n",
+      "no protein for PD00338\n",
+      "no protein for PD03576\n",
+      "4000\n",
+      "no protein for PHNE-MONOMER\n",
+      "4100\n",
+      "4200\n",
+      "4300\n",
+      "no protein for YCJV-MONOMER\n",
+      "no protein for YGAY-MONOMER\n",
+      "no protein for YHDW-MONOMER\n",
+      "4400\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(proteins_df.index)):\n",
+    "    if i % 100 == 0:\n",
+    "        print(i)\n",
+    "\n",
+    "    protein = proteins_df.loc[i, 'id']\n",
+    "\n",
+    "    req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{protein}&detail=high'\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    if r.status_code != 200:\n",
+    "        print(protein, r.status_code)\n",
+    "        proteins_df.loc[i, 'uniprot_id'] = 'NO ECOCYC ENTRY'\n",
+    "        continue\n",
+    "\n",
+    "    o = xmltodict.parse(r.content)['ptools-xml']\n",
+    "\n",
+    "    if 'Protein' not in o.keys():\n",
+    "        print(f'no protein for {protein}')\n",
+    "        proteins_df.loc[i, 'uniprot_id'] = 'NO PROTEIN ENTRY'\n",
+    "        continue\n",
+    "    elif 'dblink' not in o['Protein'].keys():\n",
+    "        print(f'no dblink for {protein}')\n",
+    "        proteins_df.loc[i, 'uniprot_id'] = 'NO DBLINK ENTRY'\n",
+    "        continue\n",
+    "\n",
+    "    o = o['Protein']['dblink']\n",
+    "\n",
+    "    if type(o) is dict:\n",
+    "        o = [o]\n",
+    "\n",
+    "    found = False\n",
+    "    for data_source in o:\n",
+    "        if data_source['dblink-db'] == 'UNIPROT' and 'dblink-oid' in data_source.keys():\n",
+    "            uniprot_id = data_source['dblink-oid']\n",
+    "\n",
+    "            proteins_df.loc[i, 'uniprot_id'] = uniprot_id\n",
+    "\n",
+    "            found = True\n",
+    "\n",
+    "    if not found:\n",
+    "        print(f'no uniprot id for {protein}')\n",
+    "        proteins_df.loc[i, 'uniprot_id'] = 'NO UNIPROT ENTRY'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T18:08:01.390251Z",
+     "start_time": "2023-06-23T17:51:08.885980Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "outputs": [],
+   "source": [
+    "proteins_df.to_csv('notebooks/fbagd/proteins.csv', index=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T18:22:13.135254Z",
+     "start_time": "2023-06-23T18:22:13.118803Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                            id  \\\n0      1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER   \n1                                1-PFK-MONOMER   \n2             2-DEHYDROPANTOATE-REDUCT-MONOMER   \n3                 2-ISOPROPYLMALATESYN-MONOMER   \n4     2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER   \n...                                        ...   \n4415                              YTFR-MONOMER   \n4416                              YTFT-MONOMER   \n4417                              ZNUA-MONOMER   \n4418                              ZNUB-MONOMER   \n4419                              ZNUC-MONOMER   \n\n                                            common_name uniprot_id  \n0     1-acylglycerol-3-phosphate <i>O</i>-acyltransf...     P26647  \n1                                 1-phosphofructokinase     P0AEW9  \n2                         2-dehydropantoate 2-reductase     P0A9J4  \n3                            2-isopropylmalate synthase     P09151  \n4     bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...     P0A887  \n...                                                 ...        ...  \n4415  galactofuranose ABC transporter putative ATP b...     Q6BEX0  \n4416  galactofuranose ABC transporter putative membr...     P39328  \n4417  Zn<sup>2+</sup> ABC transporter periplasmic bi...     P39172  \n4418   Zn<sup>2+</sup> ABC transporter membrane subunit     P39832  \n4419  Zn<sup>2+</sup> ABC transporter ATP binding su...     P0A9X1  \n\n[4420 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>common_name</th>\n      <th>uniprot_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-ACYLGLYCEROL-3-P-ACYLTRANSFER-MONOMER</td>\n      <td>1-acylglycerol-3-phosphate &lt;i&gt;O&lt;/i&gt;-acyltransf...</td>\n      <td>P26647</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1-PFK-MONOMER</td>\n      <td>1-phosphofructokinase</td>\n      <td>P0AEW9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2-DEHYDROPANTOATE-REDUCT-MONOMER</td>\n      <td>2-dehydropantoate 2-reductase</td>\n      <td>P0A9J4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2-ISOPROPYLMALATESYN-MONOMER</td>\n      <td>2-isopropylmalate synthase</td>\n      <td>P09151</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2-OCTAPRENYL-METHOXY-BENZOQ-METH-MONOMER</td>\n      <td>bifunctional 2-octaprenyl-6-methoxy-1,4-benzoq...</td>\n      <td>P0A887</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4415</th>\n      <td>YTFR-MONOMER</td>\n      <td>galactofuranose ABC transporter putative ATP b...</td>\n      <td>Q6BEX0</td>\n    </tr>\n    <tr>\n      <th>4416</th>\n      <td>YTFT-MONOMER</td>\n      <td>galactofuranose ABC transporter putative membr...</td>\n      <td>P39328</td>\n    </tr>\n    <tr>\n      <th>4417</th>\n      <td>ZNUA-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter periplasmic bi...</td>\n      <td>P39172</td>\n    </tr>\n    <tr>\n      <th>4418</th>\n      <td>ZNUB-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter membrane subunit</td>\n      <td>P39832</td>\n    </tr>\n    <tr>\n      <th>4419</th>\n      <td>ZNUC-MONOMER</td>\n      <td>Zn&lt;sup&gt;2+&lt;/sup&gt; ABC transporter ATP binding su...</td>\n      <td>P0A9X1</td>\n    </tr>\n  </tbody>\n</table>\n<p>4420 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "proteins_df = pd.read_csv('notebooks/fbagd/proteins.csv')\n",
+    "proteins_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T22:44:47.936315Z",
+     "start_time": "2023-06-23T22:44:47.922293Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Use uniprot id to get cofactor comments"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Empty DataFrame\nColumns: [protein, cofactor_uniprot_name, cofactor_uniprot_id, cofactor_uniprot_db, comment]\nIndex: []",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>protein</th>\n      <th>cofactor_uniprot_name</th>\n      <th>cofactor_uniprot_id</th>\n      <th>cofactor_uniprot_db</th>\n      <th>comment</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cofactor_slot_df = pd.DataFrame(columns=['protein', 'cofactor_uniprot_name', 'cofactor_uniprot_id', 'cofactor_uniprot_db', 'cofactor_comment', 'subunit_comment'])\n",
+    "cofactor_slot_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T22:47:10.501848Z",
+     "start_time": "2023-06-23T22:47:10.499336Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "\n",
+      "\n",
+      "P26647\n",
+      "\n",
+      "\n",
+      "P0AEW9\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Can also use Mn(2+) or Co(2+), with lower efficiency.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A9J4\n",
+      "Subunit:  Monomer.\n",
+      "\n",
+      "\n",
+      "P09151\n",
+      "Cofactor 1, option 1 Mn(2+) CHEBI:29035 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A887\n",
+      "Subunit:  Component of the Ubi complex metabolon, which regroups five ubiquinone biosynthesis proteins (UbiE, UbiF, UbiG, UbiH and UbiI) and two accessory factors (UbiK and the lipid-binding protein UbiJ).\n",
+      "\n",
+      "\n",
+      "P0A6A0\n",
+      "\n",
+      "\n",
+      "P31057\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Cofactor 1, option 2 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Cofactor 1, option 3 Co(2+) CHEBI:48828 ChEBI\n",
+      "Cofactor 1, option 4 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) ion per subunit. Can also use Mn(2+), Co(2+) and Zn(2+) to a lesser extent.\n",
+      "Subunit:  Homodecamer; pentamer of dimers.\n",
+      "\n",
+      "\n",
+      "P30125\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Cofactor 1, option 2 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) or Mn(2+) ion per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0AEK2\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P0AAI5\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0AGK1\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Can also use Mn(2+) and Co(2+) with lower efficiency.\n",
+      "\n",
+      "\n",
+      "P0A796\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P06999\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P00350\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P52697\n",
+      "\n",
+      "\n",
+      "P0AET8\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P12998\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P31119\n",
+      "\n",
+      "\n",
+      "P30750\n",
+      "Subunit:  The complex is composed of two ATP-binding proteins (MetN), two transmembrane proteins (MetI) and a solute-binding protein (MetQ).\n",
+      "\n",
+      "\n",
+      "P46133\n",
+      "\n",
+      "\n",
+      "P0A6A3\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Cofactor 1, option 2 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Comment:  Mg(2+). Can also accept Mn(2+).\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P00894\n",
+      "Subunit:  Dimer of large and small chains.\n",
+      "\n",
+      "\n",
+      "P00893\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) ion per subunit.\n",
+      "Cofactor 2, option 1 thiamine diphosphate CHEBI:58937 ChEBI\n",
+      "Comment:  Binds 1 thiamine pyrophosphate per subunit.\n",
+      "Subunit:  Dimer of large and small chains.\n",
+      "\n",
+      "\n",
+      "P76461\n",
+      "\n",
+      "\n",
+      "P0A6C8\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P23908\n",
+      "Cofactor 1, option 1 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Cofactor 1, option 2 Co(2+) CHEBI:48828 ChEBI\n",
+      "Comment:  Binds 2 Zn(2+) or Co(2+) ions per subunit.\n",
+      "Cofactor 2, option 1 glutathione CHEBI:57925 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P18335\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "Comment:  Binds 1 pyridoxal phosphate per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A6L4\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P25516\n",
+      "Cofactor 1, option 1 [4Fe-4S] cluster CHEBI:49883 ChEBI\n",
+      "Comment:  Binds 1 [4Fe-4S] cluster per subunit.\n",
+      "Subunit:  Monomer.\n",
+      "\n",
+      "\n",
+      "P36683\n",
+      "Cofactor 1, option 1 [4Fe-4S] cluster CHEBI:49883 ChEBI\n",
+      "Comment:  Binds 1 [4Fe-4S] cluster per subunit.\n",
+      "Subunit:  Monomer. AcnB can also form a homodimer. The monomer-homodimer transition is dependent on iron availability and the carboxymethylation of C-273 inhibits the dimer formation.\n",
+      "\n",
+      "\n",
+      "P69330\n",
+      "Subunit:  Oligomer with a subunit composition of (alpha,beta,gamma)6.\n",
+      "\n",
+      "\n",
+      "P31224\n",
+      "Subunit:  Homotrimer, with large domains that extend into the periplasm, interacts with AcrA and TolC. AcrA may be required to stably link this protein and TolC. Interacts with AcrZ. Part of the AcrA-AcrB-AcrZ-TolC efflux pump.\n",
+      "\n",
+      "\n",
+      "P24177\n",
+      "\n",
+      "\n",
+      "P24181\n",
+      "Subunit:  Part of the tripartite efflux system AcrEF-TolC, which is composed of an inner membrane transporter, AcrF, a periplasmic membrane fusion protein, AcrE, and an outer membrane component, TolC. The complex forms a large protein conduit and can translocate molecules across both the inner and outer membranes (Probable).\n",
+      "\n",
+      "\n",
+      "P27550\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Forms a 1:1 complex with CobB/NAD-dependent deacetylase.\n",
+      "\n",
+      "\n",
+      "P0ABK5\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer (Ref.6, PubMed:27531961). Forms a cysteine synthase complex with 1 copy of CysE (By similarity).\n",
+      "Subunit:  (Microbial infection) Interacts with CdiA-CT from strain 536 / UPEC, this is blocked upon preincubation with O-acetyl-L-serine. CysK forms a complex with CdiA-CT/CdiI (PubMed:22333533). One CdiA toxin subunit binds to each subunit of the CysK homodimer, and one CdiI immunity protein binds to each toxin subunit; the immune complex is thus a dimer of trimers. The 4 C-terminal residues of CdiA fit into the active site of CysK (PubMed:27531961).\n",
+      "\n",
+      "\n",
+      "P16703\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "Q47146\n",
+      "Cofactor 1, option 1 FAD CHEBI:57692 ChEBI\n",
+      "No cofactor slot comment\n",
+      "\n",
+      "\n",
+      "P69451\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P28305\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P22333\n",
+      "Cofactor 1, option 1 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 1 zinc ion per subunit.\n",
+      "\n",
+      "\n",
+      "P69503\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P69441\n",
+      "Subunit:  Monomer.\n",
+      "\n",
+      "\n",
+      "P00936\n",
+      "\n",
+      "\n",
+      "P0A7D4\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) ion per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A6J1\n",
+      "\n",
+      "\n",
+      "P25437\n",
+      "Cofactor 1, option 1 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 2 Zn(2+) ions per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A9Q7\n",
+      "Cofactor 1, option 1 Fe(2+) CHEBI:29033 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Forms long filaments, called spirosomes (PubMed:31586059, PubMed:32188856, PubMed:32523125). Forms rod-like ultrastructures composed of 20-60 identical subunits (PubMed:1325457). Two monomers form a dimer in a head-to-head arm-crossing fashion through the ALDH domains (PubMed:31586059). Subsequently, two dimers form one helical pitch via the interaction of ADH domains in a tail-to-tail manner (PubMed:31586059, PubMed:32523125). These units are further assembled into spirosomes (PubMed:31586059, PubMed:32188856, PubMed:32523125). Formation of these spirosomes is required for activity (PubMed:31586059, PubMed:32188856, PubMed:32523125). Can be isolated in vitro in a wide range of oligomeric states (PubMed:31586059).\n",
+      "\n",
+      "\n",
+      "P39451\n",
+      "Cofactor 1, option 1 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 2 Zn(2+) ions per subunit.\n",
+      "\n",
+      "\n",
+      "P13035\n",
+      "Cofactor 1, option 1 FAD CHEBI:57692 ChEBI\n",
+      "No cofactor slot comment\n",
+      "\n",
+      "\n",
+      "P42909\n",
+      "\n",
+      "\n",
+      "P42910\n",
+      "\n",
+      "\n",
+      "P42911\n",
+      "\n",
+      "\n",
+      "P36881\n",
+      "\n",
+      "\n",
+      "P60651\n",
+      "Cofactor 1, option 1 Mn(2+) CHEBI:29035 ChEBI\n",
+      "No cofactor slot comment\n",
+      "\n",
+      "\n",
+      "P15639\n",
+      "\n",
+      "\n",
+      "P08178\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0AB77\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "Comment:  Binds 1 pyridoxal phosphate per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A6B4\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P29012\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "\n",
+      "\n",
+      "P00957\n",
+      "Cofactor 1, option 1 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 1 zinc ion per subunit; it is not clear where this binding occurs.\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P37685\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P23883\n",
+      "\n",
+      "\n",
+      "P0A9C3\n",
+      "\n",
+      "\n",
+      "P00634\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) ion.\n",
+      "Cofactor 2, option 1 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 2 Zn(2+) ions.\n",
+      "Subunit:  Isozymes 1 and 3 are a dimer of identical chains, isozyme 2 is a dimer of heterogeneous chains, one of each of the subunits from isozymes 1 and 3.\n",
+      "\n",
+      "\n",
+      "P26612\n",
+      "Cofactor 1, option 1 Ca(2+) CHEBI:29108 ChEBI\n",
+      "Comment:  Binds 1 Ca(2+) ion per subunit.\n",
+      "Subunit:  Monomer.\n",
+      "\n",
+      "\n",
+      "P25718\n",
+      "Cofactor 1, option 1 Ca(2+) CHEBI:29108 ChEBI\n",
+      "Comment:  Binds 1 Ca(2+) ion per subunit.\n",
+      "Subunit:  Monomer.\n",
+      "\n",
+      "\n",
+      "P38038\n",
+      "Cofactor 1, option 1 FAD CHEBI:57692 ChEBI\n",
+      "Comment:  Binds 1 FAD per subunit.\n",
+      "Cofactor 2, option 1 FMN CHEBI:58210 ChEBI\n",
+      "Comment:  Binds 1 FMN per subunit.\n",
+      "Subunit:  Alpha(8)-beta(8). The alpha component is a flavoprotein, the beta component is a hemoprotein.\n",
+      "\n",
+      "\n",
+      "P06720\n",
+      "Cofactor 1, option 1 NAD(+) CHEBI:57540 ChEBI\n",
+      "Comment:  Binds 1 NAD(+) per subunit.\n",
+      "Cofactor 2, option 1 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Comment:  Binds 1 Mn(2+) ion per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0A6L7\n",
+      "\n",
+      "\n",
+      "P42604\n",
+      "Cofactor 1, option 1 Fe(2+) CHEBI:29033 ChEBI\n",
+      "Cofactor 1, option 2 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Comment:  Mn(2+) can substitute for iron, but in higher concentrations. Cannot use Fe(3+), Ni(2+) or Mg(2+).\n",
+      "\n",
+      "\n",
+      "P46883\n",
+      "Cofactor 1, option 1 Cu cation CHEBI:23378 ChEBI\n",
+      "Cofactor 1, option 2 Zn(2+) CHEBI:29105 ChEBI\n",
+      "Comment:  Binds 1 copper ion per subunit. Can also use zinc ion as cofactor (By similarity).\n",
+      "Cofactor 2, option 1 Ca(2+) CHEBI:29108 ChEBI\n",
+      "Comment:  Binds 2 calcium ions per subunit.\n",
+      "Cofactor 3, option 1 L-topaquinone CHEBI:79027 ChEBI\n",
+      "Comment:  Contains 1 topaquinone per subunit.\n",
+      "Cofactor 4, option 1 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Comment:  Binds 1 Mn(2+) ion per subunit.\n",
+      "Subunit:  Homodimer.\n",
+      "\n",
+      "\n",
+      "P0AE12\n",
+      "Subunit:  Homohexamer. Trimer of dimers.\n",
+      "\n",
+      "\n",
+      "P0AE16\n",
+      "\n",
+      "\n",
+      "P69681\n",
+      "Subunit:  Homotrimer (PubMed:12023896, PubMed:15563598, PubMed:15361618, PubMed:17040913). In response to elevation of the extracellular ammonium concentration, interacts and forms a complex with GlnK (PubMed:10637624, PubMed:11847102, PubMed:14668330, PubMed:17220269, PubMed:17190799, PubMed:16864585, PubMed:20639578). Interacts with GlnK with a stoichiometry of AmtB(3):GlnK(3) (PubMed:17220269, PubMed:17190799). Sequestration of GlnK is reversible (PubMed:14668330).\n",
+      "\n",
+      "\n",
+      "P15977\n",
+      "\n",
+      "\n",
+      "P0A9C0\n",
+      "Cofactor 1, option 1 FAD CHEBI:57692 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Cofactor 2, option 1 FMN CHEBI:58210 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Composed of a catalytic GlpA/B dimer and of membrane bound GlpC.\n",
+      "\n",
+      "\n",
+      "P13033\n",
+      "Cofactor 1, option 1 FMN CHEBI:58210 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Composed of a catalytic GlpA/B dimer and of membrane bound GlpC.\n",
+      "\n",
+      "\n",
+      "P0A996\n",
+      "Subunit:  Composed of a catalytic GlpA/B dimer and of GlpC.\n",
+      "\n",
+      "\n",
+      "P0A962\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P00805\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P77610\n",
+      "\n",
+      "\n",
+      "P00895\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) ion per subunit.\n",
+      "Subunit:  Heterotetramer consisting of two non-identical subunits: a beta subunit (TrpG) and a large lpha subunit (TrpE).\n",
+      "\n",
+      "\n",
+      "P00904\n",
+      "Subunit:  Heterotetramer consisting of two non-identical subunits: a beta subunit (TrpG) and a large alpha subunit (TrpE).\n",
+      "\n",
+      "\n",
+      "P0AE22\n",
+      "Cofactor 1, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "Comment:  Binds 1 Mg(2+) ion per subunit.\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P07102\n",
+      "Subunit:  Monomer.\n",
+      "\n",
+      "\n",
+      "P26458\n",
+      "Cofactor 1, option 1 heme CHEBI:30413 ChEBI\n",
+      "Comment:  May bind up to 3 heme groups per complex.\n",
+      "Subunit:  Heterodimer of subunits I and II.\n",
+      "\n",
+      "\n",
+      "P26459\n",
+      "Cofactor 1, option 1 heme CHEBI:30413 ChEBI\n",
+      "Comment:  May bind up to 3 heme groups per complex.\n",
+      "Subunit:  Heterodimer of subunits I and II.\n",
+      "\n",
+      "\n",
+      "P60844\n",
+      "Subunit:  Homotetramer.\n",
+      "\n",
+      "\n",
+      "P08202\n",
+      "Cofactor 1, option 1 Mn(2+) CHEBI:29035 ChEBI\n",
+      "Comment:  Binds 1 Mn(2+) ion per subunit.\n",
+      "Subunit:  Homohexamer.\n",
+      "\n",
+      "\n",
+      "P0AE24\n",
+      "\n",
+      "\n",
+      "P02924\n",
+      "\n",
+      "\n",
+      "P0AAF3\n",
+      "Subunit:  The complex is composed of two ATP-binding proteins (AraG), two transmembrane proteins (AraH) and a solute-binding protein (AraF).\n",
+      "\n",
+      "\n",
+      "P0AE26\n",
+      "\n",
+      "\n",
+      "P23910\n",
+      "\n",
+      "\n",
+      "P0A9Q1\n",
+      "\n",
+      "\n",
+      "P0AEC3\n",
+      "\n",
+      "\n",
+      "P0AAE5\n",
+      "\n",
+      "\n",
+      "P21170\n",
+      "Cofactor 1, option 1 pyridoxal 5'-phosphate CHEBI:597326 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Cofactor 2, option 1 Mg(2+) CHEBI:18420 ChEBI\n",
+      "No cofactor slot comment\n",
+      "Subunit:  Homotetramer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(100): # range(len(proteins_df.index)):\n",
+    "    if i % 100 == 0:\n",
+    "        print(i)\n",
+    "\n",
+    "    protein = proteins_df.loc[i, 'uniprot_id']\n",
+    "\n",
+    "    if protein in ['NO UNIPROT ENTRY', 'NO PROTEIN ENTRY', 'NO DBLINK ENTRY', 'NO ECOCYC ENTRY']:\n",
+    "        continue\n",
+    "\n",
+    "    # use uniprot api to get protein associated with uniprot id\n",
+    "    # req_str = f'https://www.uniprot.org/uniprot/{protein}.xml'\n",
+    "    req_str = f'https://rest.uniprot.org/uniprotkb/{protein}.xml'\n",
+    "\n",
+    "    r = s.get(req_str)\n",
+    "    o = xmltodict.parse(r.content)\n",
+    "\n",
+    "    print('\\n\\n' + protein)\n",
+    "\n",
+    "    cofactor_n = 0\n",
+    "\n",
+    "    if 'comment' in o['uniprot']['entry'].keys():\n",
+    "\n",
+    "        comments = o['uniprot']['entry']['comment']\n",
+    "\n",
+    "        if type(comments) is dict:\n",
+    "            comments = [comments]\n",
+    "\n",
+    "        for comment in comments:\n",
+    "            if comment['@type'] == 'cofactor':\n",
+    "\n",
+    "\n",
+    "                cofactor_n += 1\n",
+    "                cofactor_slot = comment['cofactor']\n",
+    "\n",
+    "                if type(cofactor_slot) is dict:\n",
+    "                    cofactor_slot = [cofactor_slot]\n",
+    "\n",
+    "                for j, cofactor in enumerate(cofactor_slot):\n",
+    "                    print(f'Cofactor {cofactor_n}, option {j+1}', cofactor['name'], cofactor['dbReference']['@id'], cofactor['dbReference']['@type'])\n",
+    "                    cofactor_slot_df.append({'protein':protein,\n",
+    "                                                                'cofactor_uniprot_name':cofactor['name'],\n",
+    "                                                                'cofactor_uniprot_id':cofactor['dbReference']['@id'],\n",
+    "                                                                'cofactor_uniprot_db':cofactor['dbReference']['@type']}, ignore_index=True)\n",
+    "\n",
+    "                    # proteins_df.loc[i, 'cofactor_uniprot_name'] = cofactor['cofactor']['name']\n",
+    "                    # proteins_df.loc[i, 'cofactor_uniprot_id'] = cofactor['cofactor']['dbReference']['@id']\n",
+    "                    # proteins_df.loc[i, 'cofactor_uniprot_db'] = cofactor['cofactor']['dbReference']['@type']\n",
+    "\n",
+    "                if 'text' in comment.keys():\n",
+    "                    if type(comment['text']) is dict:\n",
+    "                        print('Comment: ',comment['text']['#text'])\n",
+    "                    else:\n",
+    "                        print('Comment: ',comment['text'])\n",
+    "                else:\n",
+    "                    print('No cofactor slot comment')\n",
+    "\n",
+    "            if comment['@type'] == 'subunit':\n",
+    "                if 'text' in comment.keys():\n",
+    "                    if type(comment['text']) is dict:\n",
+    "                        print('Subunit: ', comment['text']['#text'])\n",
+    "                    else:\n",
+    "                        print('Subunit: ', comment['text'])\n",
+    "                else:\n",
+    "                    print('Subunit: no subunit comment')\n",
+    "\n",
+    "    else:\n",
+    "        print('no protein comment')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T22:30:06.355344Z",
+     "start_time": "2023-06-23T22:29:48.207011Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'@type': 'function',\n",
+      "  'text': 'The nitrate reductase enzyme complex allows E.coli to use nitrate '\n",
+      "          'as an electron acceptor during anaerobic growth. The alpha chain is '\n",
+      "          'the actual site of nitrate reduction.'},\n",
+      " {'@type': 'catalytic activity',\n",
+      "  'reaction': {'dbReference': [{'@id': 'RHEA:56144', '@type': 'Rhea'},\n",
+      "                               {'@id': 'CHEBI:15377', '@type': 'ChEBI'},\n",
+      "                               {'@id': 'CHEBI:16301', '@type': 'ChEBI'},\n",
+      "                               {'@id': 'CHEBI:17632', '@type': 'ChEBI'},\n",
+      "                               {'@id': 'CHEBI:24646', '@type': 'ChEBI'},\n",
+      "                               {'@id': 'CHEBI:132124', '@type': 'ChEBI'},\n",
+      "                               {'@id': '1.7.5.1', '@type': 'EC'}],\n",
+      "               'text': 'a quinol + nitrate = a quinone + H2O + nitrite'}},\n",
+      " {'@type': 'cofactor',\n",
+      "  'cofactor': {'dbReference': {'@id': 'CHEBI:49883', '@type': 'ChEBI'},\n",
+      "               'name': '[4Fe-4S] cluster'},\n",
+      "  'text': 'Binds 1 [4Fe-4S] cluster per subunit.'},\n",
+      " {'@type': 'cofactor',\n",
+      "  'cofactor': {'dbReference': {'@id': 'CHEBI:60539', '@type': 'ChEBI'},\n",
+      "               'name': 'Mo-bis(molybdopterin guanine dinucleotide)'},\n",
+      "  'text': 'Binds 1 molybdenum-bis(molybdopterin guanine dinucleotide) '\n",
+      "          '(Mo-bis-MGD) cofactor per subunit.'},\n",
+      " {'@type': 'subunit',\n",
+      "  'text': {'#text': 'Dimer of heterotrimers each composed of an alpha, a beta '\n",
+      "                    'and a gamma chain. Alpha and beta are catalytic chains; '\n",
+      "                    'gamma chains are involved in binding the enzyme complex '\n",
+      "                    'to the cytoplasmic membrane. Interacts with the NarJ '\n",
+      "                    'chaperone.',\n",
+      "           '@evidence': '2 3 4 7'}},\n",
+      " {'@type': 'interaction',\n",
+      "  'experiments': '13',\n",
+      "  'interactant': [{'@intactId': 'EBI-547248', 'id': 'P09152'},\n",
+      "                  {'@intactId': 'EBI-555067', 'id': 'P11349', 'label': 'narH'}],\n",
+      "  'organismsDiffer': 'false'},\n",
+      " {'@type': 'interaction',\n",
+      "  'experiments': '18',\n",
+      "  'interactant': [{'@intactId': 'EBI-547248', 'id': 'P09152'},\n",
+      "                  {'@intactId': 'EBI-555043', 'id': 'P0AF26', 'label': 'narJ'}],\n",
+      "  'organismsDiffer': 'false'},\n",
+      " {'@type': 'interaction',\n",
+      "  'experiments': '9',\n",
+      "  'interactant': [{'@intactId': 'EBI-547248', 'id': 'P09152'},\n",
+      "                  {'@intactId': 'EBI-555088', 'id': 'P19317', 'label': 'narW'}],\n",
+      "  'organismsDiffer': 'false'},\n",
+      " {'@type': 'interaction',\n",
+      "  'experiments': '5',\n",
+      "  'interactant': [{'@intactId': 'EBI-547248', 'id': 'P09152'},\n",
+      "                  {'@intactId': 'EBI-555059', 'id': 'P19318', 'label': 'narY'}],\n",
+      "  'organismsDiffer': 'false'},\n",
+      " {'@type': 'subcellular location',\n",
+      "  'subcellularLocation': {'location': 'Cell membrane',\n",
+      "                          'topology': 'Peripheral membrane protein'}},\n",
+      " {'@type': 'induction', 'text': 'By nitrate.'},\n",
+      " {'@type': 'domain',\n",
+      "  'text': {'#text': 'Apoenzyme contains at least 2 NarJ-binding sites, one '\n",
+      "                    'interfering with membrane anchoring and another being '\n",
+      "                    'involved in molybdenum insertion. The first binding-site '\n",
+      "                    'is a short peptide sequence near the N-terminus that '\n",
+      "                    'contains a twin-arginine homologous motif.',\n",
+      "           '@evidence': '2 3'}},\n",
+      " {'@type': 'similarity',\n",
+      "  'text': {'#text': 'Belongs to the prokaryotic molybdopterin-containing '\n",
+      "                    'oxidoreductase family.',\n",
+      "           '@evidence': '8'}},\n",
+      " {'@name': 'Worthington enzyme manual',\n",
+      "  '@type': 'online information',\n",
+      "  'link': {'@uri': 'https://www.worthington-biochem.com/NAR/'}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "protein = 'P09152'\n",
+    "\n",
+    "# use uniprot api to get protein associated with uniprot id\n",
+    "# req_str = f'https://www.uniprot.org/uniprot/{protein}.xml'\n",
+    "req_str = f'https://rest.uniprot.org/uniprotkb/{protein}.xml'\n",
+    "\n",
+    "r = s.get(req_str)\n",
+    "o = xmltodict.parse(r.content)\n",
+    "pp.pprint(o['uniprot']['entry']['comment'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-23T22:27:46.357512Z",
+     "start_time": "2023-06-23T22:27:45.245773Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Get the monomers of a complex"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T23:13:53.550880Z",
+     "start_time": "2023-06-22T23:13:53.205788Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'@ID': 'ECOLI:LEUC-MONOMER',\n",
+      " '@detail': 'full',\n",
+      " '@frameid': 'LEUC-MONOMER',\n",
+      " '@orgid': 'ECOLI',\n",
+      " 'citation': [{'Publication': {'@ID': 'ECOLI:PUB-320178',\n",
+      "                               '@detail': 'full',\n",
+      "                               '@frameid': 'PUB-320178',\n",
+      "                               '@orgid': 'ECOLI',\n",
+      "                               'author': [{'#text': 'Davis MG',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Calvo JM',\n",
+      "                                           '@datatype': 'string'}],\n",
+      "                               'pubmed-id': {'#text': '320178',\n",
+      "                                             '@datatype': 'string'},\n",
+      "                               'source': {'#text': 'J Bacteriol 129(2);1078-90',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                               'title': {'#text': 'Isolation and '\n",
+      "                                                  'characterization of lambda '\n",
+      "                                                  'pleu bacteriophages.',\n",
+      "                                         '@datatype': 'string'},\n",
+      "                               'year': {'#text': '1977',\n",
+      "                                        '@datatype': 'integer'}}},\n",
+      "              {'Publication': {'@ID': 'ECOLI:PUB-15657047',\n",
+      "                               '@detail': 'full',\n",
+      "                               '@frameid': 'PUB-15657047',\n",
+      "                               '@orgid': 'ECOLI',\n",
+      "                               'author': [{'#text': 'Yang CR',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Shapiro BE',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Hung SP',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Mjolsness ED',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Hatfield GW',\n",
+      "                                           '@datatype': 'string'}],\n",
+      "                               'pubmed-id': {'#text': '15657047',\n",
+      "                                             '@datatype': 'string'},\n",
+      "                               'source': {'#text': 'J Biol Chem '\n",
+      "                                                   '280(12);11224-32',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                               'title': {'#text': 'A mathematical model for '\n",
+      "                                                  'the branched chain amino '\n",
+      "                                                  'acid biosynthetic pathways '\n",
+      "                                                  'of Escherichia coli K12.',\n",
+      "                                         '@datatype': 'string'},\n",
+      "                               'year': {'#text': '2005',\n",
+      "                                        '@datatype': 'integer'}}},\n",
+      "              {'Publication': {'@ID': 'ECOLI:PUB-28637476',\n",
+      "                               '@detail': 'full',\n",
+      "                               '@frameid': 'PUB-28637476',\n",
+      "                               '@orgid': 'ECOLI',\n",
+      "                               'author': [{'#text': 'Parimi NS',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Durie IA',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Wu X',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Niyas AMM',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Eiteman MA',\n",
+      "                                           '@datatype': 'string'}],\n",
+      "                               'pubmed-id': {'#text': '28637476',\n",
+      "                                             '@datatype': 'string'},\n",
+      "                               'source': {'#text': 'Microb Cell Fact 16(1);114',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                               'title': {'#text': 'Eliminating acetate '\n",
+      "                                                  'formation improves '\n",
+      "                                                  'citramalate production by '\n",
+      "                                                  'metabolically engineered '\n",
+      "                                                  'Escherichia coli.',\n",
+      "                                         '@datatype': 'string'},\n",
+      "                               'year': {'#text': '2017',\n",
+      "                                        '@datatype': 'integer'}}},\n",
+      "              {'Publication': {'@ID': 'ECOLI:PUB-28744578',\n",
+      "                               '@detail': 'full',\n",
+      "                               '@frameid': 'PUB-28744578',\n",
+      "                               '@orgid': 'ECOLI',\n",
+      "                               'author': [{'#text': 'Wu X',\n",
+      "                                           '@datatype': 'string'},\n",
+      "                                          {'#text': 'Eiteman MA',\n",
+      "                                           '@datatype': 'string'}],\n",
+      "                               'pubmed-id': {'#text': '28744578',\n",
+      "                                             '@datatype': 'string'},\n",
+      "                               'source': {'#text': 'J Ind Microbiol Biotechnol '\n",
+      "                                                   '44(10);1483-1490',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                               'title': {'#text': 'Synthesis of citramalic '\n",
+      "                                                  'acid from glycerol by '\n",
+      "                                                  'metabolically engineered '\n",
+      "                                                  'Escherichia coli.',\n",
+      "                                         '@datatype': 'string'},\n",
+      "                               'year': {'#text': '2017',\n",
+      "                                        '@datatype': 'integer'}}}],\n",
+      " 'comment': {'#text': 'Isopropylmalate (IPM) isomerase catalyzes the second '\n",
+      "                      'step in leucine biosynthesis, converting '\n",
+      "                      '2-isopropylmalate to 3-isopropylmalate. LeuC and LeuD '\n",
+      "                      'are both required for the activity of IPM isomerase [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/4587614\">Yang74</a>, '\n",
+      "                      '<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/23636690\">Carlsen13</a>]. \\n'\n",
+      "                      '<p>\\n'\n",
+      "                      'Based on the IPM isomerase in <i>Salmonella '\n",
+      "                      'typhimurium</i> and cross-species complementation '\n",
+      "                      'studies with the <i>Salmonella</i> [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/7026530\">Fultz81</a>, '\n",
+      "                      '<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/374346\">Fultz79</a>] '\n",
+      "                      'and <i>S. cerevisiae</i> [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/23636690\">Carlsen13</a>] '\n",
+      "                      'enzymes, <i>E. coli</i> IPM isomerase is likely to be a '\n",
+      "                      'protein complex consisting of a 1:1 pairing of the of '\n",
+      "                      '<i>leuC</i> and <i>leuD</i> gene products [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/7026530\">Fultz81</a>, '\n",
+      "                      '<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/374346\">Fultz79</a>].\\n'\n",
+      "                      '<p>\\n'\n",
+      "                      'LeuC protein levels are increased in a '\n",
+      "                      'threonine-overproducing mutant [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/15104539\">Kim04</a>]; '\n",
+      "                      'LeuC leves are decreased after 30 minutes of excess '\n",
+      "                      'zinc stress [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/17122063\">Easton06</a>].\\n'\n",
+      "                      '<p>\\n'\n",
+      "                      '<i>leuC</i> insertion mutants, which cause leucine '\n",
+      "                      'auxotrophy, lead to increased antibiotic tolerance in '\n",
+      "                      'biofilms under leucine starvation conditions [<a '\n",
+      "                      'href=\"http://www.ncbi.nlm.nih.gov/pubmed/23300476\">Bernier13</a>].',\n",
+      "             '@datatype': 'string'},\n",
+      " 'common-name': {'#text': '3-isopropylmalate dehydratase subunit LeuC',\n",
+      "                 '@datatype': 'string'},\n",
+      " 'component-of': {'Protein': {'@frameid': '3-ISOPROPYLMALISOM-CPLX',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:3-ISOPROPYLMALISOM-CPLX'}},\n",
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+      "                                                                            'common-name': {'#text': 'SRI '\n",
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+      "                               'date': {'#text': '2013-09-26',\n",
+      "                                        '@datatype': 'date'}},\n",
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+      "                                                                            '@orgid': 'ECOLI',\n",
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+      "                                          'common-name': {'#text': 'Ingrid '\n",
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+      "             'dblink-relationship': 'unification'},\n",
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+      "             'dblink-db': 'PRINTS',\n",
+      "             'dblink-oid': 'PR00415',\n",
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+      "             'dblink-db': 'PRODB',\n",
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+      "             'dblink-relationship': 'unification'},\n",
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+      "             'dblink-db': 'ECOLIWIKI',\n",
+      "             'dblink-oid': 'b0072',\n",
+      "             'dblink-relationship': 'unification'},\n",
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+      "             'dblink-db': 'UNIPROT',\n",
+      "             'dblink-oid': 'P0A6A6',\n",
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+      "             'dblink-db': 'REFSEQ',\n",
+      "             'dblink-oid': 'NP_414614',\n",
+      "             'dblink-relationship': 'unification'}],\n",
+      " 'gene': {'Gene': {'@frameid': 'EG11576',\n",
+      "                   '@orgid': 'ECOLI',\n",
+      "                   '@resource': 'getxml?ECOLI:EG11576'}},\n",
+      " 'has-feature': [{'Feature': {'@ID': 'ECOLI:FTR0-1602312',\n",
+      "                              '@detail': 'full',\n",
+      "                              '@frameid': 'FTR0-1602312',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'accession-1': {'#text': 'PF00330.23',\n",
+      "                                              '@datatype': 'string'},\n",
+      "                              'evidence': {'Evidence-Code': {'@ID': 'ECOLI:EV-COMP-AINF',\n",
+      "                                                             '@class': 'true',\n",
+      "                                                             '@detail': 'full',\n",
+      "                                                             '@frameid': 'EV-COMP-AINF',\n",
+      "                                                             '@orgid': 'ECOLI',\n",
+      "                                                             'comment': {...},\n",
+      "                                                             'common-name': {'#text': 'Inferred '\n",
+      "                                                                                      'computationally '\n",
+      "                                                                                      'without '\n",
+      "                                                                                      'human '\n",
+      "                                                                                      'oversight',\n",
+      "                                                                             '@datatype': 'string'},\n",
+      "                                                             'parent': {...}},\n",
+      "                                           'Publication': {'@ID': 'ECOLI:PUB-26673716',\n",
+      "                                                           '@detail': 'full',\n",
+      "                                                           '@frameid': 'PUB-26673716',\n",
+      "                                                           '@orgid': 'ECOLI',\n",
+      "                                                           'author': [...],\n",
+      "                                                           'pubmed-id': {...},\n",
+      "                                                           'source': {...},\n",
+      "                                                           'title': {...},\n",
+      "                                                           'year': {...}}},\n",
+      "                              'feature-of': {'Protein': {'@frameid': 'LEUC-MONOMER',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'left-end-position': {'#text': '7',\n",
+      "                                                    '@datatype': 'integer'},\n",
+      "                              'parent': {'Feature': {'@class': 'true',\n",
+      "                                                     '@frameid': 'Pfam-Features',\n",
+      "                                                     '@orgid': 'ECOLI',\n",
+      "                                                     '@resource': 'getxml?ECOLI:Pfam-Features'}},\n",
+      "                              'right-end-position': {'#text': '457',\n",
+      "                                                     '@datatype': 'integer'}}},\n",
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+      "                              '@detail': 'full',\n",
+      "                              '@frameid': 'FTR0-1589756',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'alternate-sequence': {'#text': 'G',\n",
+      "                                                     '@datatype': 'string'},\n",
+      "                              'comment': {'#text': 'UniProt: In Ref. 5; '\n",
+      "                                                   'BAA21004/BAA21005..',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                              'evidence': [{'Evidence-Code': {...},\n",
+      "                                            'Publication': {...}},\n",
+      "                                           {'Evidence-Code': {...},\n",
+      "                                            'Publication': {...}}],\n",
+      "                              'feature-of': {'Protein': {'@frameid': 'LEUC-MONOMER',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'left-end-position': {'#text': '74',\n",
+      "                                                    '@datatype': 'integer'},\n",
+      "                              'parent': {'Feature': {'@class': 'true',\n",
+      "                                                     '@frameid': 'Sequence-Conflicts',\n",
+      "                                                     '@orgid': 'ECOLI',\n",
+      "                                                     '@resource': 'getxml?ECOLI:Sequence-Conflicts'}},\n",
+      "                              'right-end-position': {'#text': '74',\n",
+      "                                                     '@datatype': 'integer'}}},\n",
+      "                 {'Feature': {'@ID': 'ECOLI:FTR0-1589755',\n",
+      "                              '@detail': 'full',\n",
+      "                              '@frameid': 'FTR0-1589755',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'comment': {'#text': 'UniProt: Iron-sulfur '\n",
+      "                                                   '(4Fe-4S).',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                              'evidence': {'Evidence-Code': {'@ID': 'ECOLI:EV-COMP-HINF-ISM',\n",
+      "                                                             '@class': 'true',\n",
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+      "                                                             '@frameid': 'EV-COMP-HINF-ISM',\n",
+      "                                                             '@orgid': 'ECOLI',\n",
+      "                                                             'comment': {...},\n",
+      "                                                             'common-name': {'#text': 'Inferred '\n",
+      "                                                                                      'from '\n",
+      "                                                                                      'Sequence '\n",
+      "                                                                                      'Model',\n",
+      "                                                                             '@datatype': 'string'},\n",
+      "                                                             'parent': {...}},\n",
+      "                                           'Publication': {'@frameid': 'PUB-UNIPROT-2021-03',\n",
+      "                                                           '@orgid': 'ECOLI',\n",
+      "                                                           '@resource': '#ECOLI:PUB-UNIPROT-2021-03'}},\n",
+      "                              'feature-of': {'Protein': {'@frameid': 'LEUC-MONOMER',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'parent': {'Feature': {'@class': 'true',\n",
+      "                                                     '@frameid': 'Metal-Binding-Sites',\n",
+      "                                                     '@orgid': 'ECOLI',\n",
+      "                                                     '@resource': 'getxml?ECOLI:Metal-Binding-Sites'}},\n",
+      "                              'residue-number': {'#text': '410',\n",
+      "                                                 '@datatype': 'integer'}}},\n",
+      "                 {'Feature': {'@ID': 'ECOLI:FTR0-1589754',\n",
+      "                              '@detail': 'full',\n",
+      "                              '@frameid': 'FTR0-1589754',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'comment': {'#text': 'UniProt: Iron-sulfur '\n",
+      "                                                   '(4Fe-4S).',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                              'evidence': {'Evidence-Code': {'@class': 'true',\n",
+      "                                                             '@frameid': 'EV-COMP-HINF-ISM',\n",
+      "                                                             '@orgid': 'ECOLI',\n",
+      "                                                             '@resource': '#ECOLI:EV-COMP-HINF-ISM'},\n",
+      "                                           'Publication': {'@frameid': 'PUB-UNIPROT-2021-03',\n",
+      "                                                           '@orgid': 'ECOLI',\n",
+      "                                                           '@resource': '#ECOLI:PUB-UNIPROT-2021-03'}},\n",
+      "                              'feature-of': {'Protein': {'@frameid': 'LEUC-MONOMER',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'parent': {'Feature': {'@class': 'true',\n",
+      "                                                     '@frameid': 'Metal-Binding-Sites',\n",
+      "                                                     '@orgid': 'ECOLI',\n",
+      "                                                     '@resource': 'getxml?ECOLI:Metal-Binding-Sites'}},\n",
+      "                              'residue-number': {'#text': '407',\n",
+      "                                                 '@datatype': 'integer'}}},\n",
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+      "                              '@detail': 'full',\n",
+      "                              '@frameid': 'FTR0-1589753',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'comment': {'#text': 'UniProt: Iron-sulfur '\n",
+      "                                                   '(4Fe-4S).',\n",
+      "                                          '@datatype': 'string'},\n",
+      "                              'evidence': {'Evidence-Code': {'@class': 'true',\n",
+      "                                                             '@frameid': 'EV-COMP-HINF-ISM',\n",
+      "                                                             '@orgid': 'ECOLI',\n",
+      "                                                             '@resource': '#ECOLI:EV-COMP-HINF-ISM'},\n",
+      "                                           'Publication': {'@frameid': 'PUB-UNIPROT-2021-03',\n",
+      "                                                           '@orgid': 'ECOLI',\n",
+      "                                                           '@resource': '#ECOLI:PUB-UNIPROT-2021-03'}},\n",
+      "                              'feature-of': {'Protein': {'@frameid': 'LEUC-MONOMER',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'parent': {'Feature': {'@class': 'true',\n",
+      "                                                     '@frameid': 'Metal-Binding-Sites',\n",
+      "                                                     '@orgid': 'ECOLI',\n",
+      "                                                     '@resource': 'getxml?ECOLI:Metal-Binding-Sites'}},\n",
+      "                              'residue-number': {'#text': '347',\n",
+      "                                                 '@datatype': 'integer'}}},\n",
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+      "                              '@frameid': 'FTR0-1589752',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'comment': {'#text': 'UniProt: 3-isopropylmalate '\n",
+      "                                                   'dehydratase large subunit.',\n",
+      "                                          '@datatype': 'string'},\n",
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+      "                                                             'common-name': {'#text': 'Non-traceable '\n",
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+      "                                           'Publication': {'@frameid': 'PUB-UNIPROT-2021-03',\n",
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+      "                                                           '@resource': '#ECOLI:PUB-UNIPROT-2021-03'}},\n",
+      "                              'feature-of': {'Protein': {'@frameid': 'LEUC-MONOMER',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'left-end-position': {'#text': '2',\n",
+      "                                                    '@datatype': 'integer'},\n",
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+      "                                                     '@frameid': 'Chains',\n",
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+      "                              'right-end-position': {'#text': '466',\n",
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+      "                              '@frameid': 'FTR0-1440529',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              'comment': {'#text': 'UniProt: Removed.',\n",
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+      "                                           {'Evidence-Code': {...},\n",
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+      "                                           {'Evidence-Code': {...},\n",
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+      "                                                         '@resource': '#ECOLI:LEUC-MONOMER'}},\n",
+      "                              'parent': {'Feature': {'@class': 'true',\n",
+      "                                                     '@frameid': 'Cleavage-of-Initial-Methionine',\n",
+      "                                                     '@orgid': 'ECOLI',\n",
+      "                                                     '@resource': 'getxml?ECOLI:Cleavage-of-Initial-Methionine'}},\n",
+      "                              'residue-number': {'#text': '1',\n",
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+      "                                               'title': {'#text': 'Electronic '\n",
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+      "                                                                  'Ontology '\n",
+      "                                                                  'annotations '\n",
+      "                                                                  'created by '\n",
+      "                                                                  'ARBA '\n",
+      "                                                                  'machine '\n",
+      "                                                                  'learning '\n",
+      "                                                                  'models',\n",
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+      "                                                 '@frameid': 'EV-EXP-IPI',\n",
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+      "                                                                      'inferred '\n",
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+      "                                                                      'physical '\n",
+      "                                                                      'interaction\\n'\n",
+      "                                                                      '\\n'\n",
+      "                                                                      '<br>\\n'\n",
+      "                                                                      'The '\n",
+      "                                                                      'assertion '\n",
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+      "                                                                      '<ul>\\n'\n",
+      "                                                                      '    '\n",
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+      "                                                                      '2-hybrid '\n",
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+      "                                                                      '    '\n",
+      "                                                                      '<li> '\n",
+      "                                                                      'Co-purification\\n'\n",
+      "                                                                      '    '\n",
+      "                                                                      '<li> '\n",
+      "                                                                      'Co-immunoprecipitation\\n'\n",
+      "                                                                      '    '\n",
+      "                                                                      '<li> '\n",
+      "                                                                      'Ion/protein '\n",
+      "                                                                      'binding '\n",
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+      "                                                                      '</ul><p '\n",
+      "                                                                      'class=ecocomment>\\n'\n",
+      "                                                                      'This '\n",
+      "                                                                      'code '\n",
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+      "                                                                      'interest '\n",
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+      "                                                                      'molecule '\n",
+      "                                                                      '(or '\n",
+      "                                                                      'ion, or '\n",
+      "                                                                      'complex). '\n",
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+      "                                                                      'functions '\n",
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+      "                                                                      'protein '\n",
+      "                                                                      'binding '\n",
+      "                                                                      'or '\n",
+      "                                                                      'nucleic '\n",
+      "                                                                      'acid '\n",
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+      "                                                                      'binding '\n",
+      "                                                                      'assay '\n",
+      "                                                                      'is\\n'\n",
+      "                                                                      'simultaneously '\n",
+      "                                                                      'IPI and '\n",
+      "                                                                      'IDA; '\n",
+      "                                                                      'IDA is '\n",
+      "                                                                      'preferred '\n",
+      "                                                                      'because '\n",
+      "                                                                      'the '\n",
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+      "                                                                      'directly '\n",
+      "                                                                      'detects '\n",
+      "                                                                      'the '\n",
+      "                                                                      'binding. '\n",
+      "                                                                      'For '\n",
+      "                                                                      'both '\n",
+      "                                                                      'IPI and '\n",
+      "                                                                      'IGI, it '\n",
+      "                                                                      'would '\n",
+      "                                                                      'be '\n",
+      "                                                                      'good\\n'\n",
+      "                                                                      'practice '\n",
+      "                                                                      'to '\n",
+      "                                                                      'qualify '\n",
+      "                                                                      'them '\n",
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+      "                                                                      'the '\n",
+      "                                                                      'gene/protein/ion.',\n",
+      "                                                             '@datatype': 'string'},\n",
+      "                                                 'common-name': {'#text': 'Inferred '\n",
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+      "                                                                          'physical '\n",
+      "                                                                          'interaction',\n",
+      "                                                                 '@datatype': 'string'},\n",
+      "                                                 'parent': {'Evidence-Code': {'@class': 'true',\n",
+      "                                                                              '@frameid': 'EV-EXP',\n",
+      "                                                                              '@orgid': 'ECOLI',\n",
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+      "                                                          {...}],\n",
+      "                                               'pubmed-id': {'#text': '24561554',\n",
+      "                                                             '@datatype': 'string'},\n",
+      "                                               'source': {'#text': 'Nat '\n",
+      "                                                                   'Biotechnol '\n",
+      "                                                                   '32(3);285-90',\n",
+      "                                                          '@datatype': 'string'},\n",
+      "                                               'title': {'#text': 'The binary '\n",
+      "                                                                  'protein-protein '\n",
+      "                                                                  'interaction '\n",
+      "                                                                  'landscape '\n",
+      "                                                                  'of '\n",
+      "                                                                  'Escherichia '\n",
+      "                                                                  'coli.',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                               'year': {'#text': '2014',\n",
+      "                                                        '@datatype': 'integer'}},\n",
+      "                               'with': {'#text': 'UniProtKB\\\\:P30126',\n",
+      "                                        '@datatype': 'string'}}},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
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+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0016829'},\n",
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+      "                                                 '@orgid': 'ECOLI',\n",
+      "                                                 '@resource': '#ECOLI:EV-COMP-AINF'},\n",
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+      "                                               'author': {'#text': 'UniProt-GOA',\n",
+      "                                                          '@datatype': 'string'},\n",
+      "                                               'title': {'#text': 'Gene '\n",
+      "                                                                  'Ontology '\n",
+      "                                                                  'annotation '\n",
+      "                                                                  'based on '\n",
+      "                                                                  'UniProtKB/Swiss-Prot '\n",
+      "                                                                  'keyword '\n",
+      "                                                                  'mapping, '\n",
+      "                                                                  'accompanied '\n",
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+      "                                                                  'conservative '\n",
+      "                                                                  'changes to '\n",
+      "                                                                  'GO terms '\n",
+      "                                                                  'applied by '\n",
+      "                                                                  'UniProt.',\n",
+      "                                                         '@datatype': 'string'},\n",
+      "                                               'url': {'#text': 'http://www.geneontology.org/doc/GO.references',\n",
+      "                                                       '@datatype': 'string'},\n",
+      "                                               'year': {'#text': '2012',\n",
+      "                                                        '@datatype': 'integer'}},\n",
+      "                               'with': {'#text': 'SP_KW\\\\:KW-0456',\n",
+      "                                        '@datatype': 'string'}}},\n",
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+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0009082'},\n",
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+      "                                                 '@orgid': 'ECOLI',\n",
+      "                                                 '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                               'Publication': {'@frameid': 'PUB-GO_REF-0000043',\n",
+      "                                               '@orgid': 'ECOLI',\n",
+      "                                               '@resource': '#ECOLI:PUB-GO_REF-0000043'},\n",
+      "                               'with': {'#text': 'SP_KW\\\\:KW-0100',\n",
+      "                                        '@datatype': 'string'}}},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
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+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0008652'},\n",
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+      "                                                 '@orgid': 'ECOLI',\n",
+      "                                                 '@resource': '#ECOLI:EV-COMP-AINF'},\n",
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+      "                                               '@orgid': 'ECOLI',\n",
+      "                                               '@resource': '#ECOLI:PUB-GO_REF-0000043'},\n",
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+      "                                        '@datatype': 'string'}}},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
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+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0051536'},\n",
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+      "                                                 '@orgid': 'ECOLI',\n",
+      "                                                 '@resource': '#ECOLI:EV-COMP-AINF'},\n",
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+      "                              '@resource': 'getxml?ECOLI:GO:0046872'},\n",
+      "                  'evidence': {'Evidence-Code': {'@class': 'true',\n",
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+      "                                                 '@resource': '#ECOLI:EV-COMP-AINF'},\n",
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+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0003861'},\n",
+      "                  'evidence': [{'Evidence-Code': {'@class': 'true',\n",
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+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
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+      "                                'with': {'#text': 'InterPro\\\\:IPR004430|InterPro\\\\:IPR033941',\n",
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+      "                                'with': {'#text': 'EC\\\\:4.2.1.33',\n",
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+      "                                                'url': {...},\n",
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+      "                                'with': {'#text': 'RHEA\\\\:32287',\n",
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+      "                               {'Evidence-Code': {'@class': 'true',\n",
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+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-GO_REF-0000104',\n",
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+      "                                                '@frameid': 'PUB-GO_REF-0000104',\n",
+      "                                                '@orgid': 'ECOLI',\n",
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+      "                                                'title': {...},\n",
+      "                                                'url': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'#text': 'UniRule\\\\:UR000100515',\n",
+      "                                         '@datatype': 'string'}}]},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
+      "                              '@frameid': 'GO:0009316',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0009316'},\n",
+      "                  'evidence': [{'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-AS-NAS',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-AS-NAS'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-4587614',\n",
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+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@ID': 'ECOLI:EV-EXP-IGI-FUNC-COMPLEMENTATION',\n",
+      "                                                  '@class': 'true',\n",
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+      "                               {'Evidence-Code': {'@class': 'true',\n",
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+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-EXP-IGI-FUNC-COMPLEMENTATION'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-23636690',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-23636690',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': [...],\n",
+      "                                                'pubmed-id': {...},\n",
+      "                                                'source': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}}]},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
+      "                              '@frameid': 'GO:0005829',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0005829'},\n",
+      "                  'evidence': [{'Evidence-Code': {'@ID': 'ECOLI:EV-EXP-IDA',\n",
+      "                                                  '@class': 'true',\n",
+      "                                                  '@detail': 'full',\n",
+      "                                                  '@frameid': 'EV-EXP-IDA',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  'comment': {...},\n",
+      "                                                  'common-name': {...},\n",
+      "                                                  'parent': {...}},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-15911532',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-15911532',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': [...],\n",
+      "                                                'pubmed-id': {...},\n",
+      "                                                'source': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-EXP-IDA',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-EXP-IDA'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-18304323',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-18304323',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': [...],\n",
+      "                                                'pubmed-id': {...},\n",
+      "                                                'source': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-19054114',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-19054114',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': [...],\n",
+      "                                                'pubmed-id': {...},\n",
+      "                                                'source': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}}]},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
+      "                              '@frameid': 'GO:0051539',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0051539'},\n",
+      "                  'evidence': [{'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@frameid': 'PUB-GO_REF-0000002',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                '@resource': '#ECOLI:PUB-GO_REF-0000002'},\n",
+      "                                'with': {'#text': 'InterPro\\\\:IPR004430',\n",
+      "                                         '@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@frameid': 'PUB-GO_REF-0000043',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                '@resource': '#ECOLI:PUB-GO_REF-0000043'},\n",
+      "                                'with': {'#text': 'SP_KW\\\\:KW-0004',\n",
+      "                                         '@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-IC',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-IC'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-17102132',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-17102132',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': [...],\n",
+      "                                                'pubmed-id': {...},\n",
+      "                                                'source': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}}]},\n",
+      "                 {'GO-Term': {'@class': 'true',\n",
+      "                              '@frameid': 'GO:0009098',\n",
+      "                              '@orgid': 'ECOLI',\n",
+      "                              '@resource': 'getxml?ECOLI:GO:0009098'},\n",
+      "                  'evidence': [{'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-AS-NAS',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-AS-NAS'},\n",
+      "                                'Publication': {'@frameid': 'PUB-4587614',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                '@resource': '#ECOLI:PUB-4587614'},\n",
+      "                                'with': {'@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@frameid': 'PUB-GO_REF-0000002',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                '@resource': '#ECOLI:PUB-GO_REF-0000002'},\n",
+      "                                'with': {'#text': 'InterPro\\\\:IPR004430',\n",
+      "                                         '@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@frameid': 'PUB-GO_REF-0000043',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                '@resource': '#ECOLI:PUB-GO_REF-0000043'},\n",
+      "                                'with': {'#text': 'SP_KW\\\\:KW-0432',\n",
+      "                                         '@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@frameid': 'PUB-GO_REF-0000104',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                '@resource': '#ECOLI:PUB-GO_REF-0000104'},\n",
+      "                                'with': {'#text': 'UniRule\\\\:UR000100515',\n",
+      "                                         '@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@class': 'true',\n",
+      "                                                  '@frameid': 'EV-COMP-AINF',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  '@resource': '#ECOLI:EV-COMP-AINF'},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-GO_REF-0000041',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-GO_REF-0000041',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'url': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'#text': 'UniPathway\\\\:UPA00048',\n",
+      "                                         '@datatype': 'string'}},\n",
+      "                               {'Evidence-Code': {'@ID': 'ECOLI:EV-EXP-IMP',\n",
+      "                                                  '@class': 'true',\n",
+      "                                                  '@detail': 'full',\n",
+      "                                                  '@frameid': 'EV-EXP-IMP',\n",
+      "                                                  '@orgid': 'ECOLI',\n",
+      "                                                  'comment': {...},\n",
+      "                                                  'common-name': {...},\n",
+      "                                                  'parent': {...}},\n",
+      "                                'Publication': {'@ID': 'ECOLI:PUB-4570778',\n",
+      "                                                '@detail': 'full',\n",
+      "                                                '@frameid': 'PUB-4570778',\n",
+      "                                                '@orgid': 'ECOLI',\n",
+      "                                                'author': [...],\n",
+      "                                                'pubmed-id': {...},\n",
+      "                                                'source': {...},\n",
+      "                                                'title': {...},\n",
+      "                                                'year': {...}},\n",
+      "                                'with': {'@datatype': 'string'}}]}],\n",
+      " 'location': {'cco': {'@frameid': 'CCI-CYTOSOL-GN',\n",
+      "                      '@orgid': 'ECOLI',\n",
+      "                      '@resource': 'getxml?ECOLI:CCI-CYTOSOL-GN'}},\n",
+      " 'molecular-weight-seq': {'#text': '49.882',\n",
+      "                          '@datatype': 'float',\n",
+      "                          '@units': 'kD'},\n",
+      " 'parent': {'Protein': {'@class': 'true',\n",
+      "                        '@frameid': 'Polypeptides',\n",
+      "                        '@orgid': 'ECOLI',\n",
+      "                        '@resource': 'getxml?ECOLI:Polypeptides'}},\n",
+      " 'synonym': [{'#text': 'LeuC', '@datatype': 'string'},\n",
+      "             {'#text': '3-isopropylmalate dehydratase, large subunit',\n",
+      "              '@datatype': 'string'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "complex = 'LEUC-MONOMER'\n",
+    "\n",
+    "req_str = f'https://websvc.biocyc.org/getxml?id=ECOLI:{complex}&detail=high'\n",
+    "\n",
+    "r = s.get(req_str)\n",
+    "\n",
+    "o = xmltodict.parse(r.content)['ptools-xml']['Protein']\n",
+    "pp.pprint(o)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T23:23:23.482562Z",
+     "start_time": "2023-06-22T23:23:22.940684Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'ptools-xml': {'@ptools-version': '27.0',\n",
+      "                '@xml:base': 'http://BioCyc.org/apixml?fn=monomers-of-protein%26id=ECOLI:LEUC-MONOMER%26detail=LOW',\n",
+      "                'Protein': {'@ID': 'ECOLI:LEUC-MONOMER',\n",
+      "                            '@detail': 'low',\n",
+      "                            '@frameid': 'LEUC-MONOMER',\n",
+      "                            '@orgid': 'ECOLI',\n",
+      "                            'common-name': {'#text': '3-isopropylmalate '\n",
+      "                                                     'dehydratase subunit LeuC',\n",
+      "                                            '@datatype': 'string'},\n",
+      "                            'component-of': {'Protein': {'@frameid': '3-ISOPROPYLMALISOM-CPLX',\n",
+      "                                                         '@orgid': 'ECOLI',\n",
+      "                                                         '@resource': 'getxml?ECOLI:3-ISOPROPYLMALISOM-CPLX'}},\n",
+      "                            'gene': {'Gene': {'@frameid': 'EG11576',\n",
+      "                                              '@orgid': 'ECOLI',\n",
+      "                                              '@resource': 'getxml?ECOLI:EG11576'}},\n",
+      "                            'parent': {'Protein': {'@class': 'true',\n",
+      "                                                   '@frameid': 'Polypeptides',\n",
+      "                                                   '@orgid': 'ECOLI',\n",
+      "                                                   '@resource': 'getxml?ECOLI:Polypeptides'}},\n",
+      "                            'synonym': [{'#text': 'LeuC',\n",
+      "                                         '@datatype': 'string'},\n",
+      "                                        {'#text': '3-isopropylmalate '\n",
+      "                                                  'dehydratase, large subunit',\n",
+      "                                         '@datatype': 'string'}]},\n",
+      "                'metadata': {'PGDB': {'@orgid': 'ECOLI',\n",
+      "                                      '@version': '27.0',\n",
+      "                                      'species': {'#text': 'Escherichia coli',\n",
+      "                                                  '@datatype': 'string'},\n",
+      "                                      'strain': {'#text': 'K-12 substr. MG1655',\n",
+      "                                                 '@datatype': 'string'}},\n",
+      "                             'num_results': '1',\n",
+      "                             'query': 'fn=monomers-of-protein&id=ECOLI:LEUC-MONOMER&detail=LOW',\n",
+      "                             'service_name': 'apixml',\n",
+      "                             'url': 'http://BioCyc.org/'}}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "req_str = 'https://websvc.biocyc.org/apixml?fn=monomers-of-protein&id=ECOLI:LEUC-MONOMER&detail=low'\n",
+    "\n",
+    "r = s.get(req_str)\n",
+    "\n",
+    "o = xmltodict.parse(r.content)\n",
+    "pp.pprint(o)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-22T23:23:36.442761Z",
+     "start_time": "2023-06-22T23:23:36.387445Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/fbagd/comparison.ipynb b/notebooks/fbagd/old/comparison.ipynb
similarity index 100%
rename from notebooks/fbagd/comparison.ipynb
rename to notebooks/fbagd/old/comparison.ipynb
diff --git a/notebooks/fbagd/graph_work.ipynb b/notebooks/fbagd/old/graph_work.ipynb
similarity index 100%
rename from notebooks/fbagd/graph_work.ipynb
rename to notebooks/fbagd/old/graph_work.ipynb
diff --git a/notebooks/fbagd/test_environments.ipynb b/notebooks/fbagd/old/test_environments.ipynb
similarity index 100%
rename from notebooks/fbagd/test_environments.ipynb
rename to notebooks/fbagd/old/test_environments.ipynb
diff --git a/notebooks/fbagd/test_network_flow.ipynb b/notebooks/fbagd/old/test_network_flow.ipynb
similarity index 100%
rename from notebooks/fbagd/test_network_flow.ipynb
rename to notebooks/fbagd/old/test_network_flow.ipynb
diff --git a/notebooks/fbagd/test_environments_new.ipynb b/notebooks/fbagd/test_environments_new.ipynb
index 3831055f5..0a8579d0f 100644
--- a/notebooks/fbagd/test_environments_new.ipynb
+++ b/notebooks/fbagd/test_environments_new.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 1,
    "id": "8022bfb4-c69b-4cb7-a24c-fec017cc3661",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:00.320848Z",
-     "start_time": "2023-06-09T22:18:00.312957Z"
+     "end_time": "2023-06-26T19:06:22.109616Z",
+     "start_time": "2023-06-26T19:06:21.054198Z"
     }
    },
    "outputs": [],
@@ -24,7 +24,9 @@
     "import dill\n",
     "import requests\n",
     "import xmltodict\n",
-    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]\n",
     "\n",
     "sns.set(style='darkgrid', palette='viridis', context='talk')\n"
    ]
@@ -43,10 +45,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 2,
    "outputs": [],
    "source": [
-    "time = '10'\n",
+    "time = '50'\n",
     "date = '2023-06-09'\n",
     "experiment = 'fba-redux'\n",
     "entry = f'{experiment}_{time}_{date}'\n",
@@ -55,14 +57,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:01.110681Z",
-     "start_time": "2023-06-09T22:18:01.105158Z"
+     "end_time": "2023-06-26T19:06:22.114632Z",
+     "start_time": "2023-06-26T19:06:22.110641Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
@@ -75,14 +77,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:01.805666Z",
-     "start_time": "2023-06-09T22:18:01.439481Z"
+     "end_time": "2023-06-26T19:06:22.674606Z",
+     "start_time": "2023-06-26T19:06:22.112998Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 4,
    "outputs": [],
    "source": [
     "f = open(folder + 'agent_steps.pkl', 'rb')\n",
@@ -92,14 +94,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:02.171618Z",
-     "start_time": "2023-06-09T22:18:01.805882Z"
+     "end_time": "2023-06-26T19:06:23.487385Z",
+     "start_time": "2023-06-26T19:06:22.675431Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 5,
    "outputs": [],
    "source": [
     "metabolism = agent['ecoli-metabolism-redux']\n",
@@ -108,8 +110,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:02.230003Z",
-     "start_time": "2023-06-09T22:18:02.217867Z"
+     "end_time": "2023-06-26T19:06:23.489604Z",
+     "start_time": "2023-06-26T19:06:23.487666Z"
     }
    }
   },
@@ -124,13 +126,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 6,
    "outputs": [
     {
      "data": {
-      "text/plain": "{'ACET[p]': [0, 0, 0, 0, 0],\n 'AMMONIUM[c]': [816253, 824154, 859469, 865921, 881095],\n 'BETAINE[p]': [0, 0, 0, 0, 0],\n 'BUTANAL[c]': [0, 0, 0, 0, 0],\n 'CA+2[p]': [23, 162, 288, 396, 479],\n 'CARBON-DIOXIDE[p]': [-1978935, -2187468, -2252374, -2277523, -2299096],\n 'CARBON-MONOXIDE[p]': [-1, -5, -10, -14, -17],\n 'CH33ADO[c]': [0, 0, 0, 0, 0],\n 'CL-[p]': [1, 156, 277, 380, 461],\n 'CO+2[p]': [2, 6, 11, 17, 20],\n 'CPD-10774[p]': [0, 0, 0, 0, 0],\n 'CPD-108[p]': [-1, -5, -10, -14, -17],\n 'CPD-1772[c]': [0, 0, 0, 0, 0],\n 'CPD-239[p]': [0, 0, 0, 0, 0],\n 'CPD-534[p]': [0, 0, 0, 0, 0],\n 'CPD-560[p]': [-1, -186, -331, -454, -550],\n 'CPD-564[c]': [0, 0, 0, 0, 0],\n 'CPD0-2167[c]': [-2, -13, -26, -35, -41],\n 'D-ALANINE[p]': [0, 0, 0, 0, 0],\n 'D-LACTATE[p]': [0, 0, 0, 0, 0],\n 'ETOH[p]': [0, 0, 0, 0, 0],\n 'FE+2[p]': [2, 221, 389, 536, 649],\n 'FORMATE[p]': [0, 0, 0, 0, 0],\n 'GLC[p]': [767215, 841386, 906067, 928049, 955257],\n 'GLYCEROL[p]': [0, 0, 0, 0, 0],\n 'GLYCOLALDEHYDE[c]': [0, 0, 0, 0, 0],\n 'GLYCOLLATE[c]': [0, 0, 0, 0, 0],\n 'HYDROGEN-MOLECULE[c]': [0, 0, 0, 0, 0],\n 'HYPOXANTHINE[p]': [0, 0, 0, 0, 0],\n 'INDOLE[p]': [0, 0, 0, 0, 0],\n 'K+[p]': [-2, 5866, 10378, 14276, 17273],\n 'L-SELENOCYSTEINE[c]': [0, 2, 4, 6, 6],\n 'METOH[p]': [0, -1, -2, -3, -4],\n 'MG+2[p]': [1, 260, 462, 634, 768],\n 'MI-PENTAKISPHOSPHATE[p]': [0, 0, 0, 0, 0],\n 'MN+2[p]': [1, 27, 47, 65, 79],\n 'NA+[p]': [1, 6, 10, 15, 18],\n 'NI+2[p]': [1, 16, 27, 39, 46],\n 'OXYGEN-MOLECULE[p]': [2034608, 2149055, 2169775, 2168956, 2164999],\n 'PROTON[p]': [0, 0, 0, 0, 0],\n 'Pi[p]': [70436, 67160, 81767, 90661, 98134],\n 'S-ADENOSYL-4-METHYLTHIO-2-OXOBUTANOATE[c]': [0, -1, -2, -3, -4],\n 'SUC[p]': [0, 0, 0, 0, 0],\n 'SULFATE[p]': [13270, 16725, 16326, 17173, 17738],\n 'THYMINE[p]': [0, 0, 0, 0, 0],\n 'UNDECAPRENYL-DIPHOSPHATE[p]': [0, 0, 0, 0, 0],\n 'URACIL[p]': [0, 0, 0, 0, 0],\n 'UREA[p]': [0, 0, 0, 0, 0],\n 'WATER[p]': [-3973467, -1583451, 231207, 1898797, 2405331],\n 'XANTHINE[p]': [0, 0, 0, 0, 0],\n 'ZN+2[p]': [1, 16, 29, 41, 49]}"
+      "text/plain": "{'ACET[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'AMMONIUM[c]': [816253,\n  824154,\n  859469,\n  865921,\n  881095,\n  880175,\n  899327,\n  826879,\n  877100,\n  821741,\n  883986,\n  866915,\n  879898,\n  920315,\n  906384,\n  941731,\n  923156,\n  950892,\n  935505,\n  942198,\n  960592,\n  894699,\n  933352,\n  907005,\n  937700],\n 'BETAINE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'BUTANAL[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CA+2[p]': [23,\n  162,\n  288,\n  396,\n  479,\n  545,\n  598,\n  641,\n  661,\n  687,\n  696,\n  715,\n  727,\n  737,\n  755,\n  766,\n  781,\n  788,\n  799,\n  806,\n  813,\n  821,\n  814,\n  816,\n  813],\n 'CARBON-DIOXIDE[p]': [-1978935,\n  -2187468,\n  -2252374,\n  -2277523,\n  -2299096,\n  -2333612,\n  -2359294,\n  -2360849,\n  -2380093,\n  -2377430,\n  -2398409,\n  -2401014,\n  -2406075,\n  -2433275,\n  -2431991,\n  -2444870,\n  -2449074,\n  -2467528,\n  -2466263,\n  -2472228,\n  -2491605,\n  -2470017,\n  -2473566,\n  -2476245,\n  -2477271],\n 'CARBON-MONOXIDE[p]': [-1,\n  -5,\n  -10,\n  -14,\n  -17,\n  -18,\n  -21,\n  -22,\n  -23,\n  -24,\n  -24,\n  -25,\n  -25,\n  -25,\n  -26,\n  -27,\n  -27,\n  -27,\n  -28,\n  -28,\n  -28,\n  -28,\n  -28,\n  -29,\n  -28],\n 'CH33ADO[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CL-[p]': [1,\n  156,\n  277,\n  380,\n  461,\n  526,\n  575,\n  618,\n  637,\n  662,\n  670,\n  688,\n  700,\n  710,\n  727,\n  736,\n  752,\n  759,\n  770,\n  776,\n  782,\n  790,\n  783,\n  786,\n  782],\n 'CO+2[p]': [2,\n  6,\n  11,\n  17,\n  20,\n  23,\n  25,\n  27,\n  28,\n  30,\n  29,\n  30,\n  30,\n  30,\n  32,\n  32,\n  33,\n  33,\n  35,\n  34,\n  34,\n  34,\n  34,\n  34,\n  34],\n 'CPD-10774[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CPD-108[p]': [-1,\n  -5,\n  -10,\n  -14,\n  -17,\n  -18,\n  -21,\n  -22,\n  -23,\n  -24,\n  -24,\n  -25,\n  -25,\n  -25,\n  -26,\n  -27,\n  -27,\n  -27,\n  -28,\n  -28,\n  -28,\n  -28,\n  -28,\n  -29,\n  -28],\n 'CPD-1772[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CPD-239[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CPD-534[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CPD-560[p]': [-1,\n  -186,\n  -331,\n  -454,\n  -550,\n  -627,\n  -687,\n  -737,\n  -760,\n  -790,\n  -799,\n  -822,\n  -835,\n  -847,\n  -867,\n  -879,\n  -897,\n  -906,\n  -919,\n  -926,\n  -933,\n  -943,\n  -935,\n  -937,\n  -934],\n 'CPD-564[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'CPD0-2167[c]': [-2,\n  -13,\n  -26,\n  -35,\n  -41,\n  -46,\n  -52,\n  -54,\n  -56,\n  -58,\n  -59,\n  -59,\n  -61,\n  -61,\n  -65,\n  -66,\n  -66,\n  -66,\n  -68,\n  -68,\n  -69,\n  -70,\n  -69,\n  -70,\n  -68],\n 'D-ALANINE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'D-LACTATE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'ETOH[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'FE+2[p]': [2,\n  221,\n  389,\n  536,\n  649,\n  739,\n  812,\n  870,\n  896,\n  931,\n  943,\n  969,\n  985,\n  999,\n  1023,\n  1038,\n  1057,\n  1069,\n  1085,\n  1092,\n  1101,\n  1112,\n  1104,\n  1106,\n  1102],\n 'FORMATE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'GLC[p]': [767215,\n  841386,\n  906067,\n  928049,\n  955257,\n  974986,\n  997159,\n  976754,\n  1006624,\n  983157,\n  1019049,\n  1016114,\n  1022593,\n  1049309,\n  1046045,\n  1066408,\n  1061093,\n  1079737,\n  1074696,\n  1081093,\n  1094849,\n  1060890,\n  1078840,\n  1067741,\n  1080357],\n 'GLYCEROL[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'GLYCOLALDEHYDE[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'GLYCOLLATE[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'HYDROGEN-MOLECULE[c]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'HYPOXANTHINE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'INDOLE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'K+[p]': [-2,\n  5866,\n  10378,\n  14276,\n  17273,\n  19705,\n  21590,\n  23176,\n  23881,\n  24802,\n  25119,\n  25819,\n  26232,\n  26639,\n  27248,\n  27618,\n  28171,\n  28463,\n  28890,\n  29100,\n  29319,\n  29621,\n  29376,\n  29462,\n  29340],\n 'L-SELENOCYSTEINE[c]': [0,\n  2,\n  4,\n  6,\n  6,\n  8,\n  8,\n  8,\n  9,\n  10,\n  9,\n  10,\n  10,\n  10,\n  10,\n  11,\n  10,\n  11,\n  11,\n  11,\n  11,\n  11,\n  11,\n  11,\n  11],\n 'METOH[p]': [0,\n  -1,\n  -2,\n  -3,\n  -4,\n  -4,\n  -5,\n  -5,\n  -5,\n  -5,\n  -6,\n  -5,\n  -6,\n  -5,\n  -6,\n  -6,\n  -6,\n  -6,\n  -6,\n  -6,\n  -6,\n  -7,\n  -6,\n  -6,\n  -6],\n 'MG+2[p]': [1,\n  260,\n  462,\n  634,\n  768,\n  876,\n  959,\n  1030,\n  1062,\n  1102,\n  1116,\n  1148,\n  1166,\n  1184,\n  1211,\n  1227,\n  1252,\n  1265,\n  1284,\n  1294,\n  1303,\n  1316,\n  1306,\n  1309,\n  1304],\n 'MI-PENTAKISPHOSPHATE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'MN+2[p]': [1,\n  27,\n  47,\n  65,\n  79,\n  91,\n  99,\n  106,\n  109,\n  114,\n  115,\n  119,\n  120,\n  121,\n  125,\n  127,\n  129,\n  131,\n  133,\n  133,\n  134,\n  135,\n  134,\n  134,\n  135],\n 'NA+[p]': [1,\n  6,\n  10,\n  15,\n  18,\n  20,\n  23,\n  24,\n  24,\n  26,\n  26,\n  27,\n  27,\n  27,\n  28,\n  29,\n  29,\n  29,\n  30,\n  30,\n  31,\n  30,\n  31,\n  30,\n  30],\n 'NI+2[p]': [1,\n  16,\n  27,\n  39,\n  46,\n  53,\n  58,\n  62,\n  64,\n  67,\n  68,\n  69,\n  71,\n  71,\n  73,\n  74,\n  75,\n  77,\n  78,\n  78,\n  79,\n  80,\n  80,\n  79,\n  79],\n 'OXYGEN-MOLECULE[p]': [2034608,\n  2149055,\n  2169775,\n  2168956,\n  2164999,\n  2172935,\n  2188886,\n  2133919,\n  2168263,\n  2146433,\n  2180739,\n  2166280,\n  2178972,\n  2212392,\n  2200685,\n  2224984,\n  2219673,\n  2239282,\n  2227522,\n  2231438,\n  2251859,\n  2209197,\n  2224650,\n  2216657,\n  2233830],\n 'PROTON[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'Pi[p]': [70436,\n  67160,\n  81767,\n  90661,\n  98134,\n  101496,\n  108099,\n  92842,\n  105196,\n  99882,\n  110315,\n  106786,\n  112263,\n  118994,\n  117492,\n  126029,\n  123905,\n  127916,\n  124975,\n  125877,\n  128400,\n  118919,\n  123993,\n  119370,\n  126080],\n 'S-ADENOSYL-4-METHYLTHIO-2-OXOBUTANOATE[c]': [0,\n  -1,\n  -2,\n  -3,\n  -4,\n  -4,\n  -5,\n  -5,\n  -5,\n  -5,\n  -6,\n  -5,\n  -6,\n  -5,\n  -6,\n  -6,\n  -6,\n  -6,\n  -6,\n  -6,\n  -6,\n  -7,\n  -6,\n  -6,\n  -6],\n 'SUC[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'SULFATE[p]': [13270,\n  16725,\n  16326,\n  17173,\n  17738,\n  18317,\n  18681,\n  18770,\n  19097,\n  18900,\n  19534,\n  19392,\n  19624,\n  20053,\n  19680,\n  20245,\n  19773,\n  20662,\n  20209,\n  20502,\n  20850,\n  20064,\n  20524,\n  20620,\n  20212],\n 'THYMINE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'UNDECAPRENYL-DIPHOSPHATE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'URACIL[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'UREA[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'WATER[p]': [-3973467,\n  -1583451,\n  231207,\n  1898797,\n  3156435,\n  4193380,\n  4946897,\n  5844085,\n  5995275,\n  6542395,\n  6496191,\n  6851775,\n  6990345,\n  7037080,\n  7342363,\n  7392897,\n  7680402,\n  7720591,\n  7953426,\n  8021574,\n  8052271,\n  8377203,\n  8167460,\n  8271067,\n  8134592],\n 'XANTHINE[p]': [0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0,\n  0],\n 'ZN+2[p]': [1,\n  16,\n  29,\n  41,\n  49,\n  54,\n  60,\n  64,\n  66,\n  69,\n  70,\n  71,\n  72,\n  73,\n  76,\n  77,\n  78,\n  79,\n  80,\n  81,\n  82,\n  82,\n  82,\n  82,\n  82]}"
      },
-     "execution_count": 61,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -141,19 +143,19 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:03.125316Z",
-     "start_time": "2023-06-09T22:18:03.122246Z"
+     "end_time": "2023-06-26T19:06:23.501761Z",
+     "start_time": "2023-06-26T19:06:23.496061Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 7,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 1080x360 with 1 Axes>",
-      "image/png": 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4eGDnzp1YvXo15s2bhzZt2pi9FhFBVlaO2V+3OEqXLgUAyM62rboUrO/vSXuQgW7tPoXBkF3gNmXKlMbSzcNRwUrdWUUEK7/ejR9XHoK9vR0y/38LqRgFgeFvIbyPp021kNrae/wo1lc8rK94WF/xsL7iYX3Fw/qKh/X9Mw4OpQq9zrNqF9bs7GysWbMGs2fPhoODQ77bzJ07F76+vhg7diwAoGnTprh//77FAiSRpe3/LQ529oWHLzt7O+z/9TR8AuqVUFVaK7/ejY3f/6EJufrM3MfjbPz+DwBARF8vq9RGRERERNZj1QAZExODWbNmoVevXqhWrRomTJigWZ+YmIiEhASMGDFCs9zHxweRkZFITEzESy+9ZNaaRID79zPM+prFVanSUwBsry4F6/t7riXfU8NYQQz6LFxLvmeVmtNTM7Fh5UFkGfK/G6bPzMKGlQfRoq0rylewjQl/bO09fhTrKx7WVzysr3hYX/GwvuJhfcXD+v6ZKlUqoLCOZlYdA/n6669j586dGDRoEEqVKpVn/aVLlwAAr776qmZ59erVAQDx8fGWL5LIzJ6uXB6OZfNvcVeUcXTA05XLl1BFWof3nYO9feGHBnt7exzee66EKiIiIiIiW2HVAFm1alVUqVKlwPWpqakAgAoVKmiWly+fe2GdlpZmueKILMTN400YjcZCtzEajXBr+mYJVaR17056kVpI791JL6GKiIiIiMhW2MwsrPn5q/l9/qqVhMgWla9YFn4dG6KMY/49yMs4loZfx4ZW6x5q6y2kRERERGQ9Np3AKlasCABIT9e2dCgtj8p6osdNcFd3+AY1gEOZUnAs6wA7O8CxrAMcypSCb1ADBHd1t1pttt5CSkRERETWY9VJdP6KMvYxISEBOp1OXX7lyhXNeqLHjZ2dHUK6ecAvqAFiY+Jx93YaypZzhFvTN60+MY3SQhq5IRoGfd5HjZRxLA3foAZWr5OIiIiISp5NB8jq1avjxRdfxLZt29C6dWt1+fbt2/HKK6/g+eeft2J1RMVXvmJZ9VEdtjQDl9ICunV9FOzt7WHQZ6GMowOMRqPVW0iJiIiIyHpsOkACwMCBAzF27FhUqlQJnp6e2LVrFyIjIzF37lxrl0b0xLLlFlIiIiIish6bD5BBQUEwGAxYvHgx1q5di5deegkzZsyAn5+ftUsjeuLZagspEREREVmHzQTIoKAgBAUF5bsuLCwMYWFhJVwRERERERERmbLpWViJiIiIiIjIdjBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkTBAEhERERERUZEwQBIREREREVGRMEASERERERFRkZS2dgHmMGrUKBw/fhzlypUDAAQGBqJ79+7WLYqIiIiIiOgJ80QEyBMnTmDFihWoVq2atUshIiIiIiJ6Yj32AfLOnTu4desWJk2ahOTkZDRq1AijR4+Go6OjtUsjIiIiIiJ6ojz2YyBv3ryJxo0bY+rUqVi/fj1u376NTz/91NplERERERERPXEe+wCp0+mwcOFCVK1aFWXKlEHv3r3x66+/WrssIiIiIiKiJ85j34X1xIkTuHHjBlq1agUAEBGULv3Y/1lEVETpqZk4tDsOd2+noWw5R7h5vInyFctauywiIiKiJ9Jjn7SysrIwdepUNGjQAP/5z3+wdOlStGnTxtplEZGFiQjWLduPreujYG9vD70+C46ODli2aBf8OjZEcFd32NnZWbtMIiIioieKzXRhjYuLg7OzM1JSUvKs27x5M9q2bQsXFxf4+vpi48aN6rr69eujR48e6NKlC3x9fVGmTBn069evBCsnImtYt2w/IjdEI8uQA31mFiCAPjMLWYYcRG6Ixrpl+61dIhEREdETx05ExNpFXLx4ET169MD169exZ88ePPfcc+q6rVu3YsSIEejWrRs8PDywc+dOrF69GvPmzbNIS6OIICsrx+yvWxylS5cCAGRn21ZdCtZXPKzv70t7kIFu7T6FwZBd4DZlypTG0s3DUcEGurPa4j40xfqKh/UVD+srHtZXPKyveFhf8dhqfQ4OpQrtxWXVLqzZ2dlYs2YNZs+eDQcHh3y3mTt3Lnx9fTF27FgAQNOmTXH//n2LBUgisn37f4uDnX3h3VPt7O2w/9fT8AmoV0JVERERET35rBogY2JiMGvWLPTq1QvVqlXDhAkTNOsTExORkJCAESNGaJb7+PggMjISiYmJeOmll8xakwhw/36GWV+zuCpVegqA7dWlYH3Fw/r+vmvJ93K7rRbCoM/CteR7NlG3Le5DU6yveFhf8bC+4mF9xcP6iof1FY+t1lelSgUUNo2EVcdAvv7669i5cycGDRqEUqVK5Vl/6dIlAMCrr76qWV69enUAQHx8vOWLJCKb83Tl8nAsm3+vBUUZRwc8Xbl8CVVERERE9O9g1QBZtWpVVKlSpcD1qampAIAKFSpolpcvn3tRmJaWZrniiMhmuXm8CaPRWOg2RqMRbk3fLKGKiIiIiP4dbGYW1vz81fw+9vY2XT4RWUj5imXh17Ehyjjm3wu/jGNp+HVsiPIVrD+BDhEREdGTxKafA1mxYkUAQHp6uma50vKorCeif5/gru4AoD4H0qDPQhlHBxiNRvgGNVDXExEREZH52HSAVMY+JiQkQKfTqcuvXLmiWU9E/z52dnYI6eYBv6AGiI2Jx93baShbzhFuTd9kyyMRERGRhdh0gKxevTpefPFFbNu2Da1bt1aXb9++Ha+88gqef/55K1ZHRLagfMWy6qM6bG0WMyIiIqInjU0HSAAYOHAgxo4di0qVKsHT0xO7du1CZGQk5s6da+3SiIiIiIiI/lVsPkAGBQXBYDBg8eLFWLt2LV566SXMmDEDfn5+1i6NiIiIiIjoX8VmAmRQUBCCgoLyXRcWFoawsLASroiIiIiIiIhM8TkYREREREREVCQMkERERERERFQkf7sLa0ZGBp566ikAwN27d7F161bY29vD19cXTz/9tLnrIyIiIiIiIhtR5AD54MEDDB8+HA8ePMDatWuRlpaGjh074tq1axARfP7551i1ahVeeuklS9ZLREREREREVlLkLqyffvop/vjjDzRt2hQAsG7dOly9ehWjR4/GsmXLYG9vj08//dRSdRIREREREZGVFbkF8tdff0VERASGDBkCANi5cyeqVKmCnj17AgDCw8OxZMkSy1RJREREREREVlfkFsjbt2/jjTfeAACkpqbi2LFjcHd3V9c/88wzyMjIMH+FREREREREZBOKHCCrVauGxMREALmtjzk5OfD09FTXHzlyBP/73//MXiARERERERHZhiJ3YfXy8sLSpUuRlpaGLVu2oFKlSmjRogWuX7+Or7/+Gps2bcKAAQMsWSsRERERERFZUZED5OjRo5GRkYF169ahWrVq+OCDD1C2bFmcO3cOK1euRLt27dCnTx9L1kpERERERERWVOQAWaZMGUydOhVTp07VLHdycsKePXvw3//+1+zFERERERERke0o8hjIrl274uDBg3mWlylTBv/973/x66+/om3btmYtjoiIiIiIiGxHgS2QGRkZuHv3rvrvw4cPo3Xr1qhevXqebY1GI37//XckJSVZpkoiIiIiIiKyukIDZIcOHZCamgoAsLOzw8cff4yPP/443+1FRPNYDyIiIiIiInqyFBggK1eujE8++QSxsbEQESxcuBCtW7eGTqfLs629vT0qV67MLqxERERERERPsEIn0WnevDmaN28OALh69SrCwsJQp06dEimMiIiIiIiIbEuRZ2GdNm2aJesgIiIiIiIiG1fkAAkAv//+O37++WfcunULOTk5edbb2dlh6dKlZiuOiIiIiIiIbEeRA+TKlSvVZ0BWqVIFZcqUsVhRREREREREZHuKHCCXLVsGJycnfP3116hataolayIiIiIiIiIbVOQAee3aNYwbN47hkYjob0hPzcSh3XG4ezsNZcs5ws3jTZSvWNbaZRERERH9I0UOkC+//DJu3bplyVqIiJ4YIoJ1y/Zj6/oo2NvbQ6/PgqOjA5Yt2gW/jg0R3NUddnZ21i6TAZeIiIj+liIHyL59++Kjjz6Cj48P3njjDUvWRET02Fu3bD8iN0Qjy5ADIHfSMX1mFgAgckM0ACCkm4e1yntsAi4RERHZliIHyJiYGJQvXx4BAQF49dVXUbly5TwXF5yFlYgot1Vv6/qo/x8e8zLos7F1fRT8OjZA+QrWae2z9YBLREREtqnIAXLv3r0AgOeeew4ZGRlITk62WFFERI+zw/vOwd7eHkowy4+9vT0O7z0HL1+Xkivs/3scAi4RERHZpiIHyF9//dWSdRARPTHu3UlXW/MKYtBn4d6d9BKqSMvWAy4RERHZLntrF0BE9KR5unJ5OJZ1KHSbMo4OeLpy+RKqSMvWAy4RERHZriK3QHbt2rVI2y1btuwfF0NE9CRw83gTyxbtKnQbo9EIt6ZvllBFWkrALSxEWjPgEhERke0qcgtkUlJSnv8lJCTg+PHjOHz4MJKTk1GuXDlL1vqXli1bhqCgIKvWQERUvmJZ+HVsiDKO+d+jK+NYGn4dG1ptfKGbx5swGo2FbmPNgEtERES2q9hjIHNycrBr1y5MmDABvXr1Mlthf9fp06fx9ddf49lnn7VaDUREiuCu7gCgPibDoM9CGUcHGI1G+AY1UNdbgxJwIzdEw6DPzrO+jGNp+AZxAh0iIiLKq8gBsiClSpWCt7c3jh8/jlmzZmHNmjXmqOtvSU9Px/vvv48RI0Zg+fLlJf77iYgeZWdnh5BuHvALaoDYmHjcvZ2GsuUc4db0TZsIZrYccImIiMh2FTtAKl555RWsWLHCXC/3t3zwwQfo2bMnqlSpYpXfT0RUkPIVy8InoB4A4P79DCtX8ydbD7hERERkm8wSIA0GA3766SerBLgNGzbAwcEBfn5++OOPP0r89xMRPc5sNeASERGRbSr2LKwGgwHx8fF48OABBg8ebLbCiurnn3/GjRs3EBAQgIcPH+LGjRvo3r07vvvuuxKvhYiIiIiI6ElW5ACZlJSU7/JSpUrhtddeg7+/P7p06WK2wopqyZIl6n//8ccfmDFjBsMjERERERGRBRR7FlZziYuLQ3BwMHbt2oXnnntOs27z5s1YtGgREhMT8cILL6Bfv37o0KGDReshIiIiIiIiLTsRkb/zAzk5OTh58iSSk5NRpkwZ/O9//4Ozs3Oxirh48SJ69OiB69evY8+ePZoAuXXrVowYMQLdunWDh4cHdu7cidWrV2PevHlo06ZNsX5vfkQEWVk5Zn/d4ihduhQAIDvbtupSsL7iYX3FZ+s1sr7iYX3Fw/qKh/UVD+srHtZXPKzvn3FwKAU7O7sC1/+tSXR+++03TJ48GdevX4eSO+3s7PDf//4XkyZNQosWLf5WcdnZ2VizZg1mz54NBweHfLeZO3cufH19MXbsWABA06ZNcf/+fYsFSCIiIiIiIspfkQNkdHQ0Bg8ejCpVqmD48OF4/fXXISK4dOkSVq1ahSFDhmDZsmWoV69ekX95TEwMZs2ahV69eqFatWqYMGGCZn1iYiISEhIwYsQIzXIfHx9ERkYiMTERL730UpF/X1GI2N5MhJUqPQXA9upSsL7iYX3FZ+s1sr7iYX3Fw/qKh/UVD+srHtZXPKzvn6lSpQIKaYAseoBcsGABXnjhBaxbtw4VK1bUrOvSpQs6duyIRYsW4euvvy5yca+//jp27tyJKlWqYMOGDXnWX7p0CQDw6quvapZXr14dABAfH2/2AElERERERET5sy/qhidOnEBISEie8AgAFSpUQHBwMI4fP/63fnnVqlULfXZkamqq+vqmypcvDwBIS0v7W7+PiIiIiIiI/rkiB8i/Ymdnh6ysLHO9HADgr+b3sbc3W/lERERERET0F4qcwOrUqYN169bh4cOHedalpaVh7dq1qF27tlmLU1o709PT8/w+0/VERERERERkeUUeAzlo0CB07doV/v7+iIiIwCuvvAIA6iQ6169fx+TJk81anDL2MSEhATqdTl1+5coVzXoiIiIiIiKyvCIHyAYNGmDBggX48MMPMXPmTPXZICKCZ599FnPmzMFbb71l1uKqV6+OF198Edu2bUPr1q3V5du3b8crr7yC559/3qy/j4iIiIiIiAr2t54D2bJlS3h6euLUqVNISkoCALzwwgtwdnZG6dJ/66WKbODAgRg7diwqVaoET09P7Nq1C5GRkZg7d65Ffh8RERERERHl72+lvqtXr2LVqlXo06cPXFxcAABfffUVtm3bht69exc6o+o/FRQUBIPBgMWLF2Pt2rV46aWXMGPGDPj5+Zn9dxEREREREVHBihwgz507h7fffhtpaWnw9/dHpUqVAAAPHjzAypUrsXnzZqxateofP5cxKCgIQUFB+a4LCwtDWFjYP3pdIiIiIiIiMo8iz8I6e/ZslC9fHlu2bIGTk5O6fNSoUdiyZQscHBwwa9YsixRJRERERERE1lfkAHns2DF0795dnX3V1EsvvYSIiAhERUWZszYiIiIiIiKyIUUOkEajEZmZmQWuF5FC1xMREREREdHjrcgB0tXVFWvWrMGDBw/yrEtPT8fatWtRp04dsxZHREREREREtqPIk+gMGjQIERER8Pf3R7t27VC9enXY2dkhISEBW7Zswc2bNzFt2jRL1kpERERERERWVOQAWadOHSxZsgQzZszAt99+q1nn5OSEadOmoW7dumYvkIiIiIiIiGzD33oOZIMGDbB27VrcuXMHycnJMBqN+N///of//ve/lqqPiIiIiIiIbMTfCpCKypUro3LlyuauhYiIiIiIiGxYkSfRISIiIiIion83BkgiIiIiIiIqEgZIIiIiIiIiKhIGSCIiIiIiIiqSIgfIffv2FbreYDDgk08+KXZBREREREREZJuKHCB79+6NDz/8EJmZmXnWHTlyBAEBAVi8eLFZiyMiIiIiIiLbUeQAGRISglWrVqFDhw44fvw4ACAzMxMfffQR3n77bdy9exdTp061WKFERERERERkXUV+DuSUKVPg4+ODiRMnIjw8HJ06dcK+ffuQmJiIjh07YtSoUXj66actWCoRERERERFZU5EDJAB4eHjgxx9/VFsj7ezs8O6776JHjx6Wqo+IiIiIiIhsxN+ahXX//v0IDQ1FYmIivL298cILL+CTTz7B5MmTkZaWZqkaiYiIiIiIyAYUOUAOHz4cvXv3xsOHD7Fw4ULMnz8fP//8M7p06YI1a9agTZs22LJliyVrJSIiIiIiIisqcoCMjIxEhw4dsGXLFrRs2RIA8NRTT2HChAlYvnw5KlSogFGjRlmsUCIiIiIiIrKuIo+B/Prrr9G0adN819WvXx+bNm3CZ599ZrbCiIiIiIiIyLYUuQWyoPCocHR0xMiRI4tdEBEREREREdmmIrdAdu3a9S+3sbOzw9KlS4tVEBEREREREdmmIgfIpKSkPMuMRiPu3r0LvV6PF154AW+88YZZiyMiIiIiIiLbUeQA+euvv+a7PCcnB7t27cKECRPQq1cvsxVGREREREREtuVvPQcyP6VKlYK3tzdCQkIwa9Ysc9RERERERERENqjYAVLxyiuv4MyZM+Z6OSIiIiIiIrIxZgmQBoMBP/30E6pUqWKOlyMiIiIiIiIbVOxZWA0GA+Lj4/HgwQMMHjzYbIX9HfPmzcO2bdtgZ2eHZs2a4d1334W9vdkaV4mIiIiIiAjFnIUVyB0D+dprr8Hf3x9dunQxW2FFtWfPHuzfvx8//fQT7O3tER4ejl27dqF169YlXgsREREREdGTrNizsFpb8+bN0aRJEzg4OODOnTtITU1FpUqVrF0WERERERHRE+eJ6Ofp4OCAr7/+Gi1btkTVqlXh6upq7ZKIiIiIiIieOAW2QBY05rEwdnZ2WLp0abEK+qf69OmD7t27Y8yYMfjkk08wfvx4q9RBRERERET0pCowQBY05tHWnDt3DtnZ2ahZsyYcHBzg7++PxYsXW7ssIiIiIiKiJ06BAdJWxzw+6tKlS/jqq6+wevVqlCpVClu3bkXDhg2tXRYREREREdETp8AxkGfOnEFqamqJFRIXFwdnZ2ekpKTkWbd582a0bdsWLi4u8PX1xcaNG9V1bdq0QfPmzdGhQwd06NABFSpUwDvvvFNidRMREREREf1b2ImI5LeiRo0amDlzJtq1a6cuy87OxtGjR+Hk5ISKFSuarYiLFy+iR48euH79Ovbs2YPnnntOXbd161aMGDEC3bp1g4eHB3bu3InVq1dj3rx5aNOmjdlqUIgIsrJyzP66xVG6dCkAQHa2bdWlYH3Fw/qKz9ZrZH3Fw/qKh/UVD+srHtZXPKyveFjfP+PgUAp2dnYFri+wBTK/XJmamoquXbvi5MmTZikuOzsbK1euREhICPR6fb7bzJ07F76+vhg7diyaNm2KyZMnw9fXF/PmzTNLDURERERERFQ0RX4OpKKABst/JCYmBrNmzUKvXr1QrVo1TJgwQbM+MTERCQkJGDFihGa5j48PIiMjkZiYiJdeesls9QCACHD/foZZX7O4KlV6CoDt1aVgfcXD+orP1mtkff9cemomYmPicfd2GsqWc4Sbx5soX7GstcvSsOX9B7C+4mJ9xcP6iof1FQ/r+2eqVKmAQhog/36ANKfXX38dO3fuRJUqVbBhw4Y86y9dugQAePXVVzXLq1evDgCIj483e4AkIiLrExGsW7YfW9dHwd7eHnp9FhwdHbBs0S74dWyI4K7uhXavISIiIsuwaoCsWrVqoeuVSXwqVKigWV6+fHkAQFpammUKIyIiq1q3bD8iN0Qjy5ADIHdsiD4zCwAQuSEaABDSzcNa5REREf1rWTVA/pW/6i5rb1/gEE4iInpMpadmYuv6qP8fHvMy6LOxdX0U/Do2QPkK1u3Omp6aiUO742y6iy0REZE5FRogL126hKioKPXfSovg2bNnUbp0/j9qzmcwKjO9pqena5YrLY/mnAmWiIhsw+F95/7/DcKCZ6Wzt7fH4b3n4OXrUnKFmWAXWyIi+rcqNEB+8cUX+OKLL/IsnzFjRoE/ExcXV/yq/j9l7GNCQgJ0Op26/MqVK5r1RET05Lh3J13trloQgz4L9+6kF7qNJbGLLRER/VsVGCAHDRpUknXkq3r16njxxRexbds2tG7dWl2+fft2vPLKK3j++eetWB0REVnC05XLw7GsQ6EhsoyjA56uXL4Eq/rT49TFloiIyNxsOkACwMCBAzF27FhUqlQJnp6e2LVrFyIjIzF37lxrl0ZERBbg5vEmli3aVeg2RqMRbk3fLKGKtB6HLrYKjtEkIiJzs+lJdAAgKCgIBoMBixcvxtq1a/HSSy9hxowZ8PPzs3ZpRERkAeUrloVfx4aI3BANgz47z/oyjqXhG2S91r3HoYstx2gSEZGl2EyADAoKQlBQUL7rwsLCEBYWVsIVERGRtQR3dQcANQAZ9Fko4+gAo9EI36AG6nprsPUutsDjM0aTLaRERI8fmwmQRERECjs7O4R084BfUAPExsT/GTCavmn1cYW23sX2cRij+bi0kDLgEhHlxQBJREQ2q3zFsvAJqAcAuH8/w8rV5LL1LraPwxhNW28hfVwCLhGRNdhbuwAiIqLHTXBXd/gGNYBDmVJwLOsAOzvAsawDHMqUsnoXW1sfo6m0kOYXvoE/W0jT0zJLuLI/mQZcfWYWILkBN8uQg8gN0Vi3bL/VaiMisja2QBIREf1NttzF1tbHaNp6C+nj0AWYiMia2AJJRET0DyldbMN6NoOXr4tNBAo3jzdhNBoL3caaYzRtvYX0z4BbMCXgEhH9GzFAEhERPUGUMZplHPPvZFTGsTT8Oja0WthVWkgLY80WUlsPuERE1sYASURE9ISx5TGatt5CausBl4jI2jgGkoiI6Aljy2M0bX0WW1t/TAsRkbUxQBIRET2hbPExKADUFlDlMRkGfRbKODrAaDRavYXU1gMuEZG1MUASERFRibLlFlLAtgMuEZG1MUASERGRVdhqC6mtB1wiImtigCQiIiLKh60GXCIia+IsrERERERERFQkDJBERERERERUJAyQREREREREVCQMkERERERERFQkDJBERERERERUJAyQREREREREVCQMkERERERERFQkDJBERERERERUJAyQREREREREVCQMkERERERERFQkDJBERERERERUJAyQREREREREVCQMkERERERERFQkDJBERERERERUJAyQREREREREVCQMkERERERERFQkpa1dgDl89913WLduHezs7PDyyy9j6tSpeOaZZ6xdFhERERER0RPlsW+BjImJwbp167BmzRr8/PPPeO211zB79mxrl0VERERERPTEeewD5NNPP433338f5cuXBwDUrFkTycnJVq6KiIiIiIjoyfPYB8jXX38dbm5uAIC0tDR8/vnn8Pb2tnJVRERERERET57HPkAqrl+/jq5du6JevXro3LmztcshIiIiIiJ64jwRAfLMmTMIDQ1Fq1atMHnyZGuXQ0RERERE9ER67GdhTU5ORvfu3TFx4kS0bdvW2uUQERERERE9sWymBTIuLg7Ozs5ISUnJs27z5s1o27YtXFxc4Ovri40bN6rrvvvuO2RkZOCrr75CQEAAAgICMHLkyBKsnIiIiIiI6N/BTkTE2kVcvHgRPXr0wPXr17Fnzx4899xz6rqtW7dixIgR6NatGzw8PLBz506sXr0a8+bNQ5s2bcxei4ggKyvH7K9bHKVLlwIAZGfbVl0K1lc8rK/4bL1G1lc8rK94WF/xsL7iYX3Fw/qKh/X9Mw4OpWBnZ1fgeqt2Yc3OzsaaNWswe/ZsODg45LvN3Llz4evri7FjxwIAmjZtivv371ssQBIRERE9DtIeZODgnrO4ezsNlZ4pB3evGqjwn6esXRYRPeGsGiBjYmIwa9Ys9OrVC9WqVcOECRM06xMTE5GQkIARI0Zolvv4+CAyMhKJiYl46aWXzFqTCHD/foZZX7O4KlXKPRnYWl0K1lc8rK/4bL1G1lc8rK94WF/x2GJ9IoJ1y/Zj6/oo2NvbQ6/PgqOjA76csw1+HRsiuKt7oa0HJckW958p1lc8rK94bLW+KlUqoLBDiFXHQL7++uvYuXMnBg0ahFKlSuVZf+nSJQDAq6++qllevXp1AEB8fLzliyQiIiKyIeuW7UfkhmhkGXKgz8wCBNBnZiHLkIPIDdFYt2y/tUskoieYVQNk1apVUaVKlQLXp6amAgAqVKigWV6+fHkAQFpamuWKIyIiIrIx6amZ2Lo+CgZ9dr7rDfpsbF0fhfS0zBKujIj+LWxmFtb8/NX8Pvb2Nl0+ERERkVkd3nfuL69/7O3tcXjvuRKqiIj+bWw6gVWsWBEAkJ6erlmutDwq64mIiIj+De7dSc/ttloIgz4L9+6kF7oNEdE/ZdMBUhn7mJCQoFl+5coVzXoiIiKif4OnK5eHY9n8Z65XlHF0wNOVy5dQRUT0b2PTAbJ69ep48cUXsW3bNs3y7du345VXXsHzzz9vpcqIiIiISp6bx5swGo2FbmM0GuHW9M0SqoiI/m2s+hiPohg4cCDGjh2LSpUqwdPTE7t27UJkZCTmzp1r7dKIiIiISlT5imXh17EhIjdE5zuRThnH0vANaoDyFcpaoToi+jew+QAZFBQEg8GAxYsXY+3atXjppZcwY8YM+Pn5Wbs0IiIiohIX3NUdANTnQBr0WSjj6ACj0QjfoAbqeiIiS7CZABkUFISgoKB814WFhSEsLKyEKyIiIiKyPXZ2dgjp5gG/oAaIjYnH3dtpKFvOEW5N37Splsf01Ewc2h33Z30eb6J8Rdupj4j+GZsJkERERERUdOUrloVPQD0AwP37GVau5k8ignXL9qstpHp9FhwdHbBs0S74dWyI4K7usLOzs3aZDLhE/xADJBERERGZzbpl+xG5IRpZhhwAOQCgPnokckM0ACCkm4e1yntsAi6RrbLpWViJiIiI6PGRnpqJreuj8p3gBwAM+mxsXR+F9LTMEq7sT6YBV5+ZBUhuwM0y5CByQzTWLdtvtdqIHgcMkERERERkFof3nYO9feGXl/b29ji891wJVaT1OARcRXpqJn7ZdASrF/+O3yJPID3V+jURAezCSkRERERmcu9OutpdtSAGfRbu3UkvoYq0/gy4OQVuowRcL1+XkivMBLvYkq1jgCQiIiIis3i6cnk4lnUoNESWcXTA05XLl2BVf7L1gAvY/hhSInZhJSIiIiKzcPN4E0ajsdBtjEYj3Jq+WUIVaSkBtzDWDLiPUxdb+vdigCQiIiIisyhfsSz8OjZEGcf8O7mVcSwNv44Nrfa8SlsPuLY+hpQIYIAkIiIiIjMK7uoO36AGcChTCo5lHWBnBziWdYBDmVLwDWqA4K7uVqvN1gPu49DFlohjIImIiIjIbOzs7BDSzQN+QQ0QGxOPu7fTULacI9yavmm1YGZKCbDKJDUGfRbKODrAaDRaPeDa+hhSIoABkoiIiIgsoHzFsvAJqAcAuH8/w8rV/MmWA66bx5tYtmhXodtYs4stEcAASURERET/QrYYcJUutpEbovOdSKeMY2n4BjWwetClfzcGSCIiIiIiG2HLXWyJAAZIIiIiIiKbYctdbIkABkgiIiIiIptji11siQA+xoOIiIiIiIiKiAGSiIiIiIiIioQBkoiIiIiIiIqEYyCJiIiIiOhvSU/NxKHdcX9O8uPxJspX5CQ//wYMkEREREREVCQignXL9quPGdHrs+Do6IBli3bBr2NDBHd1h52dnbXLJAtigCQiIiIioiJZt2w/IjdEI8uQAyAHAKDPzAIARG6IBgCEdPOwVnkqtpBaDgMkERERERH9pfTUTGxdH/X/w2NeBn02tq6Pgl/HBlZ7ZuXj0kL6OAdcBkgiIiIiIvpLh/edg729PZSWx/zY29vj8N5z8PJ1KbnCTNh6C+njEnALw1lYiYiIiIjoL927k66GsYIY9Fm4dye9hCrSUlpIDfrsfNcrLaTpaZklXNmfTAOuPjMLkNyAm2XIQeSGaKxbtt9qtRUVAyQREREREf2lpyuXh2NZh0K3KePogKcrly+hirT+bCEtmNJCag2PQ8AtCgZIIiIiIiL6S24eb8JoNBa6jdFohFvTN0uoIi1bbyG19YBbVAyQRERERET0l8pXLAu/jg1RxjH/aVTKOJaGX8eGVptAx9ZbSG094BYVAyQRERERERVJcFd3+AY1gEOZUnAs6wA7O8CxrAMcypSCb1ADBHd1t1pttt5CausBt6ieuFlYDQYD+vXrh65du8LLy8va5RARERERPTHs7OwQ0s0DfkENEBsT/+djKJq+abWWR4XSQhq5ITrfcYZlHEvDN8h6jxhx83gTyxbtKnQbawbconqiWiDPnDmD8PBwHDlyxNqlEBERERE9scpXLAufgHoI69kMXr4uVg+PCltuIbX1LsBF9US1QH7//fcYMGAAFi9ebO1SiIiIiIiohNlyCykANcAqz4E06LNQxtEBRqPR6gG3qJ6oADl58mQAYIAkIiIiIvoXU1pIAeD+/QwrV/MnWw+4RfFEBUgiIiIiIiJbZ6sBtyieqDGQREREREREZDkMkERERERERFQkNhcg4+Li4OzsjJSUlDzrNm/ejLZt28LFxQW+vr7YuHFjyRdIRERERET0L2UnImLtIhQXL15Ejx49cP36dezZswfPPfecum7r1q0YMWIEunXrBg8PD+zcuROrV6/GvHnz0KZNG7PVICLIysox2+uZQ+nSpQAA2dm2VZeC9RUP6ys+W6+R9RUP6yse1lc8rK94WF/xsL7iYX3/jINDKdjZ2RW43iYCZHZ2NtasWYPZs2fDwcEB9+7dyxMgW7dujVq1amHu3LnqsmHDhuHs2bOIjIw0Wy02sDuIiIiIiIisprAAaROzsMbExGDWrFno1asXqlWrhgkTJmjWJyYmIiEhASNGjNAs9/HxQWRkJBITE/HSSy+ZpZbCdhYREREREdG/mU2MgXz99dexc+dODBo0CKVKlcqz/tKlSwCAV199VbO8evXqAID4+HjLF0lERERERPQvZxMtkFWrVi10fWpqKgCgQoUKmuXly5cHAKSlpVmmMCIiIiIiIlLZRAvkX/mrcYn29o/Fn0FERERERPRYeyySV8WKFQEA6enpmuVKy6OynoiIiIiIiCznsQiQytjHhIQEzfIrV65o1hMREREREZHlPBYBsnr16njxxRexbds2zfLt27fjlVdewfPPP2+lyoiIiIiIiP49bGISnaIYOHAgxo4di0qVKsHT0xO7du1CZGSk5rmQREREREREZDmPTYAMCgqCwWDA4sWLsXbtWrz00kuYMWMG/Pz8rF0aERERERHRv4Kd/NUUp0RERERERER4TMZAEhERERERkfUxQBIREREREVGRMEASERERERFRkTBAEv0L5eTkWLsEIiIiInoMMUBakdFotHYJBbpz5461SyjUnTt38ODBA2uX8Vg6c+YM5s+fb/PvMUPuk89W53Cz1boeF8r+4358svEY/c+lpaVZuwSiYmGAtILz58/jwoULsLe3t8kQeeLECbzzzjtYsWKFtUvJ15EjR9C5c2ds3LjRJg/CJ0+eRGRkpLXLyFd0dDQ6dOiAJUuWYOnSpbh37561S8rjwoULSEtLQ6lSpWz2AuXs2bPYvXs3srOzrV3KY0n53trZ2dlkyHj48KG1SyjUiRMnsGvXLmuXUaCbN28CsN3398yZMzhw4IC1yyhQYmIizpw5Y+0yChQXF4fz58/b9DHaYDBYu4QCnThxAu+99x727Nlj7VLydfHiRURHR1u7jEJlZGRYu4QC2frx2VwYIEtYdHQ0OnbsiJ49e+LMmTM2FyJPnDiBTp064dSpU1izZg3Wrl1r7ZI0jh49irfffhv379/H/PnzsXnzZpsKkYcPH0ZwcDCGDx+OH3/80drlaNy9exdz585F9+7d0bp1a6xduxbffPONTYXImJgY+Pv7w8/PDw8ePLDJC5SoqCgEBARg4MCB2L17t019f4HcAL5//35rl1GgEydO4N1338Vvv/0GwPZCxokTJxAUFGSzF3dRUVHo1KkTBg4ciK1bt1q7nDyOHTuGZs2aYdq0aQBs7/2Njo5W958tvsdKfbNmzbLJEBkdHY3AwEC0a9cOcXFxNnmMPnnyJObNm4fLly9bu5Q8jhw5gk6dOmHXrl1YtGgRDh48aO2SNKKjoxEaGoqJEyfi8OHD1i4nX6dOncKECRNw/Phxa5eSh60fn82Jz4EsQcnJyWjXrh2cnJzw8OFDZGVlYfbs2XBycoLRaIS9vfXz/DfffAMHBwc0adIEo0ePRmZmJnr16oWQkBBrlwYA2LFjB06dOoVevXrhgw8+wM6dOzF27Fj4+/ujQoUKVq0tNTUVvr6+aNCgARwdHfHTTz9hypQpCA4OtmpdpuLi4lCjRg0AwOjRo7F//34EBQWhd+/eePrpp61aW2JiIlq3bg03NzckJSXBwcEBP/zwAypVqoScnByUKlXKqvWJCO7du4fGjRujXbt2SEtLw+HDhzFt2jS0atXKJr6/0dHR6NevH5555hm8//77aNasmbVL0jhy5Ai6dOkCAHBxccGwYcPQpEkTALn7187Ozprl4ejRo+jcuTOeeuopVK1aFe+//z6aNm1q1ZpM3b17F0FBQahZsyYAYNeuXZg9ezbatm1r5cpyxcXFITAwEK+88goSExPRrVs3vPvuuwBs4/29fv06goOD8cYbb8BgMCApKQmTJk2Cl5eXVetSXLx4EWFhYXjhhRdw8+ZN1K9fHwMGDICTk5O1SwOQu//atGmDBg0a4P79+7hw4QKWL18OZ2dnmzhGA7kX8G+//TYAoFOnTujbty9efPFFK1eVS0QwefJkPP/882jUqBF69OiBV199FaNGjULjxo2tXR4SExMRFhaGl19+GWlpaXjqqacwYsQIvPXWW9YuTRUTE4Pw8HAAgLe3N/r27YtatWpZuapctn58NjuhEpOUlCR79+6V7Oxs2bRpk7Rr1078/Pzk9OnTIiKSk5Nj5Qpza9Dr9SIikpCQIAEBAeLj4yM//PCDlSv7U3Z2toiIZGVlyYgRI8TFxUW+//57SU1NtXJlufvMYDBIQkKCjBkzRpycnGTdunXWLkuMRqP638r+ExEZMWKENG7cWD755BO5e/euFSr7082bN2XPnj2SmpoqmzdvFk9PT/H29pZ79+6JiLZuazpz5ow8fPhQrl27Jv3795d69erJL7/8YvXvb1JSkjRt2lQiIiIkICBAAgMDZffu3VatyZTRaJQPP/xQvvzySzlx4oTUq1dPAgMDZf/+/ZptrCUrK0uWLFkiCxYskO3bt0toaKh4eXnJ77//brWaHqXX6+XMmTMiInL69GkZMGCA6HQ62bx5s5Ury3X8+HFZuXKlXLp0SaZNmyY6nU6mT5+urrfm+ysicvv2bYmKihIRkd9//126dOkizZs3l19//dWqdSni4uLkxx9/FBGR7777Ttzc3GTw4MESFxdn3cL+v9TUVImJiRG9Xi8HDhyQ4OBgqVu3rpw8eVJErHuMNhqNotfrJTQ0VKZPny4rVqwQJycnGTdunCQmJlqtrkcZDAbJyMgQkdz329XVVYKCguTAgQNWrkwkMTFR/S5s3rxZ/Pz8JCQkRA4ePGjlynJlZGTIyJEj5fPPP5dly5aJm5ubDBgwQGJjY61dmojY/vHZ3BggS5jpAXbjxo3i7+8vfn5+6gnCmhehpif3rKwsEbGtEGm6b5T/1uv1NhciFZcvX5b33nvPJkLkoyd2031pSyFSqSs7O1t+/vlnmwqR+V38JicnyzvvvGMTITIlJUUNjDt27JB27drZXIjMyspSL57OnTtncyHS9Pixe/dumwyRpvvn5MmTNnWRYjQa1fquXbtmkyHS9Du6Z88emwuRpsc40xCp3Gi2NtP9t3//funYsaPNhEgRkfv376s3wdesWWNTIdJ03yjfg9OnT9tUiDR9f01DpC3UJiJy/fp1dT+uWrXK5kKkLR+fzY0BsoSYfilNDyI//vijTYTIwg76V65csXqIfLQ+0y+pXq+X4cOHWzVEFvSexcfHWz1EKvsuPT1dvvzySxk7dqwsXLhQjh07pm4zcuRIq4ZI0/2nvLdZWVny008/2UyILOg9TkpKspkQafq7bS1Emtam3KA6d+6c1K9fXzp06CD79u1T11szZJjW+dtvv9lMiCzocxUbGyv9+/e3+kVKQTdYbCVEFnQO3r17t02ESNP6DAaD+t+2EiLzO0aLiOzbt89mQmR+nytbCZHKMS8nJ0cePnwoer1efZ9Pnz4tdevWtWqILOjz9/PPP9tciDStdeXKlTYRIm39+GwJDJAlQDlwZGdnS1ZWlqSlpWnWWztEKgf7hw8fyjfffCOffPKJLFiwQK5fvy6ZmZkiktuaZq0QaRqAFi5cKGPHjpUZM2aoXZFEcvextUKk8v4qXVcvXLighh0RkYsXL1olRJq2BqSlpYmfn5+0b99eOnbsKP7+/vLHH39otrdWS6TpiVWv16t3j0Vy9+mmTZusHiJN3+PExERJTEyUO3fuqOut3RJZ0MnfVkJkYe+XLYTIwurbtWuX1UOk6efv5s2bcuXKFXWZSG7XUWtepBS2/xITE60eIpV9pZx/b9y4oVlv7RBpegwUkTzXCEuWLLFqiDS9hklNTdUc+0RyuwNbO0QW9vtWr15t1RCp1JaWliYjR46UsLAwadeunaxZs0YePnwoItbtzmr6/mZnZ+d5fzdt2mT1EFnY+7tixQqrhkhbPz5bCgOkhSknhNTUVBk8eLC0b99emjRpIjNmzNBcwJdkiFRO3KYBIz09Xby9vaV169bi6ekp9evXl+bNm8vq1avVi/aSbok0vcBIT0+XNm3aiLe3t7Rt21a8vLxEp9PJF198oR6ArdGdVakxNTVVwsPD1bpCQkLk66+/VrcryRCZkJAgt2/fVv+dk5MjY8aMkc6dO8vly5dFRDQB98GDB+p/l3SINL1gGjZsmAQFBUnr1q1l9uzZcuLECRGxfog0/Q537dpVWrVqJa6urhISEqI5GVirJdL04liv18vNmzc163/55RerhkjlfcrIyJC1a9fKkiVL5IcfftAcf86ePWu1EKnsv4cPH8ratWtlwYIFsnXrVrl48aK6zc6dO60WIk2PMd26dVOPMT169JAVK1ao21nrIsX0Bt8nn3wiQ4YMkYkTJ8qWLVvUmxkJCQlWC5Gm39++fftK69atpVatWjJq1CjZvn27up21QqTpMfC9996T0NBQCQgIkG+//VYSEhLU7awVIk33X79+/aRNmzbi6uoq7777ruzYsUPdbu/evVYLkcp3ODMzU/bv3y+//fabxMTEaLaxVohUPuMPHz4UPz8/CQ0NlU8//VSGDRsmv/32m4j8uZ+UlsjAwMASC2qm7++gQYPE399fGjduLB9//LFER0er21kzRJq+vzt27JBNmzbJwYMHNedYa4VIWz8+WxIDpIVkZWVpxun5+vpKaGioTJkyRT744AOpX7++BAYGaj5IpiHSkhPr3L9/X0T+PGjl5OTIF198IeHh4RIfHy+3b9+WK1euSPfu3cXV1VXWrVunuRCwdIi8ffu25u6NiMiyZcskODhYLly4ICIiFy5ckJkzZ4qTk5N888036nYlGSJN79qFhIRIeHi4rF27Vn788Ufp3r271KpVS6ZOnapuXxLdWaOioqR58+ayY8cO9cCWnZ0tHTt2lClTpqjbKe/9jRs3ZPbs2eqJTERk1KhR0rhxY5kzZ44miJqbUoNerxcfHx8JDg6WyZMny/Dhw8Xd3V38/PzUMGEwGKzSndX0zmJISIh07txZVq1aJV9//bVERESITqeTJUuWqNuXdIg0Pfn3799f2rRpI25ubjJx4kQ5dOiQup21QqRpC7iPj494eXlJvXr1xNnZWdq3by979+5VbwCVVEuk6euZnvz9/PzEw8ND3Nzc1JtAa9asUbe1Rkuk6YRhHTt2lIiICFm2bJn88MMPEhgYKO7u7jJr1ix1+9jY2BIdc/PoDcg2bdpIly5dxMPDQ5o0aSIjRoxQexSUdEuk6TnYYDBI+/btpUuXLjJ//nz58ssvxcPDQ3x8fOS7775Tf6YkQ2ROTo76t2dmZoqvr6907NhRRowYIYMHDxadTifdu3eXw4cPqz+zdOnSEg2RyufPYDCIv7+/dO7cWRYsWCBz5syRli1biqenp3z//ffq9tbozmr6HQ4ICJBmzZqJTqeTOnXqyOjRozU31EqqO+uZM2fU46xS3+effy7t27fP9/fevn1bPdfGxcWVSIjMzs7WXKP6+/ur16gff/yx1KpVSwICAuSnn35Sf8Ya3VlN39927dqJh4eH1K5dW3Q6nYwYMULTE62kQ6StH58tjQHSzM6dO6f5t16vl/3790uPHj3k0qVL6pdh9+7d0qFDBwkODpYjR46o22/cuFGdndUSLZFRUVGaIPbw4UOZP3++9OjRQyZOnJhn+27dukmLFi00rVRKiPT395fly5ebrTYRkcOHD0tYWJj65c/IyJDBgwfLsGHDZMyYMZptU1NTZfr06VKjRg05fvy4ulyZnbV+/fqydOlSNTCbQ2xsrERFRWm6/Z46dUpGjBihaVFOSEiQ6dOni6urq+YC5fLly+rsrBs2bDBbXSK5rZyurq7Sq1cvdVlWVpbcunVLPD09ZcaMGeoyRXJysnh6empaS0VyWyJr1aol8+fPN+vn7/Lly5rfr9frJS4uTnr27Cnx8fHq8i1btkhISIi0a9dOTp06JSIFT6xj7pAWFxcnBw8e1LScXbp0SXr37q25mIuPj5cPPvhAdDqdbNu2TV2enJyszs66Y8eOPDdDzOHRk3/79u0lLCxMZsyYIXPmzBFXV1fx8/OT9evXqz9TUt1Zle53pt3yPv74Y+nSpYucOXNGkpOTZd++fRIYGCjNmzfXzPBXEiHSdL8p//72228lPDxczp07J6mpqXLo0CEJDAwUb29vzc2ekhgTeeLECfX4LJL7+Tt48KAMHDhQjh49qi5XejU0a9ZM1q5dqy43nbhhy5YtZq9P6ZmgfD+ys7PlwIED0rlzZ7XFLD09XaZNmyZNmzaV0aNHqzcgr169avEQefLkSc35Sq/Xy+7du6Vnz56a2Uzj4uIkIiJC/Pz8NN8H04l1LPE9iY+P19zYzMrKks2bN0unTp00rd779++XWrVqSa9eveT69evqckvPznr27FlNF8bMzEw5ceKE9OvXT51hUiT3WqJPnz7SsmVLzX7av3+/Ojurcuw29zFa+Twp/5+TkyODBw+WiIgIOXjwoMTExMjnn38uDRs2lB49emh601g6RB45ckR0Op2sX79eM5xgzJgxEhwcrA4LUj7zd+/elRkzZsjMmTPV7c+cOSN169aV0NBQs9/IOHv2rGZ/GAwG2b59u4SHh2uOO6dOnZLWrVtLYGCgJoyZTqxjepPSnExvXCj/njRpkkRERMjRo0fl1KlTsm7dOnF1dZXQ0FDNNbSlx0Ta+vG5JDFAmlFMTIzodDr1w2IwGGTMmDHi6ekpzZo1U09qpiGybt26snDhQs3rbNq0Sdq3by8eHh6aD2pxxcXFiU6nk3nz5ql1xMbGiqurq+h0OpkwYYK6rXJxdenSJalXr54aFJWLwjNnzohOp5Pw8HCzBbS4uDhxdnaWUaNGqfUpdxWVLgGmB2SR3AvO5s2bq61ApgflevXqib+/v9nqO3r0qNriZDQaJTs7Ww0QNWvWVEOscrK8cuWKRERESPfu3TXj+hISEmT06NF5gsc/pfzNO3bsEH9/f2ndunWeu4Njx44VNzc3uXr1qohoQ2RgYKB6MaccuCdOnCg1a9bUhLriUr4fSrexrKwsGTx4sDRs2FA8PT3zjLvYsmWLNGnSRL788kt1WU5OjmzevFm8vLzE3d1dc6FoDsp7vHbtWsnOzhaj0SjDhw8XnU4n9evXl/Pnz2u2v3TpknTu3Fl69uwp6enp6vKrV69Kv379pEaNGrJnzx6z1RcXF6e5mDQYDPLLL79Ily5d5OzZs+ryixcvip+fn/j7+2smSzINkeasSxEdHS1DhgyR5ORkEckNEt98840MHDhQPv30U822169flw4dOkhgYKBm+fnz56V+/foSHBwsu3btMmt9Bw4ckJCQEPU4kpmZKR999JGEhobKtGnTNNuePn1aOnToIOHh4er3RuTP2VlbtWpl9oChXHwqN3Sys7Plww8/lJo1a0qtWrXUz5/y/b148aK0a9dOBg4cqHmdU6dOqd2lTHsXFNfhw4elbt266nlJr9dL3759xd/fX3r27KlpbUpPT5ePPvpImjZtqtlPyuysNWvW1PTQMAflGPPtt9+KSO5+Gj9+vDRt2lTc3NzUVh5l/50+fVoaN24s77//vuZ1lEd8uLi4mLWlJSoqSho1aiQ///yz+tiJ/v37S0REhISEhKjbKZ/PvXv3ik6nk1WrVmleZ+nSpdKkSZM8obi4lM+f0rsoKytL3n33XXFxcZEGDRpovgciIn/88Yd4eXnJhx9+qFmuPOJDp9PlOWYW17Fjx2ThwoXqsT89PV127twpgwYN0tzsycjIkF9//VUaNGgg48aN07yGEiInTpwoly5dMltt165dE3d3d/H09FRbPpXP2rBhw6Rt27bqtqbflf79+6vHQeX6QTkXDR06VHNuKY7o6GipUaOGrFq1SoxGoxgMBhk5cqS0b99eWrZsqX7ulOuVU6dOSc2aNWXu3Lma19myZYv4+/uLt7e3ppuruWocO3asOlt3RkaGbNiwQcLDwzW9fURyPwt16tTJ07igzM46ePBgTagrLls/Ppc06z/5+gmRlJSELl26wMPDA76+vgCA0qVL44UXXkCFChWQnp6O+/fvAwCys7MhImjevDnatGmDn3/+GQaDAVlZWQCANm3aoHTp0sjIyECZMmXMUl9WVha2b98OALh37x4AwM7ODrVq1cLSpUvx4osv4rfffsPvv/8OAOrvLV++PBwdHZGTk6P+TXq9Hrt374a9vT3ef/99/Oc//ylWbSICANi5c6f6+ikpKbCzs0OFChWwZMkSeHp6IiYmBocPH1a3B4A33ngDpUqVwp07d9S/KTs7G4sWLUJWVhY++eQTs9R3584dhIWF4fnnn4e7uzvs7OxQqlQpBAQEoEOHDjAajbh06RIAwGg0QkTw8ssvo127djh8+DBu376tvl61atXw4MEDlC5dGv/3f/9XrNoAICUlBQDQqlUrDB06FM888wzGjx+PgwcPqtuEh4fj6aefRteuXXH16lWULl0aAHDhwgVkZWXh5ZdfBgCUKlUKJ0+eRHx8PNauXYtXXnml2PUBwOXLl9GlSxe4u7vDzc0NAGAwGFCrVi288MILyM7Ohr29vbocAPz8/FC3bl1ERkZCRGA0GmFvbw8PDw/897//xa1bt9TPsjlcu3YN3bt3R/Xq1eHm5qY+FLtPnz5o0aIF0tLScPbsWQB/fmZfffVVNGnSBCdPnkRmZqb6WhUrVoSDgwMAoHr16mapLyYmBkFBQfjll18gIsjKysLYsWOxYMECXLt2TX2vDAYDXnvtNSxYsABXrlxBZGSk+hqtWrXCkCFDYDQaMXnyZM1npLhOnz6N8PBw/N///R+ef/55iAhOnTqFTz75BDt37tRsKyL473//i2HDhuHSpUvYsWMHgNxj4//93/9h8eLFiI2NxcaNG/Hw4UOz1HfixAn07t0bb7zxhvr+7dmzB/v378eZM2dQuXJlALnHSqPRiBo1amD06NGIjo7G+fPn1ddp3rw5evTogZSUFMyaNQsZGRmaY9I/dfv2bXTu3BmvvfYafHx8AOR+H5s0aYIWLVrAaDQiISEBwJ/Huddeew2hoaHYs2cPbt68qR6ndTod9Ho9HBwczPYQ9dOnT+Ptt99Gs2bN8NJLLwHIPe+VK1cOt27dgr29PUqVKqV+NsuVK4dhw4YhKysLhw8fVl/nueeeQ0hICJ577jksX74cd+7cMcv+u379OiIiIlCnTh0EBwcDyD1f1axZE88//zxSU1MRHx8P4M/9V6NGDYSFhWHnzp1ITU1Vz8FNmzaFiMDBwQH/+9//il0bACQkJKBHjx6oV68e/P39ISIoU6YMqlatipiYGMTHxyMxMVGtOzs7Gx4eHvD09MTOnTs11whdu3ZFpUqVcOHCBTzzzDNmqe/atWvo3LkzXF1d4enpCQCwt7dHgwYNUKdOHWRnZyM1NRUA1Drc3Nzg7e2NX375BQ8fPlQ/f3Xr1kXZsmVhZ2enHtfN4cSJEwgNDUXp0qVRrlw5iAjWrVuHgQMHYseOHXB0dFS3LVu2LBo3boxu3brh119/xYULF9R1nTp1woQJE/DDDz9g48aN6t9THHq9HlWrVkXTpk1RqlQpfPrpp0hPT1fPtSEhIbh8+TI++ugjAFDPL0DueaRq1arqOe7hw4fYvXs3SpcujYEDB6JcuXLFru/q1avo1q0bGjZsiM6dO8POzg4ODg545ZVXkJqaimvXrqnHudKlS8NgMKBmzZoICQnBjh078PDhQ3U/+fn5oUyZMkhNTcV///vfYtemiI2NRXh4uOa6Y/Xq1Rg7diyOHz+u/i6j0YicnBzUqVMH48aNw88//4wTJ06oP9O5c2cMGDAA27dvx/fffw+9Xl/s2mz9+GwNDJBmIiLw8PDAyZMncejQIWRnZ8POzg4DBw5EaGgonnrqKUyaNAl37tyBg4MD7OzsYDQaAeSeUMuUKQMHBwfk5ORg//79SExMxPLly9UTdXE8ePAADg4OCAkJQb9+/bBq1SpMnTpVXe/i4oJZs2bB0dERy5cvx759+9R19+/fR8WKFTUnqdKlS8PBwQGbNm3Cm2++Wez6bty4AQAYNGgQevfujZMnT+Kjjz5CcnIyAOCZZ57B9OnTUbt2bUycOBGHDh1Sv4hnz55F6dKlNfupdOnSaNiwIX744Qc4OTkVuz47OztUrlwZQ4cOxdWrV7F8+XL1QOHq6qpeVE2ePBlxcXEoXbo07OzsAOSeVF5//XVUqFBBfb2rV68iLi4Oq1evxuuvv16s2k6ePAkvLy+MHTsWQG5A6N27N5599lmMHz8eBw4cAJB7wBo6dCjs7e0RGBiIGTNmYOrUqXjvvffUz4aiVq1a+Pzzz1GzZs1i1WaqUqVKqFmzJi5cuICoqCjo9XqUK1cOXbp0QXBwMDIzMzFy5EgAuTcvlAvKihUromrVquqFiNFoRHR0NE6cOIGNGzea5fuh+N///gc/Pz+kpaVh6dKluH79Ouzs7PDmm29i2LBhcHV1xdy5c5GQkKC+vwBQrlw5vPjii5qbPQkJCdixYwfWrl1rlgB57do19OzZU/28KSf/GjVqIDMzE1evXsXJkycBAA4ODmqI7Ny5M3777TekpaWpJ/9WrVrB0dERDx8+NNvJy2AwYNmyZQD+vDCys7NDw4YNsWzZMtjb2+OXX35RA7iy/1555RWICDIyMgBAvXH266+/onTp0hg8eLBZLp6ys7Oxfft25OTkwNnZWb2o9fb2Rp8+fVCtWjUsX74cFy9eVI/DAODk5IT//Oc/uH79OoDc47yIIDY2FllZWZgzZw6eeuopzefhn6pSpQr69++PhIQErF27Vj0utmzZEl26dIGLiwvGjRuHkydPolSpUuqFaVpaGl5//XVUqlRJDXDnzp3DlStXsHr1arPcpAKAW7duAci9eEtKSgIAvPbaa+jXrx88PT2xd+9erF27Vv1sArnfjeeff15zAWc0GvH7778jOTkZmzZtQuXKlc2y/0qXLo2OHTvizJkz6k1ZAOjSpQu6du2KV155BR9//DHOnz+v2X8ZGRl48cUX1Zs+RqMRBw8exLVr17B06VKz3URLTk6GTqdDVFQUjh8/rn4GP/zwQwwYMACZmZlYtWoVbt68CTs7O7U+Ozs7lC1bVr1GyM7Oxtq1a3Hnzh188cUXqFatmlnqq1KlCnx9fXHu3Dns2LEDqampsLe3R8eOHREWFoaqVati+PDh6jUMkPt9sLOzw8svv4xy5cqhVKlSMBqNiI2NRVxcHNavX4/XXnvNLPWlpaXhgw8+QNmyZfHaa6+hVKlSsLOzQ9euXfHee+8BAH7//Xc8ePBA/ZmyZcuifv36uHv3rhp+gdz3XLl2aN++vfr3/FOxsbH48MMPUbp0aUyePBkeHh7Yt28fpk2bhrS0NAC55+DQ0FBs3LhRDZEZGRm4cuUKDh48iP/973/qZ8LBwQFpaWlYt24d3njjjWLVpkhLS0Pz5s1x/Phx/PLLL+ryQYMGoW/fvqhcuTIWLVqEy5cvw97eXj2fZWVloUqVKihXrpx6bNy+fTtu3ryJb775xmzn4LS0NHzzzTcAcq9BypYtCwDo3r07hg8fjqysLKxZswZJSUmwt7dXjxlvvvkm7O3t1f1sNBqRlZWl3rTv27ev5sbCP2Xrx2erKLG2zn+Bq1evSu/evaVBgwayY8cOtTuA0WiUJUuWSPv27aVPnz5y/fp1dVxVYGBgnu4zycnJmm5qxXHgwAHx9vZWu2mkpKTIJ598IjqdTjOpikhu95oWLVqoXVKmT58uoaGh4u/vr3a3MPeEB3/88YfodDpNX/p58+aJl5eXDBo0SO0KJ5I7c2iXLl2kXr16MnjwYJk0aZJ06tRJAgIC1C4DlpiQwbSryeeffy46nU4mTpyozmgqktsVqmfPnuLq6irbtm2TuLg4OXLkiAQHB8s777yTp65Hp2n/p7799lvR6XSi0+k03Ti2b98unTp1Ei8vL9m7d6+I5HaLOnHihIwbN05atGghISEhMn78eM1kQObef2fPnlXHDd6+fVtCQ0PF3d1dtm/frnZRSUtLkxUrVkiLFi2kb9++kpmZKQ8ePJD4+HgJCAjI0z3q1q1bkpKSYrYaY2JiNF3EJk+eLG5ubjJ58mT19+Tk5Mj58+clMDBQWrVqJb/++qtcvHhRjh07JkFBQTJ48OA8+86c3WsvXryojkv98ccfNes2bNggzZs3l379+mm6sYqITJo0Sbp06aL+OycnR3bs2CFeXl7q+KTiMP2bT506JSNHjpRGjRppuh2L5E6u4eTkJCNHjtSMEz9x4oR4e3trutNmZWXJ1KlTzT6+KyUlRSZMmCDOzs6yZs0a9fMnkjv2vEOHDtK9e3dNl7uTJ0+Kp6enZhzSgwcP5LvvvrPI+DMRkU8//VR0Op3MmjVL8zk/cOCAdO/eXZo0aSL79++XhIQEiY2NleDgYBkyZEieZ+Oau3u3SO5kTM7OzjJ48GDNfjp37py8++670rhxY033x/j4ePH29pbFixer2xqNRjl+/LhZuw4q7ty5I++//77UrFlTVqxYkec97tSpk3Tq1ElOnDghqampcubMGQkKCpJ3331X8zqmk5qY06FDhyQkJETq1KmjGbclIjJ9+nTx8PCQmTNnyrVr10Qkd7K4du3aycyZMzXbnj17VjNDq7nk5OTI8OHDpXbt2rJixQp1+EdOTo5s2bJFOnToIP7+/nLlyhW5efOmnD9/XoKCguS9997TvE5qaqrZ9p/p53rLli0SGBiY7zjuBQsWiJOTkyxbtkzz2d+9e7f4+PhojjvKtYQy0U9xxMTESI0aNUSn06kzh+v1epk0aZI0b95cxo0bp455VcbNOzs7S9u2bSUkJET8/PykQ4cOmvPwo3+3uVy8eFEGDhwozs7OEhkZqVm3ZMkSadu2rQwZMkTdV8o579Fr1KSkJLOegxX79u2Trl27Su3atTWT44iIzJ8/X53cLykpSV1+6NAhadmypeb7lJKSIu+++65FjtG2fHwuaQyQxXD9+nWJjY2V6OhodWB0SkqK9O3bV+rXr68JkSK5X9DmzZtLgwYNxNvbW3r27Cn+/v6ageDmFBUVJbVr15aJEydqTqSFhcgjR45IixYt1PGNX331lbrO3LOpRUVFiYuLi0yZMkUyMjI0f/+nn36ab4i8e/eu9OnTR3Q6nfTs2VNzoWruyUrS0tLk/v37cvbsWc1g+y+//FINkaZjBE+fPi19+vQRJycncXJykgEDBkjnzp0t9v6K5J68fHx8JCQkRFxdXWX48OHquvxCpOLOnTv5PtjdXHJycuTWrVvi7u4uy5Yt0/zekJAQcXd3lx07dqify9TUVFmxYoU0bNhQmjdvrn4//Pz8LHpzICoqSmrWrCmzZ8/WjFNVQuSHH36oniSMRqOcP39eQkNDRafTiaurq3Tv3l26dOmiuVlk+pgcc7py5Yq8++674uTkpJkcR0Tk+++/F39/f+nfv78cP35ccnJy5MyZMxIYGCjjx4/XbJuSkmK2G1SPfm4uXLggw4YNEzc3tzwhcvfu3eLk5CSBgYHyxRdfyFdffSWdOnWSwMBAzYzQ5pSTk6M5BiuPSlBCpHLcFhFZt26dBAYGipeXlyxbtkzmzZsnISEhEhQUlOfYZ65jYUpKisTFxcmhQ4c0N5XmzJkjOp1OZs+enecipXPnzlKjRg2pU6eODBkyREJCQjQTAplTWlqa3L59W+7du6fuqy1btqgh0nSM/pkzZ+S9994TnU4ngwYNkgEDBqg3IC0xkVRB/ipEtm3bVmrWrCnu7u7Sp08fCQwMtNj+u3nzpiQkJGguhmNiYtQQ+ej4rOnTp0udOnXE3d1dwsPDpXPnztK+fXvN8cWcrl27JjExMbJjxw5NwBo2bJjUrl1bVq5cqZmsbMuWLdK6dWupW7euNG7cWN555x1p3759nudYWtIvv/wi/v7+EhgYmGf82Jw5c8TJyUkmTZokv/zyizoZW5cuXdTalH1ojs+kcv7o06ePNGrUSDPe/NEQqQSGmzdvyr59+2TEiBEyYcIE+eKLLzSPYjIn5fjy66+/qiE2OTlZDZGPzsGwZMkSadq0qbi4uIifn5907dpVAgICLPb9UJh+rg8fPixdunTJN0TOmjVLmjRpIn369JHt27fL6tWrJSQkREJDQ/PUZno+/6eU/XfgwAHNcWTevHk2cXy2NgbIf+jYsWPi7+8vLi4uotPppG/fvuokKklJSdKvX798Q+SKFSukdevW4u7uLjt37lSXm/vAERUVJc7OzvLxxx+rH3zTD29ycnKBIfL48ePSokULefvtt+WXX35Rl5vz5JVffY8qKETeuXNHevbsKc2aNctzF81cYmNjZdCgQeLp6Sk6nU5atGihmc66oBB58uRJGTlypOh0Otm6dau63JIXUBMmTJCmTZvKF198IS4uLjJy5Eh1nWmI3L9/f771WPJZbMoFZkZGhnp3tqAQqbRE+vj4iJubm+buoSX2X36fQdNgMGnSpDwhMicnR86ePSv9+vWTmjVryq5duzRTeVuaaYh8dBbf77//Xry8vKRmzZri7e0t3bp1s+jFcVxcnEyYMEHCw8Ole/fu6mQjFy9elOHDh+fbErl3715xcnJSbwBNnz5d87gZczp16pSMHTtWgoODZfDgwerkZg8fPpQxY8bkGyI3btworVu3Fp1OJxEREbJixQp1vbnri46OlhYtWshbb70lOp1O2rdvL3PmzFF/T2Ehsm/fvlKzZk3NBeCjE4wV1/Hjx6Vr167i6ekpXl5eMnLkSLXVa+vWrfmGyHPnzsmYMWPExcVFAgICJCYmJk/LSkkoLEQqLWlNmjSRjRs3qsvNvf+OHTsmHTt2FHd3d9HpdBIWFqbeyPvjjz8kNDQ03xA5f/580el0EhQUJCtXrrRYfUeOHJFWrVqp57iWLVtqJksZMWJEgSGyU6dOeS7wLXH8O3/+vHz22Wcybtw4mT59unqTRZkwLr8Qqew/nU4nffr0kREjRuQbcIt73lPOH9OnT5e7d+9Kq1at1JZi5ZhbUEtkfsz9/Th69Ki0bdtW/fx5e3vL6tWrxWg0yuXLl2Xw4MH5hshVq1aJu7u7eHt7ax7VZu7P36NM35s//vijwBCphDelkeOjjz6yyDnuyJEj4u3tLR4eHur317TVW/mcWev4bAsYIP8BpeVs5MiRsnTpUlmyZInUrl1bE8SSk5MLDJFLliwRX19fCQsLU7t5mPPgoRzYPvroozzhMSkpSV1WlO6sISEh8vPPP5utNtP68gu3SUlJmm4lhXVnjYiIEHd3d9m4caNZ7jYpjhw5IvXr15dRo0bJkiVLZPfu3TJz5kzNo0JECg6RsbGx0qtXL3F1ddWENnMzne01PDxcvvzyS/nss89Ep9PJ6NGj1e0KCpGWailLSEiQTZs2ybJly9QZ2rp37y4dOnRQu7MW1hK5fPlycXNzk4iICPU1LdH6XdBnUHkuoUj+LZHZ2dly9uxZ6dixo3h6ekpMTEyJPY9SJHf/jhgxIt8QqXRn9fb21nTLNffJ6+jRo+Lm5ib9+/eXoUOHSlhYmNSpU0c9YcbFxRUYIg8dOiROTk4yaNAgzR17c34Ojx07Jo0aNZKePXvK2LFjpX379tK0aVP1mbG3b9+WcePG5Rsif/zxRwkKChJ/f3/1JoY5jy8iud/ZZs2ayZQpU+TAgQNqy22zZs2ke/fu6vtdUIjcv3+/dO7cWVxcXNR9aM6Lp9jYWHFzc5ORI0fKypUrZerUqeLl5SW9evVSw8Qvv/witWrVyhMi4+LiZOzYsaLT6dTZuy3RPf6v3LlzR51NOr+WyMDAQPH19VXfY3PWd+rUKXFzc5MpU6bIli1b5ODBg+Lp6Snt27dXz/kHDhwoMETOmjVL3N3dZcKECWbrLWDq3Llz4uHhIR999JEcP35czpw5I8OGDZO33npL7a6tzNBZUIj09vYWLy8vi1zDiOSehxs3bixdunSRDh06iJubm3h7e6vXAabPtn00RH7zzTei0+lkwYIFcuPGDbPXd/DgQXF2dpZp06bJw4cPJSsrS7p16yY9e/ZUtzF9znF+IdKSN3HPnDkjjRs3lo8//lj2798vly9flqCgIPHy8lI/7+fPny8wRH733XfSpk0b6dGjh/poGUs+j1dhepw9fvy4hIWF5RsiFy1aJPXq1dNce5nzGB0fHy+enp4yffp0OXDggBw7dkw8PDw076/In91ZS/r4bCsYIP8m03Bm+niIAQMGiI+Pj2a65cJC5NKlS8XHx0f8/PzUA5w5REdHS+3atWXatGl5LoxPnjwpbm5umovOwkLk0aNHpX79+hIREWG2MXsxMTFSq1YtmT59unogVepTLlo2bNigObiahkjTvu/37t2Tjh07SsuWLQu9s/d3JCQkSNu2bWXKlCmax0qYvnemB6pFixYV2J21X79+otPpzDYNfHp6uqYO04e0jx49WsaMGSOpqal/GSKVerZs2WL2cT4xMTHSsmVLadq0qXqXcM2aNbJu3Tpxd3eX7t27q8/L/KvurI0aNZJ27dqZtT6RP7/DU6dOzfMdOX36tMydO1dz0VZYd1YlOJlzqnBTpg/BFskd/6nX6+XatWsyevTofEPkmjVrxM/PTyIiIizysPHbt29LcHCwTJgwQX2eWGpqqkREREibNm3UAH7+/PkCQ6TSnbVXr155Lg6K686dOxIcHCyTJk1SL3rv3bundpdV3uvCQuT69evF399f2rdvb5FniW3fvl28vLw0z/178OCBLFmyRN566y0JDQ39y5bIgwcPSkREhOh0ujw3t4ojIyND+vfvLyNGjNAcV2fNmiV16tTRjLMtKESePXtWRo8eLTVq1MjzjFlLU97f9PR0SU1NLTBEbtq0SQIDA6VJkyZmHSulTO3fp08fuXXrlrpceYSCaavioUOHCgyRM2fOlMaNG8vw4cM1N0/NYcGCBRIcHCwpKSnqeeTChQtSu3ZtWbBggbpdVlZWgSEyMjJSfHx8pG7dupq/0xyuXr0qvr6+8uGHH8qNGzdEr9fLkSNHpEmTJjJo0CB1O9PHEuXXnVWn08mnn35q1vF6Dx8+lH79+uUZGrR69WqpX7++2tNGJP8QOX78ePV6ytw3phRz586VLl26yPXr1zU3mmvXri2fffaZut2FCxcKDZHe3t7SsWNHsz+KxdSRI0fyHDuU5yNGRUUV2p3Vzc1Nxo0bZ9bH3Ynk3mBq0aKF5vi8dOlSadmypTx48EBzM6Kkj8+2hAHyb4iOjlZbLR59Ls+IESNk8ODBee5yJSYmqiFy+/btmmC0dOlS8ff3N9vDbJOTk9WuV4+Gs5MnT0qdOnVkwoQJebqM5hci9Xq93L9/X+Lj4zWTxRRHSkqKNGvWTFq1aqUuUw6gJ0+eFFdXVxkzZky+3QnnzZsnnp6eMmjQIPVZVKdOnZLbt2/neTbVP6GcRDdv3izBwcGaC4r8ur1kZGSoYc40RCr7SpkoYujQoWY5uEVHR0tAQIDMmzdPc4JSnD9/XlxcXNQbFUqINJ0cYseOHdKpUydp3bq1hIeHi5eXl1lPrFFRUVKrVi354IMP5MSJE3LgwAH1GYpHjhyRvXv3yltvvSU9evTIN0Ru375dvYhPS0uTJUuWiKenp+amQXGdOnVKPdhnZ2dLTk6O+v4q6yZPnixZWVmaz19+E+sYDAY5ffq09O7d26zPy1Ts2bNHwsLC1BaBuLg4ad26tdqN5vLlywV2Z12zZo34+vpKu3btzDJRhKlz585J06ZNNV3wRXLv+js7O2s+74WFyH379olOp5MBAwZowltxJSQkSLNmzdTnjSrv48aNG0Wn02kC4aMh8tGA4e7uLqGhoaLX6816B/7777+Xxo0bqwFcOQ5mZGTI999/L02aNJG+ffuqn03TiRtMb24cOnRIevbsadYLqIcPH4qvr696oamcsxISEjTPCFSYhkjTC80LFy5I//79pV69enLv3r0SaYFUfsf69eulQ4cOcv/+fbl7965MmjRJatasKcuXL9d81iIjI6VTp05y5coVs9WQnZ0tnTt31gSdnJwcuXr1qri5ucns2bM12x88eFANkflNrOPp6Wn2Vshhw4ZJWFiY+m/l8xcYGCgDBw5Uj40i2hBpOrFOdna2bNmyRQICAsx2jaDYt2+feHl5aS689Xq9jBw5Ulq1aqW5eV9YiFywYIHodDpZuHCh2Vogc3Jy5ObNm3muo5TznzLJmbL/CgqRKSkpsmrVKrM/S9ZoNEq/fv0kPDxcXWYwGCQ1NVVatWqV55mJpiHy0SFBy5YtE39/f7PfwBDJ3T+3b99Wh4A9ePBATp8+LU5OTpqbGIWNiVS6kU6ePNmsvWy++uoradCggea8rowP7dGjh0RERGgmIFRCZEkcn20JA2QRHT16VJycnGTs2LHqMuXAcPLkSWnYsKEsWrRI0tLSRK/Xa+4s3bhxQ/r27Sv16tWTHTt2yJ07d9RJMMzVciaSOyB+8ODBUq9ePfnpp5804axOnToyceJETfc8U3fv3pWZM2eKTqeTDz74QD7++GMJCQkx64VdUlKSOgOZ8uB6kdyuCgXVZxre5s+fr7ZEDhw4UDp37qw5kZjDqFGj8jzY/FF3796ViIgI6devn7rMtDvr6dOn5auvvpLo6Giz3GG8f/++eHt7i06nk2bNmknDhg1l3rx5eWbRnDlzpgwdOlQyMjLk1q1bsnDhQnF2dtbU+dtvv0nbtm3Fzc3NLLNwKkxb9UzfwwsXLoiPj4/a9WPTpk3SuHHjAkPkjh075ObNm7Jr1y65f/++Wd/fnJwctWvdow+1V74j77//vqZ+08/fBx98oLZEXrx4UVatWiUHDx40213kR8cQnT59Wp1ddcWKFVKnTh3N3WuRwifWWbdunbRv396sAVwkN2jXrVtX7dqu7CPlIctRUVGasKCESNOJdQwGg3qTxdwn14sXL4pOp5PNmzeLyJ+h4sCBA6LT6SQmJkaz/e3bt2Xs2LGalkjlZ/bv32/WcKGIjY2VGjVqyIoVK9RlyvkkIyNDvvzyS/Hy8tKEbuViePbs2XLp0iVZtGiRXLlyxazHaKUOLy+vPBeat27dktq1a6sP8zZ9j01nZ71w4YJkZGRIQkKCJCcnm+UmVVHCp7KNMsmP6Xi+O3fuqCFyxYoVcuvWLfnyyy8lMzOzwHPiP5WdnS3Dhw+X8PDwPH97q1at1Ifam37fTWdnVVoilVYY054w5vLZZ59JvXr18tz4CgsLkx49euTZXvmblJbI5ORk+eGHH+T27dtm651kasuWLVK3bl31Rq6yr77//ntxdnaWpKQkzf4zHROpBDKDwSBpaWny3XffmaUF7d69e3L79u0876npZ3Pw4MHi6+ubp2fPoyGyRYsW4u7uLjVr1jR7+BYR+eSTT8Tb2zvPsTUwMFD69+8vItpzm+nsrNu2bZOsrCxZuXKl6PV6s16j5mf//v3qhHSurq4yceJE0ev1msCfX4hU1i9fvlzTUvhPPHp8OX36tNSqVUvee+892bp1q+zYsUPq1asnERERMmPGDBkxYoQ0bNhQMzmT6ZhISx6fbQkDZBEtXrxYDVemLYZKeNTpdBIQECDOzs7i6+srAwYMkB9++EGio6PVVrJ+/fqpY7t0Op1FpuFOSUmRYcOGSZ06dWT37t2FhjOR3K4iK1askAcPHkhKSop6J8XJycmsAUNx7do1mT59uuh0Opk/f74kJSWJq6urjB8/Pt/6EhMTZdGiRWqL74IFC6R27dri6upq9pYVkdw7s127di10m7S0NHn77bc1s8+J/Bkile6b5rowzszMlM2bN4uXl5c0b95cpkyZIg0bNpT27dvLxIkT1QuMqKgocXd3V8cd3rhxQ2bMmKGZCVYkt2XLnKHi+PHjUrNmTZkzZ06+72Hv3r014xk3b94sjRs3lp49e2rGRIaGhkqzZs3U74c5W0dPnTolp06dkuTkZBkzZow0atRI5s2bJyK5XWYK+46YXgxMnjxZmjRpIl5eXqLT6cx28o+JiZGvvvoqz4QjN2/elBYtWkjNmjWlb9+++U4W8GhLpMFgkCVLlkhaWppFLu7u378vPj4+Mm3aNM3rKwEtv+NGfHy8DBs2TBo1aiSfffaZTJs2TYYMGWKRiTfu378vffr0UbvYKhcHBw8eFJ1Opxl3qTCdnXXFihUyePBgefvtt81WU2pqqly/fl1SU1PV93DcuHHi7u6uaYFQ3vd79+5Jr169JDQ0VPM68+fPF2dnZ2nVqpVZjzEK5XP15ZdfysCBA9VzVHZ2tly/fl3q1aunht5HZ7PcunWr1KpVSwYNGiR9+vQRNzc3s33+Hu3xU5DffvtNdDqdfPXVV3nGHCkh0sXFRTp06CA6na7YF54F+emnn6R///55HlXi5eUlH3zwgWaZsv9iYmIkNDRU6tevLz179hQ3Nzf1cR7mdvz4cfn444/VsKp87sLCwqRfv36aC2rTm3gjR44UV1dXCQwMtNg1jEhubwvlu2gaJFasWCE1a9bMd+jFrl271BC5bds2+fTTT2XRokVmaXk8evSohIeHqxNKfffdd/lupwzV2LRpU551piHyvffekxYtWljsUUB79+6VMWPGyJkzZ0Tkz+9q+/btZdiwYZptHz58KEajUZKSkmTw4MFSp04dCQ0NlZo1a1rk5pnCaDSqx8I1a9aIk5OTtGzZssDJ80xD5O+//y6zZs3K84ivfyq/z8j69evFw8NDWrZsKbVq1ZKhQ4eq3wWDwSDffvut1KtXT/MIOksfn20NA+RfMO3brzwDcNKkSZKeni7nz59Xp+pduXKlREZGypgxY+Ttt99WZxpUQqfIn8+JdHZ2ttiBQyT38SJKlwQnJyeZOHFivndBkpOTJSAgQDp16qR+gZKTk+Wzzz6zSJc8hRIilWcnffTRR3m6DCq1KBOVmLbyfPXVV2Z/hphygP3888/F1dU1TyuFQqlxyZIl4urqKjdv3tTU9vnnn4uTk5N64C6Oc+fOqUEvMzNTIiMjpV69ejJu3Dg5dOiQzJkzR1q0aCHNmzeXOXPmSGpqqkyfPl06deqkXnCZdh2zxCxgGRkZ4u/vn2fCIGWfpKenS6tWrTQt9yK5d5jd3d2lQ4cO6ji927dvqxcm5vx+KK2jygQqly5dklGjRslbb70lEyZMEFdXV5k0aVK+d1qTk5PFz89PPv/8c3XZuHHjxM3NzSzvsdFolPv370ujRo1k6tSp6nLlxHn16lVxcXGRhg0bSkREhOZxLKbfFyVE1qpVS4KCgqRmzZoW+Q6bjqdRblQo9uzZIzqdTrNfrl27pnYlPX/+vDpDsU6ns8j4TMWxY8fydPVWusyaLr969ao62dDt27dl4sSJotPppE6dOmYbt3L06FEJDQ0VLy8vadq0qaxZs0ZEcmcaVMZl5vfd2bt3r+h0Ojl//rzmgv7TTz8VFxcXi55Dbt++nSdcJSYmik6n03SXTkpKkrFjx6o3e3bs2CE6nU5q1qxptrGjZ8+ela5du6o3mwqSlZUl69evl++//77A2Tbv3r2rfuctsf9Mf5fp0Aqj0Sj37t0TNzc3mTNnjrpc2X/79u0TkdyWyKCgIKlfv75FbuCaMg1hyrmhTZs2mmc6JicnS2hoqNrqbDAYZNiwYWY/RptS9uHu3bvVXiqKZcuWSe3atTXXZSkpKeqF+o4dOyQwMFCcnZ3zHIv+qVOnTqnn3UWLFsn8+fPzBAPToNO5c2fp0KGD+j02Xad8LvV6fZ7x7eZmOrdGdna26PV68fLykkmTJqnLldCofKcvXLggAwYMkHr16ln0+Czy5/t8/PhxqV+/vvTr10/txWB67nr0ER9du3YVnU4nNWrUMEuNsbGxMmrUKOncubO88847smPHDvW7cffuXbl+/br4+Piox23l/bx8+bLodDrNkwpESub4bCsYIAtx+PBh6dSpk+aOtTK2bMiQIVK3bl2ZPHlyvndH4+Li5LfffpPp06drAkZSUpLZxjNcvnxZ1q1bJ+PHj5c5c+bI1q1b1X75d+7ckQkTJohOp5N169blG85CQ0Olbdu26smjJB9HcPXqVZk5c6a4urpqJu9RDrBKfabPybTUgHNTBw4ckBo1asi4ceM0d39ND2JZWVkyevRozRgD0wsWZaKB4khNTRV/f39xd3dXQ6Rer5ctW7aIi4uLjBkzRpKSkuT+/fsyZcoU8ff3l/r168vQoUPF19c3z+yvlhx/dOrUKfH395fQ0NA8YxT69Okj3t7e6ufS9LO1bt066devn+azaa4xrQolPE6ZMkXTuhgfHy+jR4+W+vXrS/v27dXlprUkJydL586dxdvbO0/4NvekEY0aNdJcWIrknlidnJxk2rRpcuzYMWnTpo307NlTdu3ale9rXLlyRUaMGCFubm5mO/nnN1Pvoy07yj7bsmWLetfVaDRKcnKy+Pv7S3h4uPozcXFxMn78eLO1/Dz6uc7vTrLyu3/55RfNXeGkpCQJCQmRTp06qdtev35d5s6dq3kmXnGcPn1a6tevL6NHj5aVK1fKxx9/rLn4jIyMFH9/f+nQoYM61lWxevVqadmyZb6TrCljJ4vr0f2Xk5OTZx8q+y8+Pl50Op062UZSUpKEhoaKt7e35nu9d+9es7bs/fjjj9KkSROJiIgo8MaeoijniNu3b1vkIfeKR48Vyv67c+eOuLq6yqJFi0Tkz89fy5YtNXUfPHjQbC2Phc10qawz/T67u7urN7yV99fPzy/PJHKWDj8i2v2ofL7mzZsnjRo1kgcPHkhOTo4kJyeLr6+vDB48WN02MjJSQkNDzdJt1Wg0yowZM6Rnz56aY35iYqKcOnVKE1BNj3GNGjXK0wX80e3MobDPn/J7lP/PzMyUt956Sx0+lJSUJJ06dRJvb2/N5+/8+fNmndjR1KN/+7Vr10Sn06n7KioqSurUqSPdunXTtG6b/p379u2TkSNHmuX9PXHihLi6ukrPnj3lvffek9DQUGncuLFmUjAlKH7//ffqz2VnZ8vu3bulTZs2+QZFcx2fbR0DZAGioqLEyclJpk6dKnq9XvPBX7hwoeh0OmnTpo3mQ5yVlVXgwcHc4ScmJkZatGghLVu2FE9PT3Fzc1OfraYMJL9z544MHTpUXFxcZNOmTZpWxk6dOomvr696sLFUaCys+0hycrLandV0TGRCQoKEhoaWSH35mT17tjog+tEuOqYPaH/0gv/RBxUXh3KA8vPzE19fX7XbtMFgkC1btoirq6sMHDhQbTW7ePGifPHFF9KkSRPR6XR5arO0uLg4adOmjXTq1Eltuenbt694enqqJ9nC9o8lHoNx/PhxqV27tsyYMUOMRmOe33/p0iUZPXq0NGrUSObPn6/+nHJhkt9n0Nx1Go1GycrKEjc3N83EASK5k9K8++676nt89uxZadSokQwbNqzA48mlS5cscnGn7APlRoDyUGxTS5YsERcXF7l586Zcu3Ytzw0g07vv5vboTZtbt27lqXH58uVSo0YNuXv3rqSkpOR5fxXmeI+NRqMYjUaZMmWK9OnTR1PfjRs3JCYmRl2mjH9zc3OTxYsXS1xcnOzdu1dCQ0Olb9++JTL5THx8vKarYkJCQp4Lo5iYGHWM682bNyUkJEQTLiz5KJvNmzdLq1atJCwsrMAQmd/NjpJ6dMixY8c0f/+FCxfk6NGjmt+fmJgorq6u8t1330lKSooEBwdrbuBa8gbp4cOH1VZOkdzHPHz55Zea82p2drY0adJEpk2bJikpKXne35J6jp3SW8r0uKHsx/Hjx0vLli0lOzs735vgiqJ2eS6Knj17Sq9evdR/z5w5U5o1aya1atVSH3VjGi4zMzPVx4gsW7bMbHUU5o8//pCEhAT1M3j+/HnZsmWL5jP14MEDeeutt2TOnDn5fv4s8f7GxsbKihUr8txYVupMSUmRn3/+WdLT09Xzw4EDB6RFixZ5xv6afpeKO27ZaDSKwWCQIUOGyODBgzW9jxYsWCA+Pj7SsWNHNURGRESIn5+f7N27V3JycuT48eMSHh4uPXr0eCIfz1FUDJD5UFotTB+FIaK9e6KMd5s8eXKhs6ha4gQWFRUltWvXlsmTJ6stDZcuXZIffvhB6tatK15eXmoffNMQ+fPPP5dIOIuLi9PcfSvswsJ0TOT06dPl4cOHEhYWpjlxWSo8PvreKP9Wpn7X6XQyePBg+f3330Uk9yJ1z5490qVLFwkKClLrstRFSlZWluzfv1+8vb0LDJH9+/fXnLzOnDkja9asKdHArVBCZOfOnaVTp07SokWLAlvCLH1hpzyrVafTyRdffKEuf/SzqLREmobIGzduSEhISIncwNDr9ZKRkSFvvfVWvo87UOo1nWrfUneHC7J3715xd3dXP2dHjhyRevXq5RmDvGTJEqlVq5Z6ci2J77CIyM6dOyUgIEDtUnv69Gnx9/fP0wq/fPly0el0cvjw4RK7QdW3b1910gqR3KnnlZs8bm5uMnToULl586YkJCTIhx9+KC4uLuLs7CxNmjSRsLCwPOHbEo4fPy7u7u7qhD3nz58XNzc3zXNERXK/UzqdTlatWiVhYWEW3X83btyQU6dOydq1a2XPnj2SkZEhkZGR4u3tLZ07d84TIk276R84cCDPIwksKSUlRWrWrKnOuhoXFyc6nU4WLVqkOc4lJydLnTp1ZPr06dKzZ88S+fwpXWebNWsmnTt3lri4OHV84axZs9TjS3Z2tqSmpkqTJk1kyJAhEhoaKm3atCmxG7i7d++W/v37q/tLeRzUoyFi3Lhx0qpVK7ly5Uqe1tGsrCyLPKtw4sSJ0r17dzEajbJ06VKpV6+eLF++XLZs2SJffPGFuLm5Sc+ePTX7KDk5Wb799luLPx9YJDcY1q9fX8LCwiQzM1NOnjwpOp1O5s2bpzlu3L17V9zc3GTMmDHSvXt3i3/+bt26Ja6urlK7dm3x8/OToUOHyoEDB/IMpzKtUakjv7H+Iua/bggODlaHjpjuA+VRWKGhoXLr1i05cOCAtG/fXpycnKRRo0bi5eUlnTp1KpHjsy1jgHxEYQ8YV57BplC6s/5ViDQn00eJ5NcKcOLECWnQoIG0bNlSfd5fSkqKjBgxQlxdXaVJkyaaVgFzHjiMRqNkZ2fLnDlzpFatWppHSBR2IL169apMnz5dnJ2dpUaNGha98Lxw4YLmblhBB6SsrCxZtGiRNGjQQJydnaVt27bi7u4ufn5+0rt37zyTnVhKQSFS6c7q6uoqgwYNynfCGWuFSG9vb3FxcZG1a9eqy0vyIeLKd3j06NHSu3dvadGihXz11Vfq+kcP9kqIfOutt+Sjjz6SiIgIi55cL1y4oIYdpaWqUaNGmnGWj96cMG1BFbH8587U4cOHxdvbW9q0aSMHDx6UBg0ayMiRI9Xjo1Ljjz/+KDqdTho1aiQ+Pj4ldvGZnJwsrq6u0rVrV/nxxx/VxwE9epd6165d6iRXlrw4Nn1vlItxkdxJGerWrStLliyRP/74Q50pMTQ0VB3WcPnyZdm9e7fExMRoHqNgacrskdOmTVNnQny0FSclJUXt6WKpc4hIbmteaGio5lmySo+KdevWiY+PjyZEKvvJYDBIZGSkNGnSREaOHFlixxzlcRZubm7Su3dvcXV1lffffz/P/rt9+7b6N3Xo0MGi349Hj3FHjhwRf39/6dSpk7i4uMjEiRPVmZBNDRo0SHQ6nQQGBpbY9zc7O1uWLFki7u7uMnbsWDl16pQ0aNBAhg0bluca7KOPPhJXV1dp3bq1ZmiBJb/DP//8szg5OcmhQ4fk22+/lQULFmiCzs6dO8XZ2VkWLlyY72uVxPf3+PHj4uXlpc7k++i8F8qENR07dhSdTidBQUEl8v6+88474u7uLhs2bBAfHx9xc3OTgIAA2bJlS55xpKb7vKS+u507d5Y+ffqo/zZthf3++++ldevWMm7cOPW5z9u2bZPFixfLzp07S3TIl61igDRx5MgRcXZ2lpkzZ6qzxykHrhMnToiTk5PahK1QQuSUKVMsMh2zqWPHjolOp5P3339f86F9dCzD0aNHxdXVVYYOHapuk5KSIoMHD7boiUsJ17dv35ZZs2aJu7u7jBgxQl1f0NgakdzZJidMmCB9+vSxWLh9+PChuLm5iZ+fX5FCpEhuF4wff/xRJk6cKJ9++qns2rXLIhd2ykxo+TENkX5+fur4nYyMDNmyZYs0aNBAIiIizDaup7jOnDkjbdq0yTMmsiROCrGxsZrnmV6+fFl69epVpBD53nvvWfziOCMjQwICAvJ8Br29vdVnTD76fDFFSYZwUwaDQS5duiTe3t5So0YN9cbQo/tGGSMXGBiorjP3/nu0G7Lp73Fzc5MaNWrIwIED8+3CpjxmJDQ01GL1HT16VFavXq2eP5Rnny5dulRmz54tCxYs0LyPq1atkrfeeku+/fbbfF+vJG5QKfr27Ss1atTQBFpTt2/fVu+8W2r/HT9+XOrWrSuTJk2S3377TVJTUyUyMlIzIc/69evVEKl8h/R6vURGRkrz5s01319zK6yb6bfffitOTk7i7e1d4KMP3n//fYt+P0zPAY92Gd+4caPUqFFDmjVrpnleoumxcO7cuRb9fige/Vzfv39ffvrpJ2nevLnUrFlTJk6cmO92O3fuFJ1OJx07drTIMfrChQvqzVjT1x02bJi4uLhIkyZN1AmFFJmZmdK7d2955513LNIK+leUOpWg26xZM004M61n/vz50q5dO4u/v8p7c/DgQfH19ZXIyEjJzMyUFStWqI/r8PLykq+//trik0XlR/m7161bJ25ubpquxqbHjlmzZombm1uBE3iV5I1cW8QA+f8pzw4LCwtTLz6UD9LJkyelXr16mrvaph8cZUzk4sWLLVqj0m123rx5BYYF5WSg9ME3nWL41q1bFrurfebMGXnnnXfk7NmzIpLbdXbGjBkFhkjloKbX6yUqKkoSExMlPT09z4WhOZgeQKOioqRevXrSqVMnzQxvRZmM4++s/zvi4uKkS5cueZ6fZyorK0v27dsnLVq0kI4dO6rLDQaDrF+/Xrp3725T3SiU7qz5TaxjSStWrJAvv/xSE8gvXbpUpBB54cIFWbhwocVPridPnlQnHVJ6CTRq1Eh0Op00aNBAGjVqJKGhoTJlyhT59ttvJSoqSi5fvlyiU4I/OnNvbGysuLi4qN13lPF7ynqj0Sjp6emaCbvMvf+OHDkikydP1lzEK79Lma22Tp060rFjR80MqqbfqbVr11rs/VW6eM6dO1et68aNGzJo0CAJCQkRDw8P9REYpn/D22+/rXnMTUlSvgMXL14UV1dX8fb2Fi8vL1myZIkagkz338mTJy22/27duiVdunSRiRMn/uXzXzds2CDe3t7SpUsXOXz4sOzdu1eaNm0qAQEBFqsvLi5OwsPD1XOcyJ+fP+XZqD179pS6devKyJEj850JNiUlxWLn4AsXLki7du00AUf5HcpMlwMHDpTWrVtLz549NdcG+U2SZYnj3+XLl9WWMWXfKceQqKgoqVWrltSvX1/tDmy6XiT3czpnzhyLvMcGg0EGDBggAQEB6vWVUuOJEyekb9++6s3JjIwMzT4bMmSI9O3b12y1FJVS3/Hjx6VevXoyePBgadKkifTo0UOSk5PV7UwbGSxxjaV49MbdzZs3pX379jJgwABNvaNGjVJ7FzRq1EgGDx4sx44dM+sEeo9KTEzMMzN3cnKy9OzZUwICAmTr1q3qctPjs7e3t4wfP95idT3OGCAlt1Xg/v374u/vLz4+PrJixQr1y3XixAm1S8CjLQOmJ4gffvihRJqylQleFixYUOhMkLGxseLs7Jzv84gsETSWLFkiDRs2lL59+6oXugWFSNNB21u3bs0ztbSl7uApB4XY2FipU6dOgSHS9OL5+vXrmvfdErUpD60NDQ3NM/GCKYPBIJs3b5YGDRpoxsuZhllbC5Ft27aV1q1bl+iU1qb7Q/nv+Pj4IoVIhaW/y6aTDu3fv19CQkJk+vTpsnDhQhk/frwEBgZKvXr11JOsTqezaLc8vV4v9+7dk2vXruXpGn/69GmpWbOmTJgwQR0L4u3tXWiLtyXCmZOTk+biUaGM+Zk2bZrEx8eLu7u7pnVKJO/7bIn6lHHzSuuj6TT1/v7+otPp1JYV0/UjR46Ud955x6z1FCS/G19nz55VL4yzsrLk3XffFW9vb1m4cGGBLWmWuPN+6tQp8fb21jyqJr/P+8OHD+XMmTOybds2ad26tfj7+0vdunUtGh5FcoePuLq6SufOnTU3cxISEsTV1VXGjRsnqampsmPHDnFzc5N+/foVeHyxxHE6OTlZfHx8xM/PT1auXKkuT0lJ0cx0efLkSfHz85OgoCDNo3gKevSJudy4cUNatGghERERaohU3qfY2FipXbu2jBkzRhYvXiytW7fWXDPkxxLvsfKYofDw8Dyt8L///rtERERIrVq1ZPXq1er1lxLcZ8yYYfZ6FKmpqXLr1i05dOiQREVFSXp6uno9k5SUJHXr1lUfv3L69Glp1qyZBAYGFjibryXe39jYWOnVq1eeR69s3bpVatSoobZ6K0Mdvv76a/njjz9k2LBh4u7uruk5ZE45OTmi1+slPDxcBgwYkGfs/vHjx6VNmzYSFBQkW7ZsUZcrE+2EhobKhAkTzF7Xk+BfHyCjoqJkyJAhkpGRIdevX5fg4GBp1aqVbNq0SWJjY8XV1VUmTJiQ76xPycnJeSa+MPdB7cSJE3lmZ5w5c6YaIh+9iFMODA8fPpTatWtrph62tG+//VZatmwpPXv2LDBEmta3bds2adasmfj7+1vsgv3ixYvy9ddfS9++faVbt27q/lBaVEJDQzUHPNO7oj///LO8/fbbeQ6I5mJ6Z/XUqVPi7u4uwcHBhYZIkdyB3wVNEW5rYmNjZcSIERbt6nHr1q0838/8ZmIsKERaqxuKaYjU6XSaloOHDx/K3bt3JS4uTn744Yc8Myaa06lTp2TkyJHSokULcXNzk5YtW6pj8AwGg3z44YcydepUdcxUTEyMNGvWTEJDQ9UxnJakhLOPPvoo35t4X331lUyYMEENvikpKVKjRg3p169ficwcefz4cdHpdDJ79mxN4DIajeqF8rlz58Tb21szni8jI0MSEhLE399f8wxQc7t06ZLs378/z+QVpgH3k08+0dw4GDBggISGhqphuCQsX75c6tevX+jvzMjIkHHjxkmdOnUkJydHNm7cKK6uriXS7VIkN0Q2b95cQkJC1HPciRMnZPXq1eoxKCcnRzZv3ixt2rQpsen8lfCXlJQkwcHB4u3trQmRO3fu1Mx0efToUenSpUuJDXvIzMyU7OxsmTt3rnh6ekq/fv3Uz+ODBw8kPDxcvbmSlpYmK1eulPr168v7779fIvUp34VRo0aJm5tbnrHJipiYGBk8eLA61CE4OFh8fX01Ny/MfTyMjY2VAQMGiKenp3ozsXnz5vL+++/L9evXJT09XTZv3qw5Bx4/flw6dOhQosNaDh48KDqdTnr06KG5MZGQkCDBwcEybdo0dSKzefPmqcdK5Tj4xRdfWOS7q7wfQ4cOlRo1asjo0aPzdJuNiYkRX19f8ff3V3sSZmdny+XLl8XPz6/A8a3/dv/qAKl0OZo2bZr65VOmN27WrJnUrl1bJkyYkGcCCxHRTPNvqVafnJwcmTdvnvqFM1VYiMzJyZEdO3aIt7e3/L/27jMgqmvrG/ifZu8FTSyxJQMi0hQLNhRRUREkgggqNkDFAipWbGAXlYgaazTG3rCXxBZbEoqidBAFlKLSpJeZ9X7gnfMwgMZ7L0OR9fukMwNsDufsc9Yuaz19+lQubSuu+AP4/v37PxtELlq0iPLy8ujevXvUr18/uY4a+/v708CBA8nKyopsbW1p+vTpNHr0aGF5W2BgIGlpadGPP/4oEyTm5ubSjRs3aODAgTRkyBC5dGrh4eE0evRomTIhQUFBZQaRJW9I1tbW1XJETB6BWmhoKPXt25esrKzo/v37FBMTI/N+yVpY0iDSyMiIDhw4UO7t+U+FhISQiYlJqQDyU4FPeZ+Lfn5+1LNnT5o1axbt3r2bTpw4QevWraNbt24Jn8nOzpYpUSRNY14R2WA/l9RM+gCam5tbKqnVu3fvykwsVd4yMzPJ3t6eDA0NZWrC/fbbb7Rw4UIyNzendevWUWhoKCUmJtL06dOpa9euNHLkSBo3bhyNHDlSpg8sb7GxsaShoUEGBgZkZmZGN27ckCnSLVVWTUBp8FNRe7qOHz9OWlpaQubNT91Xf/nlF1JTUxPq496/f79CE1r4+vrSwIED6ccff/xkvUuxWCycnxW1KkR6DOLi4soMIqXtKLl0VN7te/HiBU2ZMkVY/rxnzx4yMDAgBwcH4ZqOjo6mvLw8mUzo586dK7ea2V8iKyuLLC0tycvLi06cOEGDBw+m8ePHl2pDfn4++fj40NKlS2nJkiW0b98+uT7D6Orq0qJFi+j06dMUERFBjx8/ppkzZ5KWlhYNHz5cWPZZcnuQtC0Vcf4VL8GhoaFBEydOlAkit23bJgS/e/fu/eTKBqLyP4bFA0gdHR0SiUQ0c+bMUquiXrx4QTNnziR9fX2ytLSkqVOn0vDhw+XaP1d3NTaA9Pf3/2TCnMTERBo/fjxpa2vLPNRJT0Rp8Fg8W2h5X6TSm3diYqIQRJas7Vc8iCy+nFVaqsPe3l6unUfxi6r4z/m3IHLChAly368izUbr7u4u88BUPFvuu3fvKCYmhrS1tcnCwoICAgKE4Lt///5kZmZW7tlWpefQo0ePqE+fPjRy5EiZNhUPIgMCAko9vAUGBtLIkSNLpdivicRiMf3666+kpaVFpqam1L17dxo2bBjt2bOn1H5BaYZgoqJ9OPb29qSlpVXmEu+KFh4eToaGhmRqaipz05X3g3t4eDgNGDCAPDw8ZPqP4uf6mTNnZG60Ja9Tec7eSoNHDw8PmdkdoqLrYO7cuWUGiWUtYZanc+fOUf/+/YV7xebNm0lTU5MmT55M06ZNo969e9OAAQPI19eXsrOz6ebNmzRv3jxatmyZzKi7vAaqtLW1adasWbRq1SrS0NCgoUOH0s6dO8tcVl78OiGq2CXx0mWMxWuylrU37/nz50I5luLk+bcumSGyeBD5qYQl8va5erpxcXFkYWHxySDyU18vD9KZqUmTJlFoaKhMEGlvby8EkdK2lzznKmqFSHp6OpmYmNDly5eJiOjw4cNC7VFpEPm5a6O82xkTE0NDhw4ld3f3Mmey9+/fT7169aIRI0YI7ausRGtEZQeR0mv048ePZGFhQdbW1qVq9sqbdCWIubk5nTt3ju7cuUMikYhmz55dqg9MTEyk33//nRwdHWnevHm0adOmCsu4Xx3VyABSOvM0ZcqUUiMh0pMkKSmJxo0bR0ZGRkLiA6KKKXIfHh5OxsbGwgj/u3fvhBGczwWRycnJlJSUVKq4bnk/BERFRZU54lsyiDQyMqI5c+YIAVJaWhpt3bqV1NTU5JomPC0tjWbMmEFLly6V6ayKb4x2dnYmXV1dCggIoNevXwtB5O7du2nAgAFyC26lo9J5eXn08OFDMjY2pmHDhpUZRI4ZM4bu3bsnvB4eHk6WlpY0fvx47sz+v9DQUDIwMKDLly+Tv78/LV26lDQ1NWnQoEE0Z84cCgsLE4Kj4jfXqKgo2rBhQ5U5jqGhoTRixIgKSTokkUiooKCA1q1bR46OjjL7ZIpfIw4ODsLeqU/NtMiLtFbdsmXLiKiob5H2L8HBwaSpqUkLFy6Ua/H1f1P8fFq3bh3p6enRiRMnyMbGhu7duycMTN6/f19YBfGpfevyPA+9vb3JwMCAUlJS6OHDh+Tm5kYaGho0aNAgmj9/Pj179qxUuypjL3VKSgqZm5vToEGD6Pbt22W2pbCwkPbu3UvDhw+X6/K8Z8+e0ebNm+nmzZsUERFRal9w8SBy7NixFBkZKbe2lOX58+fk4uJC27dvp7/++otCQkJK3adiYmLKfIapyL9tyaDCxsamVBBZfDlrRfXHGRkZ9P79e0pOThYC2I8fP9Lly5dlAtkjR47QkCFDZIJIedd/Ll4WyczMrFSQU/wY7du3j7S0tGjLli2Vfi8Ti8VCG8qaiVy5ciX169dPWKlRke1NSUmhXr16CaXFrly58skgsiw8A1m2GhdA/vPPP6ShoUEikYgGDx5MV69eLTX1L/2/dDnr0KFD6dixY/TmzRuytraWe4Hsu3fvUvfu3WWW4/1bEKmurk4bNmwgCwsLuQa3ycnJNGDAABo+fPi/BpG7d+8mAwMD2rVrl/Cgl5qaSr/99ptclxy9evWKBg8eLJNVq3hn5eDgQDo6OjRq1CgaOXIk+fr60qtXr0hPT49EIhGNGzdOLsFjWFgYzZgxQ+iw8vLy6MGDB2UGkdHR0TR48GDq27cvTZ48mRwcHGjMmDE0btw44W9b2TeMylIyaNi0aRPp6+sLs1FRUVHCtSJdwnz9+vVPLmmsKsexIpMO5eTkkLGxsUzih+IPRPb29jRs2DDatGkTiUQicnFxoejoaLm2Serjx4+0bds20tLSovnz58ssIQsKCiItLS1auXJlmfvSiSr271l8WeCMGTNITU2NpkyZQqmpqTLH08fHhzQ0NCo0mZT05/v6+gqZVaVtTU5OptGjR5NIJCJtbW368ccf6dq1azKZGytDUFAQaWtrk6mpKd24cUPmvcLCQnr58iVZWlrSkiVL5JpQqm/fviQSiUhLS4s0NDTI2NiY7O3tadu2bUJWZKKiY2tsbCxkZ62IGaB3796Rs7OzsCRQX1+ftLW1ycTEhGbPnk379u2jJ0+eUFZWFr1//57Gjh1L5ubmMkFkRc5UVbUgMjAwkGxtbWngwIHUv39/mjdvnjCLLL23FG/DkSNHyMjIiKytrYW+qCKC8Pnz55O5uXmZ7xX/+VOnTiUTE5MK7feeP39O+/fvpytXrlBCQkKZgzn3798nDQ0Nsra2poiICEpISKCePXvKdb/351y7dk1mIOjq1atlBpEcLH65GhVASpdEbd++nZKSksjY2JgGDhz4r0GkpaUlDRkyRCgkL+8CrNKseNIU/1KfCyKl2VnlWcOOqGgG7ejRo8Iyy38LIl1cXGjw4MFl7umSV4d38eJF6tq1a5mj/TY2NjRw4EB68+aNkFFy2LBhlJaWRoGBgWRnZye3JWVHjx4lfX19mjZtmpAK/nNBZHp6Onl6etKMGTNo/vz5tG/fvhpfvNbPz4+8vLxkZpb9/PyoX79+9MsvvwjH5/jx49S1a1eaMWOGsMfQ1NSUli1bVqmzVv+mIpIOERFFRkaSvr6+kHWu+PW5Zs0a0tfXF26qv/32G4lEIjp16pTcH5weP35MVlZW9Pr1ayHhxty5c0ksFlNMTIyQEbus4DEpKanUTFFF+vPPP2nw4MG0ceNG4TXpdZqamkoaGhrC8riK5uTkRMbGxsL/r1+/Turq6nTo0CHauHEjjRw5ktTU1EhTU5POnTtXKW2Uevz4Meno6FDfvn3J3d2doqKiKDw8nE6ePEnW1tYyWwvK+3yMi4uj1NRUioqKEkrrODs7k5ubG40dO1YYZJSuopkzZ46QuXP27NkUEhJSru0pKSgoiJYvX05nzpyhuXPnUu/evWnq1Kl05coVcnJyohEjRgjZm7t27UrTp08nKysr0tDQIDMzM7mXGitLWTNTZQWRjo6Oct83GhQURPr6+sKews2bN5ORkRFNmjSpzFwSUr/++isZGxuTsbGxsEdXXqSJyWbNmkXTp08norLv99JjeuHCBdLU1KywAb7c3FwhIZhIJKJBgwbR4MGDaf78+bRz5066f/++MFgbFBREGhoaNG3aNHr06BG5u7tT9+7dS5XTkKeS51LxxG/SINLJyalCB/e+FjUmgExISCBNTU1at26dsGz148ePMkFkyaUJxYNIY2NjuRYALr7xOTExkbS1tenKlStEJHsBfC6IPH/+fIVkosvOzqZTp06Rvr7+J4PIkrWdKmp0luj/OgXp3kfp8fvw4QMtX75cpvM6cOAAqaurl0o2JK/jJx3NtLOz+6Igkug/r1H5tZImvdq8eXOpY+Do6EgjRowgoqKCyiKRiHbt2kX5+fkUFxdHFy5cIENDQzI3N69SpU4+p7z/zm/evBHOuYyMDOrXr1+pLId5eXl09epVio6OFo5TTEwMiUQiunTpUrm2pyRpX7F06VLhNU9PTzI0NKQpU6aQpqYmubu7l5mAIT4+noYNG1Yptdik8vLy6J9//hEenoqvFHj06BENGDBAZo9reXv37h0FBwfT+fPn6c6dOzJJjl68eEE9e/akCxcu0Pnz54X7h7Sfi42NpXPnzpGDg0OVGJyKjIykGTNmUM+ePalbt24kEoloxIgR5OzsLLc9SaGhoaSnp0dz5syh5ORkioqKIi0tLZo2bRr5+vpSYWEhpaSk0J07d+i3334jBwcHGjZsmJCYQyQSyXV2xd/fn9TU1EgkEtG9e/coMjKSZs2aRf369ZMpPxAeHk53796lffv20eTJk8nc3Fxo38yZM+V6H/5PZqbGjx8vLL3ds2ePkNlZXgN8ubm55OTkRPPnz5fpQ3766SfS1NQss6B98XvFzz//THPnzq2w+++mTZuoR48e/1r/948//iB1dfUKrRMsXS0jHcDYuHEjGRsbk46ODnXt2lWYtd+5cydNnTqV1NXVacmSJbRkyRKysbGp1Htwyczh0udFGxsbYWUB+zI1JoAkKhr9Krlh+0uDyNTU1Aqd/TEyMqI1a9bItEXq/fv3tG3bNlJTU6MdO3aU+trybl92djalpKRQXFycsMQpNzeXTp8+Tb169fpkECmRSGjfvn1kampaoTMDsbGxpKOjQxs2bCj1XvH2ZWVl0bRp02jq1KnCa/K6uRbvMH/55ZcvDiKrS7Ajb8Xr7BWffZL+PaUZd6dNm0YikYh27NhRKtBISkoqlZW1pggNDaWePXuSi4uLMNI6depUMjIyKjXyWvwayM/Ppy1bttDo0aPlmnW1ZLbV4n+fbdu2CeVFpMv6i78vTWpWfPVFeSqrJExJJfvcwMBAOnLkCL18+ZJu375NNjY2NGHCBLmdd8+ePSMrKyvq378/iUQi0tDQoE2bNgn3u9TUVBo/fjwZGBiQhoYGeXl5yZTLKPl7VYUgMjMzk96+fUvXrl2jGzduUGRkZKn7cnny9vYmkUhEPXr0IFdXV0pLS6PQ0FDS0tIic3PzUnuTpW0JCwuj+/fv065du+R23Hx9falr1640a9Ys6tWrF+3Zs4eIiAICAsjBwYF69epFv/76a5lfKxaLKTg4mB48eCDX/Xv/7czUixcvqKCggLZu3UqOjo5yu0Zyc3Np5MiRwjOT9FjEx8eTSCSSSTZUXFmJh8r7/Cs5+JOcnEx//vkniUQi2rhx4yf3++bn59PmzZsrJSgLDQ0lU1NTsrW1FfY1JiYm0q1bt2jv3r00d+5cGjBgAA0YMEA4J3bu3FkqG/D/6kv655Kvlwwiz549S1ZWVjXuueB/9dUHkJ/rKKUdyL8FkSU38Zenp0+f0uLFi+nXX3+lhw8fCqNI48ePL1VIt/jPTkxMpO3btwtLy+QlODiYZs+eTYaGhqShoUG9evWiP//8k4hkg0gLCwuZkUOJREJRUVH0448/0tKlSyt0z8XHjx9p4sSJ1K9fP/r999+F10t2Dr6+vjRmzBiZTLvyIP25xY/P54JIaTHokiUpaqqySjmUvDaTk5PJxsaGRCIRHT58mLKysoSvL3nu1cSbhPThuGfPnrRkyRJ6//49BQQECIlqigeHxc/TiIgIMjc3Jw8PD7ldw8WzrZYs1SG1Y8cOGjRoEM2bN08m42Dxckry3lpQXFkPLVevXqWtW7cSUVFN3O7du5NIJCJDQ0OaPHmy3PYuBwYGko6ODq1atYru3r1LGRkZdP369VLLKR88eECampq0aNGiL/q9qip5Xb85OTlkY2ND+vr6pKGhQc7OzpSRkUHh4eGkpaVF48aNo7/++kv4fEWV2pFeH9JAYujQoeTs7Cy8/+LFC3J0dKQ+ffrIBEEV1b7i/puZqTlz5tCzZ8+osLCwzGeu8mRsbFzquSo1NZW0tLRK1fQu7kuClP9WycEfdXV18vT0JCKixYsXU7du3ejw4cNCEFl8+farV69o/PjxMnvZK1JoaCgZGxuTpaWlzLUhlZmZSfHx8XTlyhU6dOhQhZUW+bf+ufh7/Hzw3/uqA8iwsDC6cOGCTK09Itkb+KeCyIpYppCbm0u2trakr69Pmpqawr6FUaNGkYGBARkaGtLp06fJ39+f0tLSSs3ipaWl0fHjx+V2QwgICCB9fX1ydnamX375hU6ePEne3t4yyz1zc3Pp3LlzZGBgQKNGjSJ/f396+fIl3bt3r9R+lYp8OAkNDRWSMdy8eVPmvby8PIqMjCQrKyuaMGGC3OoT/vbbb+Tr6yvM2pbsmH755Rfh4bJ4Yp2HDx9Sjx49ZB4SaippuZ1NmzYJM4rS4xgREUGHDh0SgsVHjx6RSCQSMq2x/1Py4djFxYVCQkJo//791LVrV1qyZAm9ePFC+LxYLKaAgACysrKSWbpf3tfw8+fPSU1Njby8vCg7O1um5u6LFy9o9uzZwqyFp6enEES+e/eOMjIyyNLSUq7BY0xMDJ05c4bc3d1p69at5OvrK+ytLiwsFNoqXQa1d+9eIio63mFhYXTv3j0KDg4WPlfe7fvw4QNNmDCB3NzcKD09vczPSI9NUlISTZ48mWxtbSu0wHh1IBaLSSKR0P79+8nNzY127NhBIpGI5s6dWyqILF4zWN4PmyUHz3Jzc8nCwoJsbW1lPvepILIyHob/m5mp4kt/5fGcID0Ohw4dIicnJ2FwVppQqkePHqUGkj9VVqQ8fWrwR9oXJycnk4ODA2loaJC7u7uwzDYjI4Pu3r1LEyZMkGv//CVCQ0Np+PDhZGVlJVNapyIGMP7b/rks1WHwrKpRICLCVygpKQkmJiYAgHr16sHKygp9+vSBnp6e8BmJRAJFRUUUFBRARUUFGRkZ+PHHH1FQUIA5c+ZgzJgxUFRUlGs7MzMz0aBBAwQFBSE5ORlRUVEIDQ1FamoqHj16JHyuXr16+Oabb6ChoYFvvvkGTZs2hampKZo1awYAKCwshLKycrm1Ky4uDjNmzICBgQGcnJzQtGnTUj9H+u/8/Hw8efIEXl5eiIyMhKKiItq3b49vvvkGu3fvhrKyMsRiMZSUlMqtfV/iyZMnmDVrFurWrYthw4bB1NQUYrEY9+/fh6+vL8RiMY4fPw4VFZVybV9qaipsbW3x8uVLAMC3336LRo0aQV1dHb169ULz5s2hr6+P2rVr4/Tp0zhx4gQaN26M5cuX4/vvv0d+fj7CwsKgoaFR4cesKklKSoK1tTUkEgnu3bsHAMjPz0etWrUQHBwMCwsLODg4YM6cOVBWVsb79+/h5OSEWrVqwdPTE6qqqpX7C1QREokECgoKOHjwIGJjY9G8eXPs2bMHo0ePxtixYxESEoIdO3agRYsW0NLSgkgkgp+fHz5+/Ii6devi4MGD5X6NAEV/y9mzZ+PBgwfYsWMHhg8fLvyMkJAQWFpaYvTo0Vi5ciXq1q0LANi2bRuuXLkCNTU1vH37FgUFBbh48SJUVFTKvQ8MCAjA4sWLoaysjMzMTOTl5SEjIwM9evTA4sWL0a1bNwDAtWvX4OLiggULFmDatGlQUFCAgoJCqe8nvd+Up5CQEDg7O8PNzQ39+vUDABBRqZ+fmZmJuLg4PH/+HKtWrcLRo0fRs2dPubSpOin5+4eFhWHKlCnYuHEjCgoK4OTkhGHDhmHdunWIj4+HpaUlRCIRFi5ciJ49e8q1bU+fPoW1tTWmTJmCefPmoU6dOgCABQsW4NmzZ7h8+TJq1aolnPMvXrzA7t278fz5czg5OcHa2lqu7fucsLAwzJs3D02aNIGLiwt69eol835WVhY+fvyIgIAAvH//Hra2tuV67X5Kamoq0tPT0aFDB+G1hIQEDB48GGvXrsW4ceMAAPHx8fDy8sKcOXPQtm1bubQlOTkZc+fORefOnbFw4UI0atSo1GcKCwuRl5eHTZs24eLFi8jPz0fHjh3x4cMHtG7dGt9++y28vb0r7RlLKiwsDM7OzmjcuDEWLFggXBtl9UXl5b/tn2tyf1fuKjd+lZ+UlBQaMWIEWVpa0m+//Ua6urrUu3dvmj17Nv3555+lZvOky7YyMjJIT0+PZs2aVSHt/NSox9WrV6l379508+ZNunTpEm3cuJGmTZtGBgYGJBKJaMyYMXKdJb106RKZm5t/MjOVtN3SVOFERSM+165do8uXL5Ovr6/cRt3/ExEREeTg4CDMvEg3S2/evFluCYfy8vLo2LFjZGVlRSKRiLZs2UILFy6kfv36CbPMBgYGNH78ePL09CRTU1Pq1asXTZ069bM1n2qaxMREWr9+PWlqasokewkMDCRtbe0ys3EePHiQNDU16cmTJxXd3Cqn5Mh5aGgo9e7dm+7du0e3bt0ikUhEixYtotDQUAoKCiJnZ2caNWoUmZiYkIuLC507d07u+76jo6Np8uTJNGDAALp16xYRFc2mlCzVUXJPpEgkorFjx8pt5tHX15c0NTVp9erVFBwcTHl5eZSRkUGenp5kYGBAenp6wp64qVOn0v79+ytltufo0aOkp6cns5+xpJycHFq+fDlpaWlRUlISjRkzhiwsLGp031IyQ3fxme+DBw+SiYkJ5ebm0pkzZ0gkEtG8efOEmUhdXV0aMmSI3DOuRkREkKenZ6k+ztPTk3R1dYX7bnEvXrwgJycnUldXp+vXr8u1ff+mMmem/hNxcXEkEomEpIVv3rwhKysrMjY2lmubgoODydjYmB48eCC8VtbSy6ysLAoLC6PQ0FA6ceIEbdu2jQ4ePEhPnz6tEs9YUsX/3vKuZVxd+uev3VcXQGZkZAgX082bN0lXV5eCg4MpNjaW9u/fT4MHD6bevXuTmZkZXb16tcwCwFlZWXK7uebl5VFmZiaFh4dTcnKysCyv5ObshIQE6tatG50+fVrm6zMzMykxMVHuyUCcnZ3Jysrqs5/Jzs4mS0vLMpPVSFWFizY7O5uSk5MpMDCQnj9/LnMDK++N3NLfV7o/1MjISKaW07Nnz+j333+nlStX0qRJk2jw4MHC8mV5Z/GrjopnHd64cSPFxsaStrY2LV++XObBSnrcs7KySFdXl1xdXSuryZXuSx+OT58+TSKRiObPny88jEokklJp6uUdaMTExJCNjQ0ZGhrSoUOHSEdHh1asWFEqKCrel5w6dUpuwa102eCnMr5KM/r26tWLYmNjSyX9qUjHjx8nLS0t4W/2qXb88ssv1LVrV4qOjiZXV1eytrauEn1zZXjx4gUNGDBAqP8nXforPZ9CQkLI2tpayGx69OhR4TrJyMig4OBgmjRpUoUH4NKfd/ToUdLU1BT2zhcPOjIzM8nDw4N++umnKjFA8KkgsrKXCxZP5BIYGCiUTXv//j2NGzdOplybvI7jlw7+LFu2jDQ1NT+5RL0qXccVUcu4OvXPX7uvKoCMiIggMzMzYX9HZGQkjRo1ilavXk1E/3ehTZ06VXhgHzBgAG3bto2Cg4NLdRTySBO+YsUKMjIyoh49elDv3r3JysqKbt++LdQ/knr//j3169dPZnO0vNtXnIuLC9na2pJEIvnkiKFYLCZbW1saOnSoTNKS6kJeNzHp983JyaFz586Rvr4+jRkzpsxjFBcXR6GhoXT27Fnavn17lRhJrGqkQaR0BtnDw0OmtlhxBQUFMuVsapr/9uHYxcVFeCCtDDExMWRra0saGho0efLkT36u5N+8vP/O0uyQ7u7uRCT7cFb832fPniU9PT2aPXt2mQ8xFeXFixekqalJP/30k/Ba8X5N2ubnz5+TSCSiiIgIysjIqJD9XVVRTk4OjRw5Urj/Dx8+nFxcXMjf31/mc25ubjR48GDh/Dp27BiJRCKys7OTGbiqjCDN19eX1NXVhRl7qbi4OLK0tCR7e/tyz3T5v6jImamSSv7+0mujeF6MgIAAEolEdPToUbK2tq6whFz/6eCPdN9mZQff/0aetYyrW//8tfuqFgOnpqYiNDQUP/30EwCgS5cuMDIywoULF/D27VsoKiriypUrePToEVasWAF3d3eIRCIcPnwYY8eOxa5du2S+X3muJ/f398e0adOQnJwMExMTbNiwAWPGjEFWVhacnJywa9cuvHv3Tvh8ixYt0KlTJwQHBwNAmevby7N9KSkpMv9v3rw5wsPDkZaWJux/Ko6IoKioiG7duiE3N7fU+9VBea3Nj4uLg4+PD1xcXDBz5kysX78eT548QZ06dWBmZoZly5bh3bt3sLa2Rl5eHoCi/V8A0KZNG6ipqcHCwgLz58+HsrIyCgsLy6VdX4uWLVvCxsYG06ZNQ4MGDZCdnQ1FRUUoKSnJHCsigrKyMszNzWvkcczNzcWSJUuQlJSEGzduwMnJCWvWrEFAQIDQV6irq6NLly7w9PREYWEhbG1tsXLlSly7dg1btmzBmzdvKqXt7du3x7p166Cjo4Po6Gjcvn0bQNHflIpt0y/Z55XnvimJRILjx49DIpFASUkJYrEYioqKwnmkqKgIiUQCALCwsMDo0aPx4MEDJCcnl1sb/lNt2rRBly5dcP78edy5cwdAUb8mbaeioiLEYjGePHmCjh07onHjxmjQoAGUlJRq5P7HOnXqYMuWLRCJRNDW1kbr1q1RUFCACRMmYPXq1cJ5t2zZMrRq1QoHDhwAAEyYMAGurq7Izs5G7dq1he9XGXvOGjVqBCJCWlqa8Fp8fDwWLlyI9PR0eHt7C+dqVdhDr6amhu3btyMzMxPLli1DWFiYXH7Oq1evcPXqVfz666/4559/kJ+fDyUlJaEPke4Fv3z5MoYOHSq0o3379mjWrBk8PDyQkZEhtz3VJWlqakIikeC3334DUHStFu/rpNewnp4exGIx3r9/D6D8nlvkpVu3bvD09BT60PJSHfvnr17lxa7lLyMjg1atWkVGRkZ0//59Iiqq8WNsbExeXl508uRJEolEtH37dmEUMTU1lQIDA2nlypVyG22Kioqifv36kbu7e6laahEREbR69WpSV1en7du3y6SydnV1JSMjI7nUNivuyZMnpKGhIbPkwNfXVxjB+VzhZmdn58/OGHztAgICaPjw4WRubk6WlpZka2tLffv2JZFIRMuWLaNXr16RRCKhCxcuUJ8+fWjMmDHCbHNNnSX7nM+NWiYmJgrLWYvviawKo+xVRUhICJmampKVlRXZ2dnRnDlzSCQS0apVq+iPP/4goqJZGGtra6GWHFHR0taqUAdLupx1wIABMiV45DnqXvx7Jycnk6urKw0cOJA2bNhQZtp56b9fv35NWlpapbYZVLSgoCAh4/SNGzdk3issLKSXL1+SpaUlLVmypMrPXlSUkJAQMjMzI1tbW7p27Rr9/vvvZGJiQgMGDCAXFxcKDAykVatW0fLly0uVpyKqvJlbiURCGRkZ1KtXL6EkQWxsbKWUsvlPyXNmyt/fn/T19cnIyIi6d+9O/fv3J3Nz81KlsC5fvkyampq0e/du4W+YkpJCQ4cOJUtLS7nlRShLSkoKmZub06BBg+j27dvC6yXLxu3du5eGDx9eYzMnV/f++Wv2VQSQxWu5REdHU+/evYVaVwUFBeTi4kIaGhqkoaFB3t7eMlPaJW8E5dlxSNvk5eVFNjY2MssmigeFycnJQhD5+PFj4fXdu3dT9+7d6dWrV+XWppJ8fX1JS0uLVq1aJdRgIyp6yFy8eDFpaWnR6tWry9xvJF0yvG3bNrm1ryp7/vw56erqkru7u0xChdDQUNq+fTupqamRg4MDvX79mgoLC4VyJ2ZmZkIQycFP0fHasmXLF9WIKr4nctWqVcLrlR34VCXV9eFYShpEDhw4UObBSl6k5UOkD2ipqam0cOFC4SHlU8s9Y2NjSUNDg86dOyf3Nv6bx48fk46ODvXt25fc3d0pKiqKwsPD6eTJk6XKKVX237eqCAkJIRMTE7K0tKTo6GhKS0ujc+fO0cCBA2ns2LFCErQLFy7IfF1VCMJNTExo7ty5FB8fT1ZWVjJ79qpi8FhSed733r9/L2xVio6Opry8PDp//jyNHj2aDAwMhGcqX19fEolEtGfPnlLXQHh4uNwThpWFB3/+3dfQP3+tqnUAGRYWRoGBgaVev3LlCqmrqwtZtWJjY2nQoEE0derUim4iERFZWVnRwoULP/uZ0NBQGjp0KM2cOVOYHT1x4gRZWlrK7Yb/uQLtREWB7fTp06l79+40depUio2Npby8PBKLxfTw4UOysrKiH3/8sVJrEFWWjx8/kr29Pbm6upa5uV0ikdDx48dJTU2N1qxZQ0T/tyeyf//+1K9fP5mH95pIIpFQYWEhLVmyhHR0dGjTpk1ftDcrKSmJtm3bRl27duVamZ9QnR+OiYqCyIkTJ1LXrl3lum8qKCiI5syZQ6NGjaJRo0YJxyMtLY1cXFxo0KBBMg8pxYOwmzdv0qhRo2Tq4lamyMhImjFjBvXs2ZO6detGIpGIRowYQc7Ozp9dRVKTFS+ELv075uXlkbe3N82fP59EIhEtWbKkchtZjLRfnD59Oo0dO5bMzMxo+PDh1Sp4LG9hYWFkYGBAjx49El7Lz8+n0NBQmjRpEvXu3Zt8fX3p77//pitXrsjcW0oWkq+M64MHfz7ta+qfv0bVNoCMiYkRNsJ7eXnJPGQkJyeTi4sLWVpaUlRUFOXl5dGiRYto+PDhFBUVVaHtLCwspFGjRtG6deuE/3/K4sWLydjYWLgZJCQkyG0zvDR4XLdunRA8Sn9WXFwcvXz5koiKLlQPDw8yMDCgrl27krGxMfXv35+GDRtGU6dOlXumsqoqKSmJDAwMPrs8QiKR0KZNm0gkEsk8nBw7dowcHBxq3DErSRpAS5ellLwZlLxZFv9/cnIyrV27tkosu6yqqtvDcUnR0dG0Zs0auV0nvr6+pKurS46OjjR37lwyNzcnkUgkFBRPSUkRRrrXr18v9HVisZhiY2PJ0tKSHBwcqtT5l5mZSW/fvqVr167RjRs3KDIyslSGbyYrNDSUhg0bRlZWVsJslUQioZycHLp69WqVDMq8vLxIJBKRmZlZjQ4eiYr+fr169RJWKxQ/z1+/fk22trZkaGgobB+qSterFA/+lPY19s9fm2obQEZERNDo0aNpyJAh1KdPH7KwsJApJ/Hnn3+SoaEhHTt2jIiIbt++TSKRiE6ePElEFTPSnp+fTwUFBWRmZiYz+1nyZ0tP/F9++YX69+9PSUlJZdYDKi9+fn6kqalJ69atK1Vn7cWLF9SnTx86ffq00HHl5eXR69evaf/+/bRhwwby9PSkP/74o0rVIKpo9+/fJzU1NUpMTCSiT/+Nnj17Rrq6urR//37htYKCghr/UBcSEkIrV64UBipSUlLKHFGUnmPS45WXl0fPnz+npKQkys3NrTLLLquq6vhwXBZ5DaBt2LCBUlNTiajoIc7JyYm0tLSEjLRpaWmllkulpKTQuHHjaOTIkdVmZqCqt6+yFc8U+vfff5d6v6pdJ7GxsbR27doK3bNXVZS816anp9OgQYNo3rx5wmvF+4ugoCAaMWIE2draVulVPzz4839qWv9cXVXbADI3N5fc3d1p3rx5dO3aNdq6dSv16NGDRo4cST4+PiSRSGjXrl2kq6sr1EWbOXMm9ezZk5KSkuTWrtDQUFq+fLnMa4cPHyaRSERnzpwRXivrhHZxcSEHBwe5tY2oKBFJz549ydTUtFTnJF2Pv3Tp0lKzkp9S0zo2Kel+Cumo5+eO06hRo4RlrMWPV1VZLlgZtm3bRurq6uTm5kavX78mok8HkdIBlvz8fLp69aqQFEuqJh/HL1HdHo7lzc/PT1h9kZWVJXP+XL58mdTV1WWWw0kfUgwNDcnNzY3GjRtX5ROWsP9cRdSwkwc+/4iuXr1KampqtGvXLuE16f0jLy+Pjhw5QgMHDiyz7ndVV9OCH+6fq49qmcNbIpGgdu3amDZtGv766y+EhYVhwYIFOHfuHFRVVeHl5YVp06ahT58+0NHRwfr16yGRSKCtrY1OnTqhRYsWcmmXWCzG1atX4ePjg2XLlgmv6+rq4ocffoC3tzf++OMPALIphwEgMjISsbGx0NPTk0vbpAoKCjBy5Ei8fPkShw4dAlCUijw4OBg2NjYYPXo03NzcUKdOHaGd0q8rS1VIE14RoqKi8M8//wgpozt37oxmzZp98u8plZqaitTUVLRq1QqA7PGq6um45cnZ2RkzZszArVu3sHfvXrx+/RpNmzbFihUroKenhxs3bmDz5s3Iz8+HiooKcnNzcefOHWzevBlKSkqYNWuW8L1q8nH8EsXT6K9YsaJUGn15pqqvasLDwzFt2jQYGhpi2bJlqFevHhQUFISyOq1atULt2rWF/k4sFqNx48Zwc3ODvr4+Tp8+jcLCwgpL9c8qjpqaGjZu3AhNTU18//33ld2cL1aTzr/Y2FicPXsWHh4e8PT0hJ+fH1JSUmBiYgIbGxv89NNPOHjwIAAIpR5q1aqFwYMHIzExEa9fv67cX+C/UJNK7XD/XM1UdgT7pYKDg0sVziUiunjxIunq6spk6zt79ixNnTqV1NTUyNramkaPHi3sAZJ3gd3k5GTavHkz9e3bV8gES1Q0QmZgYECDBg0SltESFY2OBAQE0Pjx48nKyqpCZvQSEhLIw8ODRCIRnTp1iiIjI0lLS4vc3NxkiiRLvX37lg4fPkxv376Ve9uqosjISBKJRGRoaEh//fWXsAxm69atJBKJ6NChQ8JnJRKJzLLLP//8k8zNzYWZH54tk732tmzZQr169aKlS5cK2YZTUlJowYIFNHDgQNq4cSNlZ2fTvXv3qF+/fjRmzJgauWyrPMgzjX51cenSJerVqxfZ2NhQQEAAEclmxLa2tiYLCwuZr5Fez6mpqbRr1y4+/2qImnydVEX+/v5kZGREw4cPp379+lHPnj1JJBKRra0thYeHU15eHi1dupREIhHt3LlT5l776NEjGjRokHDNs6qJ++fqRYGoWOXSKiovLw+TJk1CYGAgbG1tYWFhAXV1dQBFI1Lr169H48aN4erqiubNmwMoKqp97tw5HD58GHFxcZgxYwYWLFgAoKg4tTxnLVJSUrB//35cunQJffv2xZYtWwAA169fx6FDh/DixQt06dIFqqqqSElJAQA0bNgQhw4dgoqKCsRisdxn9hITE3HgwAGhiO2ECROwaNEi1K1bV+Zz8fHxmD17NvLz83H58uUaNRomFRsbi1GjRiE/Px8dOnTAqlWr0KdPH0RGRmLlypUIDQ3F/PnzYWdnJ3xNYWEh4uLi4Obmhvr162PPnj018tgVV/y8Lv7vrVu34uzZsxg8eDAcHR3Rvn17pKWlwcPDA/7+/lBTU0NQUBCaN2+Os2fPQllZmUcW/0cV0cdUVVevXsWOHTvQokULuLi4oGfPngAABwcHREZG4tdff0Xbtm0/eb4C4POPsQrk5+eHqVOnwsLCAuPGjUOXLl2Qn5+Pffv24ezZsygoKMCRI0fQpUsXbNu2DUePHkX//v3Rv39/KCgo4Pz581BRUcGxY8dq/H24quP+ufqoFgEkACQlJeHy5cvYuXMnOnbsiOHDh8PR0REAcPHiRaxcuRLe3t7o37+/zMkUEhKCp0+fwsrKSi4nVHZ2NvLy8pCSkoJ27dpBUVERysrKSE5Oxv79+3H58mX06dMHW7duBVC0FPLly5e4fv06CgsL0aZNG+jo6GDo0KFQUlKq0BM/ISEBJ06cwKFDhzB79mzMnDlT5v34+Hi4uLggKytL6IAlEkmN6oAlEgkkEgm2bNmCwMBAZGVlITk5GZ6enujTpw8CAgKwbds2+Pn5wdjYGD169ECXLl3w4MEDPH36FPn5+Th9+jSUlZVr3LGTiomJQevWrVG7du3PBpHnzp3DyJEjMWPGDLRq1Qrp6enYsGEDfHx80K1bN5w8eZKDR/Yfe//+Pd6/f4+QkBCoqqpCX18f9+7dw/bt29GyZUssW7YMe/bsQVBQEHbv3i0MTjLGKp+fnx/s7Owwfvx4zJ8/Hw0aNJB538fHBzt27EBubi6OHTuGzp074969e9i6dStSUlJQr149dOnSBTt37qywAXr25bh/rr6qTQApFRYWhm3btiE0NBTt2rWDu7s7OnfujJUrV+Lx48c4e/YsmjRpUmYnUd4PnsHBwdi7dy+CgoKQmZmJVq1aYejQoTA1NUWHDh1kgsjiM5GfIq+O7XPf9+3btzh8+DCOHj2KhQsXYvr06cLrLi4uyMjI4PXkAG7fvo1ly5Zh2bJl+OOPP+Dn54dt27ahT58+iI6Oxr1793Dq1Cm8e/cOBQUF6N69O7S1teHi4lKjg553797BxsYGrVu3xoEDBz4bRG7YsAGnTp2Cm5sbxo4dCwUFBaSlpeH48eOwt7ev0ceR/XcCAwOxYcMGxMfH4927dwCKRrKdnZ1x7tw57N+/H6mpqVBSUsKZM2fQpk2bSm4xY0wqODgYVlZWGD9+PFasWCEzCFv83+fOncPGjRvRq1cvbNy4EQ0aNEBWVhbS09NBRPj222+hoKDA948qhvvn6q3aBZBAUWISX19f7Nq1C8nJyRg/fjzatGmDmzdvolu3bnBwcICKiopc2+Dn54eZM2eiR48eEIlEaNKkCf744w88e/YM3377LTw9PaGpqSkTRBoYGGDz5s0AipLSyLONL168wKVLl7B8+XIAnw8iExMTcfDgQRw9ehSurq4YM2YMZs2ahczMTPj4+NTY4LHkjOHSpUvx5s0bLFmyBBs3bkR0dDS2bt2KPn36AChaNp2eno7s7Gx06NBBWCZdk0c8c3JysGvXLly9ehUikQheXl6fDSLt7e2RkJAAHx8fuQ8Asa/b8+fPYWdnB1NTUwwaNAg9evTAw4cP0bZtW3Tr1g1A0ezFrl270KBBA6xatQra2toA5L/NgTH2eRKJBG5ubrhw4QImTpwIV1fXUqu0it+j16xZg3PnzuHSpUvo0KFDmd+vJq4Aqqq4f/4KVM7Wy/Kzfv16mjhxIo0aNYpGjx5NDg4OlJycLNefKa1R4+7uTu/fv5d575dffiEDAwPq0aMHBQUFEVFRYp2NGzeSgYFBhRTuLigoEAoNr127Vnj9c0kB4uPjycPDg7p27Ura2to0atSoGpkGOSEhgYKDg2Vekx63+/fvk62tLfn6+lJcXBxZWlqSgYEBPX78WGajtzxreFYnxctv7N27lwwMDMjBwYFyc3OJSPZ8lCYmunr1Kunr61NsbGzFN5h9NT58+EATJkwgNzc3Sk9P/+xnz58/T8bGxjR+/Hjy9/evoBYyxspS/J6ZnJxMrq6uQp2/sur6Sf/9+vVr0tLSotOnT1dsg9l/jPvnr0O1HY6h/z9xunTpUjg5OWHIkCGIiIjAvXv3cPHiRbn93KdPn8LOzg52dnaYP3++UBJEmmbYzs4Ozs7OqF27NpYtW4Y3b96gWbNmsLe3h6mpKS5cuIDdu3fLrX1AUVpvKysrODo64tixY1i9ejWA/0trXZy09MQ333wDR0dHmJubQ1NTU9jzWJNmffz9/TFmzBjMnTsXe/bsQVpamszsWL9+/UBEOHToENq2bYtt27ahbdu2WLx4Mfz9/YVzsvjIWE0cJYuKioK3tzccHBzg5OSEGzduYPr06bC2tkZISAjmzZuH3NxcmfOxVq1aAIDo6Gi0a9dObqV2WM2QlJSEDx8+wNjYGI0aNQLwf/eM4nJyctC1a1e4uLggOTkZ27Ztw99//13RzWWM/X+5ubkgIqSkpKBZs2ZYunQpevbsiRs3bmDr1q0Qi8UyZbOks4qKioooLCyssat9qhPun78O1TaAVFBQEE44fX19zJ8/H4cOHcK4ceMwceJEufzMuLg4ODo64rvvvsPs2bPRoEEDoROrVauW8G8LCwtMmDABERERePz4MQCgadOmmD59Otzc3ODg4FDubZMeC2kbWrVqhfHjx8Pe3h4nT54sM4gkIigqKiI/Px9+fn5QUFDA8uXLceTIkRoVPEqP3dGjR4UlqF5eXnBwcMD27duRlpYGoOgGtWrVKkRERODmzZto06YNNm3ahO+++w5TpkxBcHBwJf4WVYO/vz/s7Ozw4MEDEBEiIyMRGBiI3NxcTJ06FZaWlggODsb8+fOFIFIqMjISd+/eRdeuXUtlA2bsPxEQEIDk5GTo6OgIr5UczMnNzYWHhwesrKwwdOhQzJo1C+Hh4bh+/XpFN5cxhqI9j4sXL4apqSkmT54MHx8fNGnSRKgPfPPmTWzZskUIIqV1mSUSCUJDQ9GxY0d06tSpkn8L9m+4f/46VOvooPgJR0To27cv+vbtC0A++6Xq168PLS0tvHr1Cnv27IGLi4swEqaoqCjz71mzZuHatWu4ceMGLC0tAQDNmjWDjY0NgPLfFyftSN++fYsmTZqgXr16aN26NWxtbQEA+/btAwCsXr0aSkpKwh7MgoIC3L17Fx4eHjA0NMTatWsBFB3PmhA8AkVlYurUqYMdO3Zg9uzZuH//PqZOnYpXr17h8ePHuHDhAmbPng09PT2IRCJoa2vj6dOnGDZsGNq3b4/Vq1fj6NGjNT47WEhICBwdHTFmzBjY2dmhbdu2SE5ORmFhIerVqwegaI9j7dq1cfToUUyaNAmenp5QVlZGTEwMvLy8QERYuXIlAN7nwP570r1S+fn5qF+/fpn7n+rUqYPvv/8eeXl5SEhIgJmZGZo2bYp+/fpVUqsZq7n8/Pzg4OAAfX19dOrUCXFxcViyZAnS0tJgZ2eHFStWYP369bhx4waICAsXLhQyw799+xYHDx5EmzZt0L1798r+Vdi/4P7561Atk+hUBukJnpycDHd3dwQGBmLUqFFCbcniF0B+fj5q1aqFNWvW4O+//8bJkyfRsGFDuT0Mh4WF4dSpU3j8+DE+fPiAevXqoUOHDpg7dy60tbWRk5ODAwcOYN++fRg/frwwG5mTk4M///wT69atQ7NmzXDmzBm5Jx+qaoKDg3H+/HmYmZlBU1MTADBx4kTExcVh8uTJ0NLSws2bN3H16lXUr18f9vb2kEgkWLt2LY4fPy58jVRNTZiTn5+P5cuXo7CwEG5ubmjWrJlwTWRmZiI5ORlPnz5F8+bNoaenh2vXruHQoUOIj49HrVq1oKqqClVVVezdu5dTrbP/WVBQECZMmIAZM2Zgzpw5AGQHJKTn5osXLzBu3DgcPXpUqDcG1NzrmLHKIC3VYWtrC0dHRzRp0gRRUVHw8vLCgwcPcPr0afzwww9IT0+Hh4cHfH19MXz4cCxatAgfP36Eg4MDsrOz4ePjU6NLZlUX3D9/HWrGFFM5kM4uNm/eHG5ubnB3d8eVK1cAAAsWLICioqJwUkv3c6WlpaFNmzbCGm958Pf3x7x586CpqQkjIyPo6Ojg4cOHePLkCezt7TFlyhRMmTJFKM+xb98+KCsrY8WKFXj8+DE8PDzQvHlzIXisKctWgaL6Q1OmTMHHjx+FGVd1dXUcPXoUEydOhLe3NxYsWIClS5fCxMQEv//+O1auXIm+ffuCiHD48GGsWbNGpi5VTe3UCgsLERISAnNzczRr1gxA0TXz6tUrbNy4EX5+fsjKygJQtJd0/fr1GD58OK5cuQIiQrt27dC3b19hWVJNOQeZfLRp0wZdunTB+fPnoaGhgcGDB0NBQUFmtYhYLMaTJ0/QsWNHdO7cWebra+p1zFhFk257mDBhAubOnStsX+jSpQuGDRuG27dv48OHD/jhhx/QuHFjrFixAh4eHrh16xays7MRFhaGzMxMXLx4kUs9VRPcP38d+Cr7D5QVRF69ehVAURCppKQkjKKEhYXh1atXGDp0qNzaExUVhXnz5sHExATTpk1Dq1atAABGRkZ4/fo1du7cid27d0NRURGzZ88Wls8eOHAAr169wuvXr2ts8AgALVu2hIaGBp48eYILFy4gPz8ftra2UFNTw9GjRzF58mRs2LABQNG+Vi0tLZiZmeHXX39F/fr1kZ6ejvr161fyb1E1SPckv3nzBgDw8uVLPHnyBF5eXsjIyIChoSFMTU3x8eNH7NixA97e3li7di3Gjx8v830kEkmNOgeZfDRt2hTu7u6wtbWFl5cXCgoKMGzYMGFWQiwWIyYmBrdv34a2tjaaNm1ayS1mrOYJDw/HtGnTYGhoiGXLlgmvS1dxtWrVCrVr10ZBQQGAouu2cePGcHNzw/r163H69Gl07dqVa1VXM9w/fx14Cet/QTrT+OHDB3h4eODp06cwNTUVlrPGxcVh4cKFEIvFOHnyZLl3aNIgddu2bUIh1m+//RaA7N7P3NxcLF++HHfu3MHx48ehrq6OpKQknDhxAj///DN++OEHnD9/vkaO2klHup4/f46ffvoJBQUF+Pvvv2FmZoYpU6ZAJBIBACZPnozAwEAsWrQIo0ePRqNGjZCdnY3k5GR8++23MoMGNVlBQQE2bNgAHx8ftGnTBqmpqfjw4QN0dHRgamoKa2tr4bObNm1CSEgIfv75Z9SpU6fGHzsmP0+ePMHs2bNRt25djBgxAtbW1hCLxXj69CkuXryInJwcnDlzhpe9MVYJLl++jHXr1qFLly5YsGABdHR0ZGpkT5gwAfn5+Th79qzwNdLrNC0tDcePH4e9vX2NfIb5GnD/XL1xAFmGV69eoUWLFmjYsGGZ7xcPGDIyMrBy5Uo8e/YMpqamsLS0hKurK9LS0uDj4yPX/VwWFhYQiURYv379Jz8TFRWFGTNmQENDA9u3b4eKigri4+Px8OFDWFhYlCrMW9MkJSXBzc0N/fr1Q4MGDbBs2TKYmppi2rRpQhBpZ2eHwMBALFy4UAgipXgt/v9JTEzEkSNHEBQUhMLCQowaNQpGRkbCzLj0BuDq6ors7Gx4e3tXcotZTRAVFYXNmzfj2bNnyMnJQUFBATp16gQ1NTVs3rwZysrKfB0zVkmuXr2KHTt2oEWLFnBxcRH2ujk4OCAyMhK//vor2rZtK3ONlrxea/IzTHXH/XP1xQFkCenp6RgzZgyaN2+Ow4cPlwoipcHjtWvXcPPmTWzevBmZmZlwd3fH8+fPkZqaim+++UauSyqICIWFhRg+fDhGjx6N+fPnf3YWbN68eYiOjsalS5dKfaYmXpglR7Ju3boFFxcX3Lx5E76+vliyZAnGjBmDqVOnCkHktGnT8OjRI3h5eWHYsGGV1fQqT7r0KC0tDU2aNCn1fmRkJJYvXw4jIyPY29tXfANZjZSVlYX09HQEBgZCUVERnTt3RufOnaGgoFAj+0DGKsv79+/x/v17hISEQFVVFfr6+rh37x62b9+Oli1bYtmyZdizZw+CgoKwe/fuGp/dvCbg/rl64iGbEurUqQNnZ2ds2bIFs2fPhre3Nxo1agQiEuom3rhxAy4uLsK+R+meyEWLFqFt27Y4dOiQXJZUSB/OFRQUUFhYiNq1a+Ply5cAyi55IP35Ojo6ePbsGdLT09GgQQOZNtWUCzM8PBwPHz7EtGnTZNbZKygowNjYGCYmJti3bx/WrFmDtLQ0bNy4EQCEIPLgwYNYt24dhgwZUpm/RpUnXXrUpEkTYU9ku3btQEQIDg6Gh4cHJBIJpk6dWsktZTVJ/fr1Ub9+fWGpv5REIqkxfSBjlU265SY+Ph7v3r0DUDTT6OzsjKysLOzfvx9TpkyBkpISzpw5gzZt2lRyi1lF4P65euIAsoTatWtj+PDhQhkOJycnIYhUUFDAhQsXsHTpUixatAhTpkyRSayzY8cONGjQQC6ZJJ8+fYpTp05hxowZ6NSpE+rWrYtRo0bhp59+wsOHD9GvX79SQaR06j8gIABdu3Ytc0aoJggPD4eVlRVyc3Nx9+5djB49Gv369ZO5Oeno6OCXX37Bu3fvYGdnBwDYuHEjFBUVYWtrCw0NDSxfvhwAL5f5nOLn38KFC/H06VP07NkTKSkpSEhIQIMGDXDixAlelsKqBN5Tw1jFeP78OaZMmQJTU1M4OjqiR48eePjwIdq2bQsAwpaaXbt2oUGDBnj//r1wj+Y8AzUT989VW43/60hX8IrFYuH/tWvXxuDBg7Fq1SpERETAyckJHz9+BADExsZi5syZQvAIFJ3kRIRGjRoJAWV5BRhEhMzMTNjZ2cHHxwd79uzB69evAQCDBw9G586d4erqimfPngmZMIuLiopCQkIC9PX1y6U91VFkZCRq164NVVVVREZG4uzZs5gyZQouX76M+Ph4AIC1tTWaN28u1Mi0s7PD8uXLceHCBdy/f1/m+3Hw+GUsLCzwzTff4NmzZwAAExMTHDt2TFjazcEjY4x9/ZKTk7Fp0yaMGjUKLi4uGDRoEBo0aIDhw4ejW7duwufMzMwwa9YsZGdnY9OmTQgICAAADh4Zq4Jq/B7I5ORkNG/evMzZkKysLNy/fx9r1qzBDz/8gL1796JevXqVkg1qx44dOHLkCHJycmBgYIC1a9eiTZs2uHDhArZt2wYigoeHB/r27YtatWohNzcXERER2LRpE8RiMY4dO1ajH9h9fHxw5MgRNGrUCFpaWpBIJDhw4AB69uyJ/v37w97eHnfu3MHp06cxY8YM6OnpAQBu3LgBIyMjDhr/S3l5eSgoKJCplckzj4wxVnOEhITA2dlZSFgHlD2rmJOTg9jYWLx+/Rqenp5QVVXFnDlz0KtXr8poNmPsM2r0U3FISAisrKygpaWFVq1aoW/fvmjRogW0tLRQu3Zt1K9fHyYmJlBQUMC6deswffp0/PzzzzJZOCuKvr4+Hj9+DD09PVy/fh0LFy7E9u3bYW5uDgUFBRw+fBiOjo7Q0NBAmzZtEB8fD4lEgnr16uHo0aNQUlKqkQ/u0n2jZmZmKCgowK+//gp/f3+sWbMGJiYmOHr0KHbv3o0///wTvXv3xqtXr+Dv7y8EkMOHDwfAy1b/W7Vr10bt2rVlXqtp5yBjjNVkAQEBSE5Oho6OjvBayeAxNzcXHh4euHr1KgICApCTk4N169bh+vXrHEAyVgXV6BnIJ0+eYMqUKQCAunXrIicnB8rKymjUqBFatmwJHR0ddOvWDW3atEFCQgLc3d3Rr18/eHh4oHHjxhXeXicnJ3z48AELFy7EggUL0K5dO2zduhWtW7dGXFwc/vrrL9y9exfZ2dno1KkTdHR0YGJiUuNKdcTFxSElJQVaWlql3jt79iz2798vFLL9/vvv8fLlS6xbtw4SiQR//fUXgKIZSzU1tYpuOmOMMfZVOXHiBDZt2oS7d++iadOmn1zFdfjwYWzatAl//PEH2rRpg/v376Nfv3486MhYFVSjA0igKIicO3cuunfvDgsLCzRs2BCPHz/Gy5cvERYWhpSUFCgpKQmZTwsLC9GzZ0/s2bNHZlleeZLOFEqXeEj///r1a8ybNw92dnZo2bIlFi5ciO+//x4bNmwQNqKXtSykJs08xsTEYOTIkVBVVUXHjh0xc+ZMtG/fHqqqqsJnzpw5gwMHDqBZs2ZYuHAh9PT0kJ+fj3/++Qe3bt1CWFgYTpw4UWOOGWOMMSYvQUFBmDBhAmbMmIE5c+YAkH1WkQaUL168wLhx43D06FGhHiRQs55hGKsuanwACQB3797F3LlzMWDAACxbtkzI/JWYmIisrCy8ePECYWFhiI2NhZ+fH1q1aoWLFy/KZR9kREQEbt++DSMjI3z//ffC60SErKwsrF69GnXq1IGHhwfu378PV1dXqKmpYe3atfjuu++Ez0oT6tS0zed+fn6wtbXFyJEjkZ2djUePHqFz586wtbWFrq4uOnbsCAC4cOEC9uzZg+bNm2PBggXo0aOH8D1KBu6MMcYY+++kpqZi2rRpSE1NhZubGwYPHgxAtiazWCzGwYMHceHCBRw7dgzNmjWrzCYzxv5FjQogs7OzUVhYiJcvX0JZWVlYoqiiooI7d+7A2dkZ+vr6WLBgwSeXL3748AHNmjUTsq2WZxAZFhYGMzMzAEDTpk3h4OCA3r17y7Tl2bNnsLW1hbe3NwYNGoQ///wTixcvRteuXbFixQohQKrJVq5cCT8/P5w/fx43btzA77//jtu3b6Njx44wMDDAxIkT8d133+HOnTvYtGkTVFVVMXPmTPTt21f4HjUx+GaMMcbkITg4GLa2tmjfvj1mzZqFYcOGCe+JxWLExMRg6dKl6NSpE9avX8/3X8aquBoTQAYHB2P//v14/vy5ULqhQ4cOGDFiBH788Ue0adNGCCL79OmDBQsWCDOA0pmokqNl5T07FR8fj5EjRyInJwddu3ZFdHQ0unTpAm1tbcyfPx8qKiqoXbs21q5di7S0NKxevRoNGjTA48ePMX36dNja2mLFihXl2qbqRBr03bt3DytXrsTChQthamqK/Px8vHr1CrNmzcLbt2/RtGlTaGlpwcXFBffv38fJkycxYMAArFq1qrJ/BcYYY+yr9OTJE8yePRt169bFiBEjYG1tDbFYjKdPn+LixYvIycnBmTNnoKysXCnZ7hljX65GBJABAQFwcHDAwIEDoa2tDT09PURHR+P8+fN4/PgxdHV14eHhgY4dO+LOnTtwcXFBr169hD2GFUEakL558waWlpbo3Lkz+vXrh8zMTFy5cgXKysowMTGBra0toqKisHr1anh5eUFNTQ0FBQUICQlBt27deMkligJJa2trKCoq4vjx4wCAW7duwdnZGXPmzEF0dDQeP36MzMxMfPvtt+jZsydWrlzJx44xxhiTo6ioKGzevBnPnj1DTk4OCgoK0KlTJ6ipqWHz5s1QVlbm7SOMVQNffQD5+vVrTJs2DYaGhpg1a1apdfWenp44ffo0fvjhB2zZsgWtW7fGvXv34OzsjN69e2Pu3LlQV1evkLZKO82YmBiMGzcOHTt2xIwZM6CrqwsvLy8EBAQgJSUFixcvxp49e9ChQwfs2rVLZpSuJmVbLYv0GD58+BBOTk7YuXOncMwcHBzg7OwMoGiv6c2bNxEQEID9+/fzTYsxxhirAFlZWUhPT0dgYCAUFRXRuXNndO7cmXMPMFaNfLUBpHQ54/Hjx+Hj44P169ejS5cuwntisVgItLZu3YqDBw/C1dVVKOtx//59ODg4YOrUqXB1da2wdhcPIi0tLdG8eXN4eHhAV1cXr169wokTJ3Djxg0oKCggKSkJx44dE2oWsv+TkJAAe3t75Ofn4+3bt5g+fTrs7e1Rr169Mj/PNy3GGGOs8vCyVcaqj682gJRydHREVlYWjh49Wuq94p2Vra0tsrOzcf78eeH9p0+fQlNTU24zep8KWqSziDExMbCyskKTJk3g5uYGAwMDAIC/vz+ePHmCFy9eYPfu3Rz4fML58+exbNkyTJgwAStXrpR5r3imWqB0UWPGGGOMMcZYaV/tUI80MMjKykLLli0BFAWMxSkqKkIsFgMAhgwZglevXiExMVF4X0dHB8rKyigsLCzXtiUkJAAAlJSUhJ9fnDQg/O6773DmzBmkpaVhw4YNuHfvHgBAT08Pjo6O2Lt37ye/BwN69OgBTU1NoRxLcdKAUUFBgYNHxhhjjDHGvtBXG0BKg8UOHTogMDAQMTExZS6NkL7Wpk0bFBQUoKCgoNRnynMGMj4+HlZWVkK21JIBoHRm7Pr16zh06BDatWuH06dPIzk5Gdu3b8etW7dKtYlnIMvWvn176Onp4a+//hIy75YcRGCMMcYYY4x9ua8ugJQGCNKgSldXF2/fvsXNmzeRnZ1d6vMKCgooKCjAP//8A11dXbRr106u7VNSUkKfPn1w7949rFu3TnitsLBQCB5v3LgBZ2dnSCQSFBQUoH379jh16hTCw8Px+++/y7V9XwvpDLSjoyMaNmyI7du3AwDvr2CMMcYYY+x/8NWk6wwLC4OPjw8iIiKgoaGBIUOGQFtbG+bm5njw4AH27NmD1q1bY+jQoahbt66w/5GIEBcXh6CgIPTq1UuubSQitGrVCs7Ozqhbty4uXboEAFi+fLkwo3jx4kUsXrwYixYtwpQpU4Rltu3bt8f9+/fRokULubbxayFdllqvXj106dIFGRkZQoDOGGOMMcYY++98FUl0/Pz8MHv2bHTp0gV169aFr68vunXrhp07d6JZs2YIDw/H+vXr8fz5c0yfPh1GRkYQiUTIyMhAYGAgfv75Z2RnZ+P06dNQVlaWS6Ah/Z7SwDUxMRE///wzrl+/DlNTUyxfvhwSiQTz58/HDz/8gFmzZsnMlhVP+FPTS3X8pxITE9GyZUsoKSlxEMkYY4wxxtj/oNoHkH5+frCzs8P48ePh4OCAli1bIjQ0FObm5ti7dy8GDhwIAIiJicFPP/2Eq1evokGDBujUqRMSEhLQvHlztGzZEnv27JFLLcDs7GyZ0hHFA5j4+Hjs27cP169fx5gxY7Bs2TLk5+dDQUEBKioq5dYGVoRLdTDGGGOMMfa/qdYB5NOnTzFx4kRMnjwZM2fORIMGDSAWi5Gfnw9ra2vY2Nigfv36aNmyJXr27AkAuHPnDl6/fo2YmBioqqpCT08P+vr6UFRULPeZvaCgIHh4eKBTp07o3r07unTpglatWsnss3z37h12796Na9euYfTo0XBzcwPAwQ5jjDHGGGOs6qm2AWR0dDRGjRoFLS0tHDx4EPXq1UNBQQFUVFTg7+8PGxsbtGjRAh8+fICCggKGDBkCb2/vT36/8i5gW1hYiD179mDXrl0AgMaNGyM9PR1NmzbFd999h/bt26Nv375QU1NDrVq1cPz4cVy/fh1mZmZYtGiRXNrEGGOMMcYYY/+LahtAfvjwAZMnT4ZYLMbEiRNhZWUFZWVlPH/+HJMnT8bQoUNhaWmJ5s2bY//+/Th//jzmzJmD2bNnA4BMEp3y3hOXm5uLOnXqID4+HhcvXhSW0o4dOxYBAQHw9/dHYmIiEhISIBaL8d133yE3NxdpaWnIz8/H+PHjsWrVqnJtE2OMMcYYY4z9r6ptJpYWLVrg0KFDmD17Ng4fPoyGDRuiU6dOmDx5MkaOHIklS5agfv36UFBQgKurK3x9ffHixQthmap0Zq+8g8cnT55g79692Lp1K7799luYmZkhJycH+/btg46ODpydnQEAWVlZCAsLw4cPH/D48WMkJSUhODgY79+/R2BgICd7YYwxxhhjjFU51SqATElJwbt371CvXj00atQIrVq1ws6dOzFnzhx4enoiNTUVY8aMwdq1a4UAjIjQpEkTtGvXDvKebPXz88OMGTMwduxYNGzYEADwzTffwNbWFmKxGBs3bkRaWhrmz5+P+vXrQ09PDwAwdOhQKCoqIikpCWlpaejSpYvQdg4iGWOMMcYYY1VFtQkgnz9/Dnd3d0RHRwMABg4ciDlz5qBjx47YtWsX5s+fj48fP6Jz585QUFCQCcBiY2ORlJQEY2NjuZW/kGaDtbW1xfz581G7dm3h56uqqsLW1hYKCgr4+eefoaysDCcnJwCQCWpbtmyJVq1aAeAkOowxxhhjjLGqp1pkaPHz88PkyZPRsmVL2NvbY8iQIbh27Rpu3ryJ/Px8tGrVCl5eXvj+++9x7NgxHDt2DEDR8tS4uDi4urqiVq1awv7H8p6JlAaPEyZMwPz581GnTh1IJBIoKCjg3bt3AIpmIidNmoTp06fD29tbSOijoKAgLKctnjCHg0fGGGOMMcZYVVPlZyClwZm1tTWcnJzQuHFjAEBISAh8fX3h4OAAIoKqqip27twJJycnHD58GAoKChg4cCAWL16MjIwM+Pj4yKXOo7+/P6ZMmQJbW1vMmzdPCB4VFRURHBwMR0dHrF27FoaGhlBVVcWkSZMAAN7e3lBQUBCCWsYYY4wxxhir6qr0DGTx4HHBggVo3LgxCgoKUFhYiG+++QZt27ZFQUEBiAiFhYVo1aoVvL290bRpUxw6dAhWVlZIT0+Hj48PVFRUUFhYWK7BY0xMDGxsbGBoaIh58+ahbt26EIvFQvA4YcIEGBoaom/fvsLXSINIe3t77Ny5E+fOnSu39jDGGGOMMcaYPFXZADI4OBh2dnYYP348li9fjlq1akEikUBFRQVhYWF4+vQptLW1hSWg0r2N0uWsSkpKUFVVxcWLF4Xgsbz3P+bl5UFdXR0hISEICQkRZjeDg4NhY2ODMWPGYOnSpahdu7bM16mqqsLOzg5ubm4YM2ZMubaJMcYYY4wxxuSlStaBlEgkcHNzw4ULFzBx4kS4uroKM4ehoaGws7NDeno6NDU1ER0djR9++AEikQiGhobo1KkT2rVrh8zMTNStWxdKSkpyCR6Bor2UUVFRWLp0KZKTk3Hw4EHk5OTAxsYGpqamWLp0KerWrSvzNUlJSfD398fgwYNRp04dAJBb+xhjjDHGGGOsPFXJABIA0tLSsHHjRvz9998YOnQoli1bhujoaIwdOxa9e/eGrq4uJBIJIiIi8Pz5c7x580b4WgcHB6HeoryymUozrEokErx8+RKLFy9GQkICsrKyYGZmhuXLl5eaeYyPj8esWbPQvHlzHDhwgEt0MMYYY4wxxqqVKhlASoO+9PR0uLu7IyAgALq6uvjjjz9gYWEBZ2dnNGjQQPh8eno6Pnz4gHv37uH9+/dYuHChXGb04uLi8OzZM/j5+aFOnToYMmQI9PX1AQCRkZHYtGkTHj16hEOHDqFPnz4yXxsfHw8XFxchoY+KigrXeWSMMcYYY4xVK1UugJQGj8WDSA8PD/zxxx9o27YtTpw4gQYNGgjvSzOellTey0IDAgKwdOlSqKioIDc3F4qKimjYsCG2bNmCTp06CctZFy9ejLS0NGzZsgV6enoAgLdv3wrBozz3ZDLGGGOMMcaYPFWJAPLVq1dISEiAnp6ezLLPgoICqKio4OPHj/Dw8MA///yDYcOGCXsiiweP8pzNe/78Oezs7DB27FhYWFhAXV0daWlpSE5ORufOnQEAb968Qdu2bfHy5UssWrQIaWlp8PT0RKtWrUrNPHLwyBhjjDHGGKuOKj2AjIuLw4gRI9CkSRO0bNkSjo6OEIlE6NChg8znpDORvr6+GDZsGBYtWiSXuo4lffjwAfPmzUPnzp2xaNEiNGjQAAoKCsjPz0etWrUAAEuWLBGWzqqrq+Ply5dwdXXFhw8fUKdOHdSqVQvnz5/n4JExxhhjjDFWrVV6GY+cnByoqKhAS0sLWlpaWLBgAezt7eHt7Y2wsDDhc40bN8by5cvRo0cP3Lp1C56enuVe17Esr1+/RnJyMoYNG4aGDRtCQUEBYrFYCB4dHR1x+fJlPH78GN7e3ggKCkLnzp2xefNmNG7cGMrKyhw8MsYYY4wxxr4KlT4DCQC7du3CiRMncPnyZYSEhODmzZs4f/48WrZsCW1tbdjZ2aF169Zo1aoV0tLSsGnTJvj4+GDZsmWYOHGiXNvm7e2NEydO4M6dO6Wyqk6ZMgXR0dE4f/48/Pz8MG/ePAwbNgxr1qxBkyZNEBsbi7Zt20JRUZGDR8YYY4wxxli1V6kzkNLYtVevXqhVqxYuXrwIAwMDrFq1Cn/++ScaNmyI69evw87ODk5OTrhy5QqUlZWxZs0aODs7Y8KECXJpl0QiEf4tnTksKCiQabOfnx/q1q2LXbt2oVmzZhg2bBgmT56MR48eIT09HQDQvn17KCoqQiKRcPDIGGOMMcYYq/YqNYCUJr3p0aMHNDQ0cOLECQCAkpIS/vnnHyGr6fjx45GTk4NFixahV69euHv3Luzt7YVsreUpMDAQv/76q/D/zp07IysrC9evX5dJ1KOtrY3NmzejW7duUFBQQGZmJgICAtC3b1989913Mt+zrCyxjDHGGGOMMVbdVOi02Pv37/H+/XuEh4ejSZMm6NatG1q2bAkAcHBwwNSpU+Hj4wMiwtKlS+Hg4ICJEydCWVkZEyZMgK+vL27duoUhQ4YI37M890Dm5uZi69atyM7Ohp2dHQCgZ8+eaNGiBS5dugR9fX0hOFRWVhZqURIRAgICkJeXh0GDBgmvcY1HxhhjjDHG2NekwvZABgYGYsOGDYiPj8e7d++grKyMSZMmYe7cuahTpw7S0tIwc+ZMxMXFIS0tDfb29pg2bRrq168PoHRAJq89hStXrsTDhw9x584d4bVr165h8eLFMDIywpw5c9CpUyeZdsTFxWHJkiWoV68eDhw4IPfEPowxxhhjjDFWGSokgJTWUTQ1NcWgQYPQo0cPPHz4EN999x3U1dWFzz18+BCzZs3C8OHDsXnz5jK/l7xm9qTf99KlS3Bzc8Ply5fRrl07YXnqqVOn4OXlha5duwq/R3Z2Nh4+fIibN28iNzcXp0+fhoqKitxLizDGGGOMMcZYZZB7AJmcnIy5c+eic+fOWLhwIRo1alTqMwUFBVBRUcG7d+/g6uoKsVgMLy8vNGvWTJ5NK1NERARMTU3h7e0NIyMj4fXMzEw8efIEGzduxIcPH6CkpIS8vDxoampCTU0NK1asgLKyMmdbZYwxxhhjjH215B7pJCUl4cOHD5g5c6YQPJacRVRRUUFmZiaSk5MxYsQIrFq1Ci9fvkSzZs0gkUgqJAmNtE2NGjVCmzZt8Pr1awAQZhMbNGiAoUOHok+fPggPD0dSUhLq1KkDLS0tNGvWTKgPycEjY4wxxhhj7Gsl98gsICAAycnJ0NHREV4ruQQ1NzcXGzduhLW1NQwNDaGmpoZNmzZBLBbLLXgMCAjA9OnTsWvXLty7d08IGFu3bg19fX08evSoVFslEgkaNGgAPT09mJiYYPDgwWjevDkUFBRARLxslTHGGGOMMfZVk/t0mZKSEgoLC5Gfn4/69euXOaNYp04ddOnSBQUFBcjKyoJIJEJcXJzcspgWFBTgyJEjCAkJwT///IP8/HzUqlULHTp0gLa2NuLj41GrVi0AEOo4KioqfjaY5YyrjDHGGGOMsa+d3ANITU1NSCQS/Pbbb5gzZw4UFRVllrBKgzM9PT2IxWIUFhbCzc0NdevWlQneysvTp0/x5MkTbN68GbVr10ZQUBA+fPiAZ8+e4cWLF3j69CkiIyMBAIcPH4adnZ1c2sEYY4wxxhhj1Y3cA8g2bdqgS5cuOH/+PDQ0NDB48GAoKCjIzOqJxWI8efIEHTt2ROPGjYX6iuUZtBERiAh79+5FVlYWZs2aBQDo1q0bAAj1GzMzMxEREYHDhw/jwIEDAMBBJGOMMcYYY4yhAvZANm3aFO7u7khLS4OXlxdu3rxZ9IP/fyAmFosRExOD27dvQ1tbGy1btvy/xpVjsKagoABFRUV8//33SEpKwsePH2Xel0gkAID69etDV1cXs2bNgra2Nvbt24cjR46Ue3sYY4wxxhhjrLqpkDqQAPDkyRPMnj0bdevWxYgRI2BtbQ2xWIynT5/i4sWLyMnJwZkzZ6CsrCzXmb5jx45h+/btuH37Nho3bvzZz4aFheHnn3/GjRs3sG3bNpiYmMilTYwxxhhjjDFWHVRYAAkAUVFR2Lx5M549e4acnBwUFBSgU6dOUFNTw+bNm6GsrCyUzShv0qA0MDAQ1tbWOHnyJDQ1Nf81+U1QUBB+//13zJ07l7OsMsYYY4wxxmq0Cg0gASArKwvp6ekIDAyEoqIiOnfujM6dOwt1FMszSIuOjkZsbCy6d++O2rVro379+sjMzETPnj3h6ekpM6NYsjZlWeQV3DLGGGOMMcZYdVDhVe/r16+P+vXr49tvv5V5XSKRlGtwlpiYCAsLC+Tk5KBevXpo2LAh1NTU8M0330BVVRVxcXGIiYnBd999B6DsMhwlg0oOHhljjDHGGGM1WYXPQFaU1NRUhIeHIzc3F0+fPkVSUhJCQkKQkZGBhIQEAEDjxo3Rtm1btG/fHurq6vjhhx/QvHlzNG3aFN9++y0nzWGMMcYYY4yxYr7aALIsHz9+hIKCAhYvXozHjx9j1qxZuH//Pt68eYOkpCThc+bm5tiwYUMltpQxxhhjjDHGqp6vKoB8//49EhMT8c8//0BZWRlqamr49ttv0a5dOwBAQUEBVFRUcOzYMezcuRM3b95Ew4YNoaioiMjISCQkJODly5eYOHEilJUrfHUvY4wxxhhjjFVpX02UFBgYiA0bNuDdu3dIS0uDgoICsrKyoKamBhsbG4wbNw4qKioAgNatWyMtLQ2xsbHQ1NQEAHz//ff4/vvvMWDAAABAYWEhB5GMMcYYY4wxVsxXscnv2bNnsLOzg0gkwpo1axAQEIDff/8dGzduRH5+Ptzc3LBv3z7h8y1btkTt2rURFxcHoCiBT0kcPDLGGGOMMcaYrGofQL579w7u7u4YPXo0FixYgP79+wMAmjRpAjMzM6xfvx4GBgbYsWMHLly4AADo1KkTGjZsiMDAQADgZDmMMcYYY4wx9gWqfeQUFxeHjx8/YsSIEWjUqJHwurT8hra2NubMmYN27drh3LlzSElJgYKCApo2bYr4+PjKajZjjDHGGGOMVTvVPoD08/NDZmYm9PT0ZF4vXr9RW1sbU6dOhZ+fH+Lj44ValBkZGRXdXMYYY4wxxhirtqp9AKmoqAgigjSZbMmkskQEiUSCHj16QEVFRSjXMWnSJBw4cKDMr2GMMcYYY4wxVlq1DSClQd8PP/yAzMxMnD9/HkDRzGPxgFBBQQGKioqoV68eCgoKhPdMTEygrKyMwsJCmdlKxhhjjDHGGGNlq7YBpDToU1dXR4sWLXD27Fk8f/5ceK94ECkWi/HgwQOoqakJZTukONsqY4wxxhhjjH2ZahtASqmqqmLt2rWIiIjA7t278eLFCwD/F2CKxWK8ffsWPj4+EIlEUFVVrczmMsYYY4wxxli1pUBfyQbAU6dOwd3dHZ07d8bIkSMxYsQI5Obmwt/fH5cvX0ZWVhbOnj0LZWVlSCQSLt3BGGOMMcYYY/+hryaABIAnT55gw4YNiIuLg0QiQV5eHrp27YouXbpg/fr1wp5HXrbKGGOMMcYYY/+5ryqABICPHz/i3bt3eP36NVRUVKCmpgZVVVUoKChALBZDSUmpspvIGGOMMcYYY9XSVxdAfgovW2WMMcYYY4yx/02NCSAZY4wxxhhjjP1veEqOMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX4QCSMcYYY4wxxtgX+X8DyjXFRSfpQwAAAABJRU5ErkJggg==\n"
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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -161,7 +163,7 @@
    ],
    "source": [
     "sim_exchanges = pd.DataFrame(fba[\"estimated_exchange_dmdt\"])\n",
-    "sim_exchanges = pd.DataFrame(sim_exchanges.loc[4, :].abs().sort_values(ascending=False))\n",
+    "sim_exchanges = pd.DataFrame(sim_exchanges.loc[24, :].abs().sort_values(ascending=False))\n",
     "sim_exchanges.head(10)\n",
     "\n",
     "# plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
@@ -179,14 +181,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:03.856890Z",
-     "start_time": "2023-06-09T22:18:03.651989Z"
+     "end_time": "2023-06-26T19:06:23.887908Z",
+     "start_time": "2023-06-26T19:06:23.506008Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 8,
    "outputs": [
     {
      "data": {
@@ -220,21 +222,21 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:04.821552Z",
-     "start_time": "2023-06-09T22:18:04.538210Z"
+     "end_time": "2023-06-26T19:06:24.137601Z",
+     "start_time": "2023-06-26T19:06:23.888894Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 9,
    "outputs": [
     {
      "data": {
       "text/plain": "                                                            4\nATPSYN-RXN (reverse)                                7963937.0\nmaintenance_reaction                                7600314.0\nNADH-DEHYDROG-A-RXN-NADH/UBIQUINONE-8/PROTON//N...  3656705.0\nTRANS-RXN0-545[CCO-PM-BAC-NEG]-CARBON-DIOXIDE//...  2277523.0\nTRANS-RXN0-474                                      2168940.0\nRXN-21817-CPD-9956/PROTON/OXYGEN-MOLECULE//UBIQ...  2168919.0\nTRANS-RXN-145                                       1898797.0\nGAPOXNPHOSPHN-RXN                                   1636060.0\nPHOSGLYPHOS-RXN (reverse)                           1636058.0\n3PGAREARR-RXN (reverse)                             1486502.0\n2PGADEHYDRAT-RXN                                    1486488.0\nPYRUVDEH-RXN                                        1150328.0\nPGLUCISOM-RXN-GLC-6-P//FRUCTOSE-6P.21.               914733.0\nRXN0-7166                                            884407.0\nTRANS-RXN-157-PTSH-PHOSPHORYLATED/GLC//GLC-6-P/...   872057.0\nTRIOSEPISOMERIZATION-RXN (reverse)                   834966.0\nCITSYN-RXN                                           744564.0\nPHOSICITDEHASE-RXN                                   744358.0\nDEPHOSICITDEHASE-RXN                                 744358.0\nRXN-14047                                            744291.0",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ATPSYN-RXN (reverse)</th>\n      <td>7963937.0</td>\n    </tr>\n    <tr>\n      <th>maintenance_reaction</th>\n      <td>7600314.0</td>\n    </tr>\n    <tr>\n      <th>NADH-DEHYDROG-A-RXN-NADH/UBIQUINONE-8/PROTON//NAD/CPD-9956/PROTON.46.</th>\n      <td>3656705.0</td>\n    </tr>\n    <tr>\n      <th>TRANS-RXN0-545[CCO-PM-BAC-NEG]-CARBON-DIOXIDE//CARBON-DIOXIDE.47. (reverse)</th>\n      <td>2277523.0</td>\n    </tr>\n    <tr>\n      <th>TRANS-RXN0-474</th>\n      <td>2168940.0</td>\n    </tr>\n    <tr>\n      <th>RXN-21817-CPD-9956/PROTON/OXYGEN-MOLECULE//UBIQUINONE-8/PROTON/WATER.59.</th>\n      <td>2168919.0</td>\n    </tr>\n    <tr>\n      <th>TRANS-RXN-145</th>\n      <td>1898797.0</td>\n    </tr>\n    <tr>\n      <th>GAPOXNPHOSPHN-RXN</th>\n      <td>1636060.0</td>\n    </tr>\n    <tr>\n      <th>PHOSGLYPHOS-RXN (reverse)</th>\n      <td>1636058.0</td>\n    </tr>\n    <tr>\n      <th>3PGAREARR-RXN (reverse)</th>\n      <td>1486502.0</td>\n    </tr>\n    <tr>\n      <th>2PGADEHYDRAT-RXN</th>\n      <td>1486488.0</td>\n    </tr>\n    <tr>\n      <th>PYRUVDEH-RXN</th>\n      <td>1150328.0</td>\n    </tr>\n    <tr>\n      <th>PGLUCISOM-RXN-GLC-6-P//FRUCTOSE-6P.21.</th>\n      <td>914733.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-7166</th>\n      <td>884407.0</td>\n    </tr>\n    <tr>\n      <th>TRANS-RXN-157-PTSH-PHOSPHORYLATED/GLC//GLC-6-P/PTSH-MONOMER.46.</th>\n      <td>872057.0</td>\n    </tr>\n    <tr>\n      <th>TRIOSEPISOMERIZATION-RXN (reverse)</th>\n      <td>834966.0</td>\n    </tr>\n    <tr>\n      <th>CITSYN-RXN</th>\n      <td>744564.0</td>\n    </tr>\n    <tr>\n      <th>PHOSICITDEHASE-RXN</th>\n      <td>744358.0</td>\n    </tr>\n    <tr>\n      <th>DEPHOSICITDEHASE-RXN</th>\n      <td>744358.0</td>\n    </tr>\n    <tr>\n      <th>RXN-14047</th>\n      <td>744291.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 64,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,45 +247,45 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:17.386647Z",
-     "start_time": "2023-06-09T22:18:17.382112Z"
+     "end_time": "2023-06-26T19:06:24.137784Z",
+     "start_time": "2023-06-26T19:06:24.115142Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 10,
    "outputs": [
     {
      "data": {
-      "text/plain": "7504"
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-1,\n  33,\n  173,\n  6,\n  -1,\n  4104,\n  36,\n  4068,\n  130,\n  2,\n  284,\n  185,\n  20,\n  -1,\n  -1,\n  -1,\n  103,\n  4,\n  4,\n  -1,\n  658,\n  0,\n  443,\n  -1,\n  -1,\n  -1,\n  12,\n  0,\n  293,\n  183,\n  67,\n  24,\n  1,\n  1,\n  2,\n  24,\n  ...]]"
      },
-     "execution_count": 65,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(fba[\"reaction_catalyst_counts\"][3])"
+    "fba[\"reaction_catalyst_counts\"]"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:17.866059Z",
-     "start_time": "2023-06-09T22:18:17.847111Z"
+     "end_time": "2023-06-26T19:06:24.261823Z",
+     "start_time": "2023-06-26T19:06:24.134582Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 11,
    "outputs": [
     {
      "data": {
       "text/plain": "                                                          4\nRXN0-1741-MALTOSE//MALTOSE.17.                      24833.0\nRXN0-0-CPD0-1123//N-ACETYLNEURAMINATE.31.           24807.0\nRXN-15315                                           24807.0\nRXN0-0-CPD0-1122//CPD0-1122.21.                     24807.0\nRXN0-0-CPD0-1122//N-ACETYLNEURAMINATE.31.           24807.0\n...                                                     ...\nBETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTO...      0.0\nBETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTO...      0.0\nBETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/A...      0.0\nBETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/G...      0.0\nRXN0-963                                                0.0\n\n[7504 rows x 1 columns]",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>RXN0-1741-MALTOSE//MALTOSE.17.</th>\n      <td>24833.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-0-CPD0-1123//N-ACETYLNEURAMINATE.31.</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>RXN-15315</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-0-CPD0-1122//CPD0-1122.21.</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-0-CPD0-1122//N-ACETYLNEURAMINATE.31.</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTOSE/GLC.35.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTOSE/Glucopyranose.45.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/ALPHA-GLUCOSE.41.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/GLC.31.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-963</th>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>7504 rows × 1 columns</p>\n</div>"
      },
-     "execution_count": 66,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,8 +298,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-06-09T22:18:18.159949Z",
-     "start_time": "2023-06-09T22:18:18.153240Z"
+     "end_time": "2023-06-26T19:06:24.333696Z",
+     "start_time": "2023-06-26T19:06:24.306108Z"
     }
    }
   },
@@ -312,22 +314,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 12,
    "outputs": [
     {
      "data": {
       "text/plain": "{'AMMONIUM[c]',\n 'CA+2[p]',\n 'CARBON-DIOXIDE[p]',\n 'CL-[p]',\n 'CO+2[p]',\n 'FE+2[p]',\n 'GLC[p]',\n 'K+[p]',\n 'L-SELENOCYSTEINE[c]',\n 'MG+2[p]',\n 'MN+2[p]',\n 'NA+[p]',\n 'NI+2[p]',\n 'OXYGEN-MOLECULE[p]',\n 'Pi[p]',\n 'SULFATE[p]',\n 'WATER[p]',\n 'ZN+2[p]'}"
      },
-     "execution_count": 83,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "uptake = (fba[\"unconstrained_molecules\"][3]).copy()\n",
-    "uptake_c = fba[\"constrained_molecules\"]\n",
-    "\n",
-    "uptake.extend(list(uptake_c.keys()))\n",
+    "uptake = metabolism.allowed_exchange_uptake\n",
     "uptake = set(uptake)\n",
     "\n",
     "# addition = set([\"CPD0-1074[c]\"])\n",
@@ -355,8 +354,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:11.464515Z",
-     "end_time": "2023-05-03T13:22:11.527729Z"
+     "end_time": "2023-06-26T19:06:24.337983Z",
+     "start_time": "2023-06-26T19:06:24.335409Z"
     }
    }
   },
@@ -371,52 +370,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 13,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[[],\n 7130668,\n 7682916,\n 7655656,\n 7600314,\n 7529897,\n 7560265,\n 7566014,\n 7555646,\n 7555453,\n 7576138,\n 7568193,\n 7560014,\n 7555559,\n 7598335,\n 7584044,\n 7582321,\n 7600258,\n 7619933,\n 7613406,\n 7618277,\n 7667139,\n 7642944,\n 7617385,\n 7652652,\n 7642911]"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fba[\"maintenance_target\"]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:06:24.341392Z",
+     "start_time": "2023-06-26T19:06:24.338980Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "outputs": [
     {
      "data": {
-      "text/plain": "2-3-DIHYDROXYBENZOATE[c]     73.0\n2-KETOGLUTARATE[c]          186.0\n2-PG[c]                      49.0\n2K-4CH3-PENTANOATE[c]        73.0\n4-AMINO-BUTYRATE[c]         160.0\n                            ...  \nMN+2[p]                      52.0\nNA+[p]                       52.0\nOXYGEN-MOLECULE[p]           51.0\nCA+2[p]                      52.0\nPi[p]                        52.0\nName: 4, Length: 171, dtype: float64"
+      "text/plain": "7642911"
      },
-     "execution_count": 84,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "homeostatic = pd.DataFrame(fba[\"target_homeostatic_dmdt\"]).loc[4, :]\n",
-    "exchanges = pd.DataFrame(fba[\"estimated_exchange_dmdt\"]).loc[4, :]\n",
-    "maintenance = pd.DataFrame(fba[\"maintenance_target\"]).at[4, 'maintenance_reaction']\n",
-    "kinetic = pd.DataFrame(fba[\"target_kinetic_fluxes\"]).loc[4, :]\n",
-    "homeostatic"
+    "homeostatic = pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :]\n",
+    "maintenance = pd.DataFrame(fba[\"maintenance_target\"][1:], columns=['maintenance_reaction']).iat[24, 0]\n",
+    "kinetic = pd.DataFrame(fba[\"target_kinetic_fluxes\"], columns=metabolism.kinetic_constraint_reactions).loc[24, :]\n",
+    "maintenance"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:11.468714Z",
-     "end_time": "2023-05-03T13:22:11.551006Z"
+     "end_time": "2023-06-26T19:06:24.352427Z",
+     "start_time": "2023-06-26T19:06:24.344104Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 15,
    "outputs": [],
    "source": [
-    "model = NetworkFlowModel(reactions=stoichiometric_matrix_dict,\n",
-    "                         homeostatic_metabolites=metabolism.homeostatic_objective,\n",
-    "                         free_reactions=FREE_RXNS)"
+    "model = NetworkFlowModel(\n",
+    "            stoich_arr=metabolism.stoichiometry,\n",
+    "            metabolites=metabolism.metabolite_names,\n",
+    "            reactions=metabolism.reaction_names,\n",
+    "            homeostatic_metabolites=list(dict(homeostatic).keys()),\n",
+    "            kinetic_reactions=list(dict(kinetic).keys()),\n",
+    "            free_reactions=FREE_RXNS)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:11.530620Z",
-     "end_time": "2023-05-03T13:22:15.402965Z"
+     "end_time": "2023-06-26T19:06:24.775269Z",
+     "start_time": "2023-06-26T19:06:24.354150Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 16,
    "outputs": [],
    "source": [
     "model.set_up_exchanges(exchanges=metabolism.exchange_molecules, uptakes=metabolism.allowed_exchange_uptake)"
@@ -424,53 +450,80 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:15.397820Z",
-     "end_time": "2023-05-03T13:22:15.882028Z"
+     "end_time": "2023-06-26T19:06:24.777068Z",
+     "start_time": "2023-06-26T19:06:24.775416Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 17,
    "outputs": [],
    "source": [
-    "solution: FlowResult = model.solve(homeostatic_targets=dict(homeostatic),\n",
-    "                                   maintenance_target=maintenance,\n",
-    "                                   kinetic_targets=dict(kinetic),\n",
-    "                                   reaction_catalyst_counts=sim_catalysts.to_dict()[4],\n",
-    "                                   objective_weights={'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001},\n",
-    "                                   upper_flux_bound=100000000)"
+    "objective_weights = {'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001}\n",
+    "solution: FlowResult = model.solve(\n",
+    "            homeostatic_targets=np.array(list(dict(homeostatic).values())),\n",
+    "            maintenance_target=maintenance,\n",
+    "            kinetic_targets=np.array(list(dict(kinetic).values())),\n",
+    "            binary_kinetic_idx=[],\n",
+    "            objective_weights=objective_weights,\n",
+    "            upper_flux_bound=100000000)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-06-26T19:06:25.033011Z",
+     "start_time": "2023-06-26T19:06:24.775553Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "5182    8.249992e+06\n639     2.490931e+06\n4382    2.221422e+06\n3617    1.074952e+06\n446     9.069960e+05\n            ...     \n1775    0.000000e+00\n1774    0.000000e+00\n1773    0.000000e+00\n1772    0.000000e+00\n5313    0.000000e+00\nLength: 5314, dtype: float64"
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(solution.exchanges).abs().sort_values(ascending=False)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:15.937791Z",
-     "end_time": "2023-05-03T13:22:16.319469Z"
+     "end_time": "2023-06-26T19:06:25.037247Z",
+     "start_time": "2023-06-26T19:06:25.033497Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 19,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 2160x360 with 1 Axes>",
-      "image/png": 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\n"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "cur_exchanges = pd.Series(solution.exchanges).abs().sort_values(ascending=False)\n",
+    "cur_exchanges = pd.Series(solution.exchanges, index=metabolism.metabolite_names).abs().sort_values(ascending=False)\n",
+    "nonzero = len(cur_exchanges[cur_exchanges > 0])\n",
     "\n",
     "# plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
     "plt.figure(figsize=(30, 5))\n",
-    "plt.plot(cur_exchanges.head(30), 'o')\n",
+    "plt.plot(cur_exchanges.head(nonzero), 'o')\n",
     "\n",
     "# shorten all xtick names to 10 characters\n",
-    "plt.xticks([i for i in range(30)], [i[:20] for i in cur_exchanges.index[:30]], rotation=45, ha= 'right')\n",
+    "plt.xticks([i for i in range(nonzero)], [i[:20] for i in cur_exchanges.index[:nonzero]], rotation=45, ha= 'right')\n",
     "# make xticks aligned to right left of bar\n",
     "plt.yscale('log')\n",
     "plt.ylabel('Flux counts')\n",
@@ -480,26 +533,26 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:16.323042Z",
-     "end_time": "2023-05-03T13:22:16.615742Z"
+     "end_time": "2023-06-26T19:06:25.303692Z",
+     "start_time": "2023-06-26T19:06:25.039178Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 20,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 1080x360 with 1 Axes>",
-      "image/png": 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\n"
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ERETpxmKSiIiIiIiI0o3FJBEREREREaUbi0kiIiIiIiJKNxaTRERERERElG4sJomIiIiIiCjdWEwSERERERFRurGYJCIiIiIionRjMUlERERERETpxmKSiIiIiIiI0o3FJBEREREREaUbi0kiIiIiIiJKNxaTRERERERElG4sJomIiIiIiCjdWEwSERERERFRurGYJCIiIiIionRjMUlERERERETpxmKSiIiIiIiI0o3FJBEREREREaUbi0kiIiIiIiJKNxaTRERERERElG4sJomIiIiIiCjdWEwSERERERFRurGYJCIiIiIionRjMUlERERERETpxmKSiIiIiIiI0o3FJBEREREREaWbdXYHIKJXdDHxOHn4GqIitbDPYQf32mXhkNM+u2MREREREaWJxSRRNhMRbF5zHHu2nIFarYZenwg7OxusWXoIPgHVEBjqAZVKld0xiYiIiIhMsJgkymab1xzH3q1nkZiQDCAZAKCPTwQA7N16FgAQ1L52dsUjIiIiIkpThtpMxsXFKf8/KioK69atw3fffYcXL15kNBfRf4IuJh57tpxBgj4pzekJ+iTs2XIGOm18FicjIiIiInq7d3oy+fLlSwwYMAAvX77E999/D61Wi4CAADx8+BAigiVLlmD9+vUoXrx4Zucl+lc5fewG1Go1jE8k06JWq3H66A14NamYdcGIiIiIiP7GOz2ZnDdvHk6dOgVPT08AwObNm/HgwQMMGTIEa9asgVqtxrx58zIzJ9G/0ovnOuWV1jdJ0CfixXNdFiUiIiIiIvpn3unJ5E8//YSQkBD069cPAHDw4EHkzZsXnTp1AgC0bdsWq1evzryURP9SuZ0cYGdv89aC0tbOBrmdHLIwFRERERHR33unJ5ORkZEoU6YMACAmJga//fYbPDw8lOl58uQxaU9JRGlzr10WBoPhrfMYDAa4e5bNokRERERERP/MOxWTBQsWREREBIBXTyWTk5NRr149Zfr58+dRuHDhTAlI9G/mkNMePgHVYGuX9ksCtnbW8AmoBgdHjjdJRERERJblnV5z9fLywjfffAOtVovdu3cjV65cqF+/Ph4/fozly5djx44d6NWrV2ZnJfpXCgx99VTfOM5kgj4RtnY2MBgMaNLSTZlORERERGRJ3qmYHDJkCOLi4rB582YULFgQ48aNg729PW7cuIF169ahWbNm6Nq1a2ZnJfpXUqlUCGpfGz4t3XD5XBiiIrWwz2EHd8+yfCJJRERERBbrnYpJW1tbTJo0CZMmTTL53NnZGb/88gsKFCiQKeGI/kscctqjkV8VAEB0NNscExEREZFle6c2k6Ghofj1119TfW5ra4sCBQrgp59+QtOmTTMcjoiIiIiIiCzTP3oyGRcXh6ioKOXfp0+fxmeffYaSJUummtdgMODIkSO4d+9e5qUkIiIiIiIii/KPi8kWLVogJiYGwKs2XlOmTMGUKVPSnF9ETIYKISIiIiIion+Xf1RMOjk5YebMmbh8+TJEBIsXL8Znn30GjUaTal61Wg0nJye+5kpERERERPQv9o874Klbty7q1q0LAHjw4AGCg4NRqVIlswUjIiIiIiIiy/VOvblOnTo1s3MQERERERHRe+SdikkAOHLkCHbu3Ilnz54hOTk51XSVSoVvvvkmQ+GIiIiIiIjIMr1TMblu3TpljMm8efPC1tY2U0MRERERERGRZXunYnLNmjVwdnbG8uXLkS9fvszORERERERERBZO/S4/9PDhQ7Ru3ZqFJBERERER0X/UOxWTJUqUwLNnzzI7CxEREREREb0n3qmY7NatG7799lvcvHkzs/MQERERERHRe+Cd2kyeO3cODg4O8PPzw0cffQQnJyeoVCqTedibKxERERER0b/XOxWTR48eBQAUKlQIcXFxuH//fqaGIiIiIiIiIsv2TsXkTz/9lNk5iIiIiIiI6D3yTm0miYiIiIiI6L/tnZ5MhoaG/qP51qxZ8y6/noiIiIiIiCzcOxWT9+7dS/WZwWBAVFQU9Ho9ihYtijJlymQ4HBEREREREVmmTG0zmZycjEOHDmH06NHo3LlzhoIRERERERGR5crUNpNWVlbw9vZGUFAQZs2alZm/moiIiIiIiCyIWTrgKVWqFP744w9z/GoiIiIiIiKyAJleTCYkJOCHH35A3rx5M/tXExERERERkYXI1N5cExISEBYWhpcvX6Jv374ZCkZERERERESWK9N6cwVetZksXbo0fH190aZNmwwFIyIiIiIiIsuVqb25EhERERER0X/DOxWTRsnJybhy5Qru378PW1tbFC5cGC4uLpmVjYiIiIiIiCzUOxeTP//8M8aPH4/Hjx9DRAAAKpUKBQoUwBdffIH69etnWkgiIiIiIiKyLO9UTJ49exZ9+/ZF3rx5MWDAAHz88ccQEdy5cwfr169Hv379sGbNGlSpUiWz8xIREREREZEFeKdicuHChShatCg2b96MnDlzmkxr06YNAgICsHTpUixfvjxTQhIREREREZFleadxJi9duoSgoKBUhSQAODo6IjAwEBcvXsxwOCIiIiIiIrJM71RM/h2VSoXExERz/GoiIiIiIiKyAO9UTFaqVAmbN29GbGxsqmlarRbff/89KlSokOFwRGQ5dDHx+HHHeWxYdQQ/770EXUx8dkciIiIiomz0Tm0m+/Tpg9DQUPj6+iIkJASlSpUCAKUDnsePH2P8+PGZmZOIsomIYPOa49iz5QzUajX0+kTY2dlgzdJD8AmohsBQD6hUquyOSURERERZ7J2KSTc3NyxcuBATJkzAjBkzlAtJEUH+/PkxZ84c1KhRI1ODElH22LzmOPZuPYvEhGQAyQAAffyr19j3bj0LAAhqXzu74hERERFRNnnncSYbNGiAevXq4erVq7h37x4AoGjRonBxcYG19Tv/WiKyILqYeOzZcub/C8nUEvRJ2LPlDHwC3ODgaJ/F6YiIiIgoO71zBzwPHjzA3LlzUbJkSfj4+MDHxwenTp3C7NmzERkZmZkZiSibnD52A2r12w8TarUap4/eyKJERERERGQp3qmYvHHjBvz9/bF69Wo8fPhQ+fzly5dYt24dWrRogYiIiEwLSUTZ48VznfJK65sk6BPx4rkuixIRERERkaV4p2Jy9uzZcHBwwO7du+Hs7Kx8PnjwYOzevRs2NjaYNWtWpoUkouyR28kBdvY2b53H1s4GuZ0csigREREREVmKdyomf/vtN3To0EHpxTWl4sWLIyQkBGfOnMloNiLKZu61y8JgMLx1HoPBAHfPslmUiIiIiIgsxTsVkwaDAfHxbx5jTkTeOp2I3g8OOe3hE1ANtnZpd6pla2cNn4Bq7HyHiIiI6D/onYpJV1dXbNy4ES9fvkw1TafT4fvvv0elSpUyHI6Isl9gqAeatHSDja0V7OxtoFIBdvY2sLG1QpOWbggM9cjuiERERESUDd5pDI8+ffogJCQEvr6+aNasGUqWLAmVSoXw8HDs3r0bT58+xdSpUzM7KxFlA5VKhaD2teHT0g2Xz4UhKlIL+xx2cPcsyyeSRERERP9h71RMVqpUCatXr8b06dOxcuVKk2nOzs6YOnUqKleunCkBicgyOOS0RyO/KgCA6Oi4bE6Tmi4mHicPX/ur2K1dFg45LafYtfR8REREROn1TsUkALi5ueH777/H8+fPcf/+fRgMBhQuXBgFChTIzHxERG8lIti85jj2bDkDtVoNvT4RdnY2WLP0EHwCqiEw1AMqlYr5iIiIiDLZOxeTRk5OTnBycsqMLERE6bZ5zXHs3XoWiQnJAJIBQBkbc+/WswCAoPa1syuexecjIiIielfv1AEPEZEl0MXEY8+WM0jQJ6U5PUGfhD1bzkCnzZ7epS09HxEREVFGsJgkovfW6WM3oFa//TCmVqtx+uiNLEpkytLzEREREWUEi0kiem+9eK5TXhl9kwR9Il4812VRIlOWno+IiIgoI1hMEtF7K7eTA+zsbd46j62dDXI7OWRRIlOWno+IiIgoI96pmDx27NhbpyckJGDmzJnvFIiI6J9yr10WBoPhrfMYDAa4e5bNokSmLD0fERERUUa8UzHZpUsXTJgwAfHxqTuNOH/+PPz8/LBq1aoMhyMiehuHnPbwCagGW7u0O6a2tbOGT0A1ODhmz3iOlp6PiIiIKCPeqZgMCgrC+vXr0aJFC1y8eBEAEB8fj8mTJ6Ndu3aIiorCpEmTMjUoEVFaAkM90KSlG2xsrWBnbwOVCrCzt4GNrRWatHRDYKgH8xERERGZwTuNMzlx4kQ0atQIY8aMQdu2bdGqVSscO3YMERERCAgIwODBg5E7d+5MjkpElJpKpUJQ+9rwaemGy+fCEBWphX0OO7h7lrWIJ36Wno+IiIjoXb1TMQkAtWvXxrZt25SnlCqVCkOHDkXHjh0zMx8R0T/ikNMejfyqAACio+OyOU1qlp6PiIiIKL3euTfX48ePo3Xr1oiIiIC3tzeKFi2KmTNnYvz48dBqtZmZkYiIiIiIiCzMOxWTAwYMQJcuXRAbG4vFixdjwYIF2LlzJ9q0aYONGzeicePG2L17d2ZnJSIiIiIiIgvxTsXk3r170aJFC+zevRsNGjQAAHzwwQcYPXo0vv32Wzg6OmLw4MGZGpSIiIiIiIgsxzu1mVy+fDk8PT3TnFa1alXs2LEDixYtylAwIiIiIiIislzv9GTyTYWkkZ2dHQYNGvROgYiIiIiIiMjyvdOTydDQ0L+dR6VS4ZtvvnmXX09EREREREQW7p2KyXv37qX6zGAwICoqCnq9HkWLFkWZMmUyHI6IiIiIiIgs0zsVkz/99FOanycnJ+PQoUMYPXo0OnfunKFgRESUdXQx8Th5+BqiIrWwz2EH99pl4ZDTPrtjKSw9HxER0X/ROxWTb2JlZQVvb29cvHgRs2bNwsaNGzPz1xMRUSYTEWxecxx7tpyBWq2GXp8IOzsbrFl6CD4B1RAY6gGVSsV8RERElMo7dcDzd0qVKoU//vjDHL+aiIgy0eY1x7F361kkJiRDH58ICKCPT0RiQjL2bj2LzWuOMx8RERGlKdOLyYSEBPzwww/ImzdvZv9qIiLKRLqYeOzZcgYJ+qQ0pyfok7BnyxnotPFZnOwVS89HRET0X5epvbkmJCQgLCwML1++RN++fTMUjIiIzOv0sRtQq9UAkt84j1qtxumjN+DVpGLWBft/lp6PiIjovy7TenMFXrWZLF26NHx9fdGmTZsMBSMiIvN68Vz36tXRt0jQJ+LFc10WJTJl6fmIiIj+6zK1N1ciInp/5HZygJ29zVsLNls7G+R2csjCVH+x9HxERET/dWbpgIeIiCyfe+2yMBgMb53HYDDA3bNsFiUyZen5iIiI/uv+0ZPJN7WRfBuVSoVvvvkm3T9HRERZwyGnPXwCqmHv1rNpdnJja2eNJi3d4OCYPeM5Wno+IiKi/7p/VEy+qY0kERG93wJDPQBAGccxQZ8IWzsbGAwGNGnppkxnvr+ni4nHycPXEBWphX0OO7jXLguHnJZT6Fp6PiIiev+oRESyO4SlMhgEkZHa7I5hIleuDwAA0dFx2ZwkbcyXMcyXMcz37nQx8bh8LuyvQsOzrEU98bPkfCKCzWuOKwWvXp8Iu/8veH0CqiEw1AMqlYr5/gFL3kcA5sso5ssY5ssY5ns3efM6Qq1+8zniHz2Z/OOPP1C0aFHkzJkz04IREZHlcMhpj0Z+VQBY3okMsOx8m9ccx96tZ5GYkAzjMCbGToP2bj0LAAhqXzu74ll8PiIien/9ow54/P39cfjwYZPPkpKScObMGcTExJgjFxERkcXTxcRjz5YzabbpBIAEfRL2bDkDnTY+i5O9Yun5iIjo/faPism03oSNiYlBaGgorly5kumhiIiI3genj92AWv32U6larcbpozeyKJEpS89HRETvtwwNDcLmlkRE9F/24rnureNgAkCCPhEvnuuyKJEpS89HRETvN44zSURE9I5yOznAzt7mrfPY2tkgt5NDFiUyZen5iIjo/cZikoiI6B251y4Lg8Hw1nkMBgPcPctmUSJTlp6PiIjebywmiYiI3pFDTnv4BFSDrV3anaPb2lnDJ6Batg1jYun5iIjo/faPhgYBgDt37uDMmTPKv429uF6/fh3W1mn/mmrVqmUwHhERkWULDPUAAGUcxwR9Imz/fxzHJi3dlOnMR0RE/zYq+Qe96Dg7O6c5oLGIvHWg42vXrmUsXTYzGASRkdrsjmHCUgc0NWK+jGG+jGG+jGG+jNHFxOPyuTBERWphn8MO7p5lLeqJn6XnAyx/HTNfxjBfxjBfxjDfu8mb1xFq9ZvrvX/0ZLJPnz6ZFoiIiOjfyCGnPRr5VQFgeRcDgOXnIyKi9w+LSSIiIiIiIko3dsBDRERERERE6cZikoiIiIiIiNKNxSQRERERERGlG4tJIiIiIiIiSjcWk0RERERERJRuLCaJiIiIiIgo3VhMEhERERERUbqxmCQiIiIiIqJ0YzFJRERERERE6cZikoiIiIiIiNKNxSQRERERERGlm3V2ByAiIiLSxcTj5OFriIrUwj6HHdxrl4VDTvvsjkVERG/BYpKIiIiyjYhg85rj2LPlDNRqNfT6RNjZ2WDN0kPwCaiGwFAPqFSq7I5JRERpYDFJRERE2WbzmuPYu/UsEhOSASQDAPTxiQCAvVvPAgCC2tfOrnhERPQWbDNJRERE2UIXE489W84gQZ+U5vQEfRL2bDkDnTY+i5MREdE/wWKSiIiIssXpYzegVr/9UkStVuP00RtZlIiIiNKDxSQRERFlixfPdcorrW+SoE/Ei+e6LEpERETpwWKSiIiIskVuJwfY2du8dR5bOxvkdnLIokRERJQeLCaJiIgoW7jXLguDwfDWeQwGA9w9y2ZRIiIiSg8Wk0RERJQtHHLawyegGmzt0u5c3tbOGj4B1eDgyPEmiYgsEYcGISIiomwTGOoBAMo4kwn6RNja2cBgMKBJSzdlOhERWR4Wk0RERJRtVCoVgtrXhk9LN1w+F4aoSC3sc9jB3bMsn0gSEVk4FpNERESU7Rxy2qORXxUAQHR0XDanISKif4JtJomIiIiIiCjdWEwSERERERFRurGYJCIiIiIionRjMUlERERERETpxmKSiIiIiIiI0o3FJBEREREREaUbi0kiIiIiIiJKNxaTRERERERElG4sJomIiIiIiCjdWEwSERERERFRurGYJCIiIiIionRjMUlERERERETpxmKSiIiIiIiI0o3FJBEREREREaWbdXYHICIiIrJ0uph4nDx8DVGRWtjnsIN77bJwyGmf3bGIiLLVv66Y/OWXXzB//nzo9Xo4Oztj6tSpsLW1ze5YRERE9B4SEWxecxx7tpyBWq2GXp8IOzsbrFl6CD4B1RAY6gGVSpXdMYmIssW/6jXXe/fuYcSIEZg3bx52794NEcH69euzOxYRERG9pzavOY69W88iMSEZ+vhEQAB9fCISE5Kxd+tZbF5zPLsjEhFlm39VMXngwAE0bNgQJUqUAACMHj0aTZo0yeZURERE9D7SxcRjz5YzSNAnpTk9QZ+EPVvOQKeNz+JkRESW4V9VTN69excqlQq9e/dG8+bNMW/ePOTMmTO7YxEREdF76PSxG1Cr336ppFarcfrojSxKRERkWf5VxWRSUhKOHj2KsWPHYuvWrYiLi8PChQuzOxYRERG9h1481716tfUtEvSJePFcl0WJiIgsy7+qA578+fOjevXqKFiwIACgadOmWLFiRTanIiIiovdRbicH2NnbvLWgtLWzQW4nhyxMlTZL723W0vMR0bv5VxWT9evXR79+/fDs2TPky5cPP//8M8qXL5/dsYiIiOg95F67LNYsPfTWeQwGA9w9y2ZRotQsvbdZS89HRBljka+5Xrt2DS4uLnj06FGqabt27ULTpk1RsWJFNGnSBNu3b1emVahQAQMGDECHDh3QuHFjvHjxAn369MnC5ERERPRv4ZDTHj4B1WBrl/a9d1s7a/gEVIODY/Y9YbP03mYtPR8RZYxKRCS7Q6R0+/ZtdOzYEY8fP8Yvv/yCQoUKKdP27NmDgQMHon379qhduzYOHjyIDRs2YP78+WjcuHGmZxERJCYmZ/rvzQhraysAQFKSZeUyYr6MYb6MYb6MYb6Ms/SMzJd+IoJ1yw9j27qTUKtV0OsTYWtnAzEI/NvWQNuu9bLtyZr2ZRzaN5uHhIS0e5sFAFtba3yzawAcs+GVUkvP9zpL3P5SYr6MYb53Y2Nj9dZjnMW85pqUlISNGzdi9uzZsLGxSXOeuXPnokmTJhgxYgQAwNPTE9HR0WYrJomIiOi/TaVSIaSbF1oE18Cvv1xHVKQWufLkgEf9T7O9ADr+8zWo1G8vZFVqFY7/9Dsa+VXJolR/sfR8KWlfxpmuX69ycPzwg2zNRPQ+sJhi8ty5c5g1axY6d+6MggULYvTo0SbTIyIiEB4ejoEDB5p83qhRI+zduxcREREoXrx4pmYSAaKj4zL1d2ZUrlyvDmyWlsuI+TKG+TKG+TKG+TLO0jMyX8Z81swVwKt8yQbJ9pwP77/4R73NPrz/IluyWno+4M1tOpfN2WdxbTotff9gvoyx1Hx58zribbuAxbSZ/Pjjj3Hw4EH06dMHVlZWqabfuXMHAPDRRx+ZfF6yZEkAQFhYmPlDEhEREVkIY2+zb5Odvc1aej6AbTqJMspiisl8+fIhb968b5weExMDAHB0dDT53MHh1QFIq9WaLxwRERGRhXGvXRYGg+Gt82Rnb7OWnk8XE489W84gQZ92m84EfRL2bDkDnTY+i5MRvT8sppj8O3/XT5Ba/d78KUREREQZZum9zVp6vtPHbvzt9aNarcbpozeyKBHR+8di2kz+nZw5cwIAdDqdyefGJ5LG6URERET/FYGhHgCgtPlL+P/eZg0GA5q0dFOmM19qL57r/lGbzhfPdW+dh+i/7L0pJo1tJcPDw6HRaJTP7969azKdiIiI6L9CpVIhqH1t+LR0w+VzYYiK1MI+hx3cPctm6/iX70M+Y5vOtxWU2d2mk8jSvTfFZMmSJVGsWDHs27cPn332mfL5/v37UapUKRQpUiQb0xERERFlH4ec9srwGpbWGyRgmfnca5fFmqWH3jpPdrbpJHofvDfFJAD07t0bI0aMQK5cuVCvXj0cOnQIe/fuxdy5c7M7GhERERG9R4xtOvduPZtmJzy2dtZo0tIt25+gElmy96qYbNmyJRISErBq1Sp8//33KF68OKZPnw4fH5/sjkZERERE7xlLbtNJ9D6wyGKyZcuWaNmyZZrTgoODERwcnMWJiIiIiOjfxpLbdBK9DyyymCQiIiIiyiqW2KaT6H3AwRmJiIiIiIgo3VhMEhERERERUbqxmCQiIiIiIqJ0YzFJRERERERE6cZikoiIiIiIiNKNxSQRERERERGlG4tJIiIiIiIiSjcWk0RERERERJRuLCaJiIiIiIgo3VhMEhERERERUbqxmCQiIiIiIqJ0s87uAERERERE9Ga6mHicPHwNUZFa2Oewg3vtsnDIaZ/dsRSWno/Mh8UkEREREZEFEhFsXnMce7acgVqthl6fCDs7G6xZegg+AdUQGOoBlUrFfH/D0otdS8/3NiwmiYiIiIgs0OY1x7F361kkJiQDSAYA6OMTAQB7t54FAAS1r51d8Sw+n6UXu5ae759gm0kiIiIiIguji4nHni1nkKBPSnN6gj4Je7acgU4bn8XJXrH0fIBpsauPTwTkVbGbmJCMvVvPYvOa49mW7X3I90+wmCQiIiIisjCnj92AWv32S3W1Wo3TR29kUSJTlp7P0otdS8/3T7GYJCIiIiKyMC+e65RXRt8kQZ+IF891WZTIlKXns/Ri19Lz/VMsJomIiIiILExuJwfY2du8dR5bOxvkdnLIokSmLD2fpRe7lp7vn2IxSURERERkYdxrl4XBYHjrPAaDAe6eZbMokSlLz2fpxa6l5/unWEwSEREREVkYh5z28AmoBlu7tAdfsLWzhk9ANTg4Zs8QEpaez9KLXUvP90+xmCQiIiIiskCBoR5o0tINNrZWsLO3gUoF2NnbwMbWCk1auiEw1IP53sDSi11Lz/dPcZxJIiIiIiILpFKpENS+NnxauuHyubC/BrX3LGsRRYal5zMWs8ZxHBP0ibC1s4HBYMj2Yvd9yPdPsJgkIiIiIrJgDjnt0civCgAgOjoum9OkZqn5LL3YtfR8/wSLSSIiIiIi+tey1GLXyNLzvQ3bTBIREREREVG6sZgkIiIiIiKidGMxSUREREREROnGYpKIiIiIiIjSjcUkERERERERpZtKRCS7Q1gqEYGlLR2V6tV/LS2XEfNlDPNlDPNlDPNlnKVnZL6MYb6MYb6MYb6MYb53o1K9GsLkjdNZTBIREREREVF68TVXIiIiIiIiSjcWk0RERERERJRuLCaJiIiIiIgo3VhMEhERERERUbqxmCQiIiIiIqJ0YzFJRERERERE6cZikoiIiIiIiNKNxSQRERERERGlG4tJIiIiIiIiSjcWk0RERERERJRuLCaJiIiIiIgo3VhMEhERERERUbqxmMxmIgIAMBgM2ZyEiP6tjMcZejc8PhO93yz1GGipuYjSg8VkNoqJiUGrVq1w//59qNWWvyp40Pt3s+T1a8kX88blZonLLzY2Fr/99htUKpVFL8Pk5OTsjvBGycnJUKvViI+PR0RERHbHMREbG4udO3fixYsX2R3ljQwGg0XuG2QelrSu4+PjcfXqVRgMBqhUquyOk0psbCxmzpyJq1evZneUN7Lk81tKlp7v387yK5h/Ka1Wi4CAAKhUKtjb22d3nDRdvXoVBw4cwMmTJwH8dUFvyRel9M89ePAAp06dwvbt2xEZGZndcVKJjY3F9u3bAQBqtdritrvk5GQkJSUhKioKACzuYkVEMG3aNAQHB+PEiRMWuQyNrKysEBcXh/3791vURYHBYICVlRViY2PRsmVLfPPNN4iNjc3uWIqVK1diyJAh+OGHH/Dy5cvsjpNKXFwcBg0ahF9//dVit733iSXtG3FxcVi2bBmGDBmCKVOm4MSJE0rRZgk5RQS9e/dGz549cf78eYvI9Lpff/0Vq1atwsqVK3H9+vXsjpOKTqdDnz598OjRI4s7vwGAXq/H7du3AcBitrvXJSQkIDw8PLtjmJ11dgf4L9JqtfDz80OxYsUwY8YM5M2b12S6iFjEjnvr1i0cOHAAd+/eRY4cOaDRaNCpUyeUKlUqu6NRBl24cAGjRo1CZGQkoqOjUahQIfzvf/+Dj48PbG1tszseAGDFihVYsmQJEhMTERQUpBRDlvAUX6fTYfLkyfjjjz8QHR0NHx8fdO3aFR9++KHF7L8qlQrNmzfH/fv30aVLF3z11VeoXbu2xSzD140ZMwa//fYbvL29szsKACjLKTExEcuWLUPevHnRvn17fPDBB9kdTdG3b188fvwYM2fOhMFgQMuWLfHhhx9mdyzFs2fPcPDgQdy9excjRoxA1apVLXLbs0Tx8fHYt28foqKiUKBAAXh7e8PGxia7YwF4dQ3z+eefIzExESKCJ0+eYM+ePejfvz+CgoIs5vg3ZMgQ9O7dG5MnT8aoUaNQtWpVi8hm1KBBA0ydOhUjRoxAcnIyevXqBY1Gk92xALxax+3atcO1a9dQp04dtG7d2mLObcCr43OnTp3w8uVLTJs2DS4uLkpBaSkZk5KS0KVLF1hbW2PEiBEoU6ZMdkcyGx7Vs5hOp4Ofnx9KlCiBGTNmoECBAgCAly9fQq/XIyYmxmJ2BD8/PyxatAibN2+Gh4cHbt26BT8/PyxZssRi7rSk9XqcJdydsoQMb3L27Fm0b98e7u7umDVrFubNm4ciRYpg6tSpuHPnDgDLyP/xxx8DeFVkrFq1CoBlPKE0vlVw69YtlC1bFiVLlsTy5csxceJEAJb1hNLNzQ39+vVD9erV0bVrVxw7dswilmFa2rVrh8jISKxfvz67owB4ta0lJCRgwoQJOHjwIJydnVG8ePHsjpXKpEmT4Ovri1mzZmHr1q0W9YSyePHi2L59O6KiojBp0iScO3fOIrc9S6PVahEUFISvvvoK8+bNw6JFi3DlypXsjgXgr5vhTk5O+PLLL7F161Zs3boV1tbWWL9+PbRabXZHVDg7O2PZsmUm258lnNuAv86x/v7+mDx5Mn788UcsWbLEIp5QarVaNGvWDI6OjihSpAgeP34MwLLObWq1Gn369MHTp08xbdo0Zf+wpCeU1tbWcHNzw40bN7B48WLcuHEjuyOZj1CWOnjwoGg0Gvn++++Vz3766Sf5/PPPpW7dulKvXj1ZtGiR3LlzJxtT/iUpKUlERJKTk+XFixcyffp0+fTTT2XQoEFy9erVbM2WmJgoIiJxcXGya9cuWbdunTx79kz5PDszibxaZq8zLk8REYPBkCWZUjpz5oy4uLjIlClTRKfTKZ///vvv4uHhIUOGDMnyTG9y+fJlqV69uvTt21fKlSsnK1euVKZlx7ITEYmJiZH69etL+/btJTw8XEREIiMjZeHChaLRaGTHjh3ZkistKbfF3377TTp06CDOzs5y7NgxEUl7+8wqr393UlKSREZGSq9evaRLly7y8uXLbFvHKSUkJMiAAQOkfPny4uPjI3q9XkSyd9kZjyEGg8FkGQ0fPlxcXFxk9erVEh0dnV3x0nTz5k2pV6+eNG/eXE6fPp2ty88ore0r5bLNLomJidK9e3cJCQmR69evy9OnT+XRo0ep5suOZWg8/oWGhqbKdPjwYdFoNPLLL79kea6UkpOTlWVjXI83b96UunXrip+fn5w5cyZb16/xuw0Gg8n1wObNm0Wj0Ui/fv3kjz/+yK54EhMTI15eXhISEiJPnz6VHj16SI8ePUQke497b3LmzBlxc3OTkJAQuXz5svJ5dp8/Ui6r5cuXS/Xq1aV///5y/fr1bExlPiwms8GYMWPEzc1NXr58KRcvXlSKs2nTpsmoUaNEo9HI8OHDJSoqKrujikjqA8i3334r1apVk2HDhklERES2ZDIeKGJiYqRp06ZSrVo10Wg0UqdOHdm9e7fEx8dneaa4uDjp1auXfP3118pnKZed8cTx559/yokTJ7I832+//SaffvqpzJkzR2JiYkymabVaadmypQwYMCDLc72JXq+Xpk2bypo1a2T48OGi0Whk+fLlyvSs3j+MJ9kOHTrIw4cPTabdvHlT3NzcZMGCBVmaKSW9Xi/3799/4/SzZ89KaGioODs7y9GjR0Ukey4OjPuuXq9PdfzYt2+faDQaZf/I6guCtJaHXq+XSZMmiaurqwwbNkzZd7J62cXFxcn9+/fl+fPnb5xn6NChSkH54sWLLEyXmnH5GG9qpLygz46CMj4+Xo4fPy4//fSTXLt2LdV04/H59u3bMmDAANFqtVmaz+jJkyfi4+MjGzduVD47deqUzJgxQ8aOHSurV6+W2NhYEcna/SM2NlY5/j179kz53Lgejx8/Lq6urnL8+PEsy5RSQkLCW6ffuHFDPD09pXnz5nLmzJks3/50Op388ssvcvXqVTEYDMqNqZQ2bdokGo1G+vbtm+Y2am4xMTHi4eEhHTp0kMePH4uIyLJly8TNzU2ePn2a5XnSktbNgrNnz0q1atUkJCRELl26pMyb3TcNUt4s+LcXlGwzmYUSExNhY2OD9u3b48aNG+jZsyfs7e3Rt29fdOjQQemIp0KFCvjiiy9Qp04dNGnSJEszJicnw8rKyuQzYxuXhIQE2NraIiQkBElJSZg2bRo+/vhjdO3aNUvbYRkzGgwGTJ06FQULFsSECRNgbW2NefPmYcKECRARNGjQIEs7N4qKisLvv/+Ou3fv4oMPPkCrVq2UVwpFBFZWVoiIiICPjw8aNmwId3f3VMvaXJ49e4bRo0fDzs4Obdq0gaOjIwwGAwwGA6ytrSEiiIyMROXKlbMkz+sSEhIQGRmJwoULA3i1jg0GAxwcHJA3b14MHjwYADBr1ix88MEHiIiIQJ48edChQwfY2dmZPZ9er0dISAgMBgMWLlwIR0dHiAiSk5NhbW2NfPnywcHBIdter9Hr9QgMDMSTJ09Qs2ZNODk5wc3NDaVLl0aBAgXg5OSEqlWrYvTo0ZgyZQq6du2K5cuXZ0sbSpVKhcTERLRu3RoJCQkICgpCYGAgcuTIgUaNGqFu3bpYsmQJnJ2dkSdPnizLlZSUBGtrayQmJiIiIgIJCQmwt7dHqVKlMGTIEIgI9u/fr7RxMu5DWbHsEhIS4OvriwcPHqBw4cJwc3ND/vz5Ubt2bTg5OaFkyZKws7PD9OnT8eGHH2LmzJlQqVRo1qwZnJyczJ4PeNWEY+PGjShTpgw++ugj5MyZE7ly5YK19avLjE8++QRfffUVunXrhgkTJmDs2LFZ1oZSq9WiY8eOiIyMRGRkJHLmzInNmzejQIECUKvVyroPDw/H559/jooVK2bZsfl1BoMB0dHRyiuFP/74IwYOHAiNRoPo6Gjs3r0bGzZswNdff41ChQpl2Tb4yy+/4MGDByhfvryyTRmXGwAcPHgQDg4OcHZ2NnuW18XFxaFFixZwdHRE/vz5UbduXRQuXBguLi5wcnKClZUVypQpg1WrVqFHjx6YOHEiRo0aBTc3N6jVarO3s0tKSkLnzp1x4cIFAMCnn36KXLlyoVatWihRogTKlCmDYsWKISgoCPny5UPPnj1hY2ODTp06wcXFxWy5UhIRfPXVVyhXrhwmTZqkNMEqX748kpKScPHiRTRo0CDNa8SsYNzW0trWq1atiiVLlqBHjx6YPn06hg0bhgoVKmR5G8qU11QqlQpWVlbKdX+XLl1gMBiUJju9evVC2bJlsyRXVlBJdl39/EfExcVh5cqVaN++PXLmzKl8vmnTJqxcuRJPnjzBokWL4OHhAeCvHaZz586wtbXFwoULYWVllSU7g/EgERsbi2XLluHevXtQq9WoXLkyWrRogRw5cigFJQBMnz4dGzZswA8//JDlbYn0ej02b96Mo0ePws/PTym6X758if79++P333/H2LFjs6ygNJ7Qw8PD8b///Q9xcXHo2LEjWrVqpczz559/IjAwEPXr18eYMWNMtgdzS0pKwpdffomdO3eiUKFCWLBgAXLlyqVM79KlCyIiIrBz507Y2tpm6QFYp9OhY8eOaNy4MTp16mQybcqUKUpnN+Hh4Vi1ahU2bNgAlUqFgwcPomjRoma/mDIYDIiKikKbNm2gUqnQpUsXBAYGAvirsyzjvrBr1y4ULVrUbFne5NmzZxg4cCBOnz4Na2trfPjhh4iJiUFycjLy58+PTz75BJ9++inq1q2LsLAw7Nq1C2fOnMGaNWvg5uaWLZ3ybNiwARcuXMDevXtRrlw5eHp6olu3bti7dy++/PJLTJ48GVWqVMmSixfjd2i1WvTs2RNPnjzBw4cP8cEHH+Dzzz9Hp06dkCNHDkyZMgWHDh2Cp6cnhg8frtxUyIp9pUePHjh8+DBsbW2Vfffp06ews7NDqVKlUKJECVSqVAkVK1bE/PnzcffuXfTs2RNNmjQxe0EpIujevTuOHDkCW1tbfPDBByhcuDA+/vhjfPzxx6hduzby5MmD4sWLIyIiAh07doSTkxMGDhwId3d3s257CQkJyk2n3r17Q0SQJ08efPLJJybzRUREoHHjxmjSpAnGjRsHR0dHs2V6G61Wi8DAQFSoUAEzZ85EcHAwatWqhdDQUOTKlQuHDh3C7NmzYW1tjS1btmRZh2lRUVHYu3cvpk+fjs8++wxTpkxRvnvRokVYtmwZvvrqK9SsWTPLC47r16+jVatW0Ov1AABHR0dotVp88MEHKFGiBMqXL4+KFSsq67xnz54oX748unfvjmrVqmXJsW/58uXYsGEDoqKiULx4ceTPnx8nT55EUlISHB0dkTt3bmg0GjRr1gy7du3CkSNH4Ofnh6CgIFSsWNHs+QAgPDwcTk5Oqbb9xo0bo1y5cpg7d26W5HhdXFwcAgICkC9fPuVmQZEiRVCmTBmT65hz586hd+/e+OSTTzB06FBluWXFMTouLg7du3dHzpw5UbFiRRQpUgTu7u5wcHAwWZ5ff/01Fi9eDA8PD/To0SNbbr6YA4tJM1u2bBnmzp2L4OBgDB482GSj6tevH548eYKlS5ciT548Jhd03bp1g8FgwIoVK7Ikp3Fn0+l0aNmyJWxsbFC0aFE8fvwYL1++hKOjI9avXw9HR0flRPH48WP0798fLi4uGDVqFFQqVZYVIEuXLsWGDRsQExODLVu24KOPPlIKXa1Wi759++LatWsYO3YsvLy8sqQHRuNyuXv3LgYMGGBSUCYnJ6Nbt25wcHDAlClTsvRCxbhdJSUl4euvv8b69etRrFgxLF++HHZ2dujWrRtu3ryJr776CmXKlMnSwkKr1aJ169a4ffs26tSpg2XLlgH4q6H/8uXLsWPHDuzatQvAqwvqX3/9FXq9HsOHD0eHDh3Mmi8pKQn9+vVDr169YGdnh7FjxyIqKgqdOnVSbhQsWrQIX375JRYuXAgvL68svZDS6XT46aef0KxZM9y5cwcLFizAL7/8gunTpyNnzpx48OABLl68iMuXL+PBgweIjo6GtbW1cgcVANavX48qVaqYNeeblomI4I8//sCCBQtw/fp12NjYoGvXrpgxYwY8PT0xe/Zss+ZKSa/XIzg4GDly5EC7du1ga2uLu3fvYvr06QgMDMT48eORnJyMadOm4fDhwyhfvjymT59u1mOLVqvF1q1bERoaChHB4MGDcf78eTRt2hSff/457t+/j99//x2nTp3Cn3/+iSdPnkCn0yFXrlzKuh49erTyloQ5GI8XZ86cwahRo/Do0SOUK1cOJUuWxOXLl/H48WPExsbC2toaGo0GFStWhL29PVavXg0vLy+0bdsWtWrVMlu+P//8E927d8ewYcPg5eUFlUqFK1eu4OLFi9Dr9ahYsSLc3Nywbt06/PHHHxgyZEiW9oiblJSEFy9eQK1Ww87ODg4ODti5cydGjBiBPn364OTJkxg4cKByYazX67Fv3z5MnjwZkydPxmeffZZlWWNiYvDDDz9g+vTp8Pb2xqxZs7BkyRIsWbIEM2bMgI+PT5ZlMTJeu1y8eBEDBgxAnjx54O3tDQ8PD/zyyy+4dOkSbty4gYcPH0KtVqNo0aJ48eIFYmJiUKFCBQwfPhxVq1Y1SzadTodp06ZhzJgxsLW1xTfffINNmzahWLFiGDVqFHLlyoWbN2/i9OnTuHXrFm7evIm7d+8iZ86cynBdgYGBGDt2rNluGuj1ely7dg2urq4ATAsv43F76dKl2LhxI2bPnm22ZfU2586dQ9u2bZV/Ozg4QKfTIXfu3Pj4449Rvnx5uLq64qOPPsLz58/Rs2dP1KlTB126dEGlSpWyJOOePXswdOhQJCUlAQDy58+PqKgo5M+fH1WqVEGpUqVQt25dlCtXDl9++SW2bdsGV1dX9OjR49/xhDLr3qj9b7pw4YK4urqKl5eXDB8+XF6+fGky/d69eyIiJu/PP3v2TDp06CATJ06UpKSkLHvv22AwyMiRI6VVq1ZK5yIiIgMHDhSNRiOHDx9O1YHMnDlzJCgoKMvyGYWHh8uECRNEo9GYdBpjbJ8TExMjnTp1Mml/lRX5UraL9Pf3l8aNG8umTZtEROTatWup2ipmlZTtl5YvXy5eXl7SqVMn6dixo9SvX1+uXLmS5ZmMbRC7d+8uw4cPl+bNm6ea59KlS+Lv7y8iIgMGDJCaNWvK7t27ZcyYMaLRaGTNmjVmzRgVFSXDhw+XhQsXiojI+fPnJTg4WBo1aiT79u2TZcuWiYuLi+zatcusOd6kS5cu0qlTJxF5tf39+eef0q5dO/Hw8JAjR44o8+n1enn27JkcOXJEtmzZIn379pWQkBBxcXGRW7dumTWjcZ/QarUyZcoU6dmzp4SEhMiaNWvk9u3bIvKqPdGtW7dk2LBhEhgYKDVq1BCNRiNnzpwxazaRv44rP/74o3h7e8vFixeVz3bs2CEajUZ27twpv//+u4i8WpYDBw6U3r17m73d1fjx46VRo0ZKezCDwSC9evWSWrVqyYIFC5R2fUlJSZKcnCy3bt2SAwcOyIoVK6R79+7SokULZRmbQ2xsrLRr1075jvPnz4uXl5d07txZfvrpJ0lKSpJHjx7JDz/8IF999ZV06tRJ6tSpI1WrVhWNRiMajUa6dOkicXFxZst47tw50Wg0SluqvXv3SsWKFcXT01Nq1qwpGo1G5syZI3/88YfJ+S0rxMTESPfu3cXX11c8PDxk6tSpEhsbKw8fPpThw4dLzZo1pXLlykobK+N2EBsbK66urrJixYoszSsi8vLlS1m7dq24uLhIo0aNpEKFCrJ3794sz5GWkydPipeXlwQGBpq03Xz58qXcuHFD9u/fL+PGjZP//e9/UrNmTalRo4ZZ+33YtGmTVKpUSWl/KCKyYsUK5fz7ets5rVYrt2/flhMnTsiCBQtk6NChcvPmTbPlExHp2LGjNG/e3GR5vX7Nee3aNXF1dZW5c+emOd1cdDqdhIWFicirDp5q1aolrVu3lmXLlsmpU6dk2rRp8vnnn0v16tVFo9GIi4uLNG/eXKpUqSIajUbat29v9mub+Ph4efTokSQnJ8uqVaukWbNmUr9+fdm9e7ds3LhRhg0bJo0aNZJKlSqJRqMRV1dX6dixo2g0GqlVq5Z07tzZrMforMJi0sxu3bol5cuXl6CgIPH29pYRI0akKiju378v8+bNk9WrV8vGjRtl0KBBUq1aNbNf5KXl888/lzFjxign1f3794uzs7Ns3LhRDh48KCtXrjQ54cbExIi3t7ecOnXKbJleP8EbC8bIyEgZP368VK1aVcaPH59qenR0tEyaNMksFwharVYWLlwoGzdulDt37ig3BVK6deuW+Pv7S4MGDUx6782uRuGvF5QNGzaUTz/9VFl3WdkhQUxMjDRo0EBCQkLk+fPnsmfPHnF3d0914rx3757UrFlTGjVqJNWrV5fz58+LyKtOMiZOnGj2E62IyA8//CD169dXDvjnzp2T4OBg5UL00KFDIpI963XUqFESEBBgcjMqPDxcQkNDxd3dXY4cOZJm78bGdZ1VnRhptVrx9vaWFi1aSN++faVTp05Sr149adKkiVy8eNFk3rNnz8qqVaukQ4cOZr24f/13r127VqpUqaJcXO7atUs0Go189dVXcvv2bQkMDFQK9MTERGUZmnO/WbRokXh4eJicMwwGg/Tt21dq1qwpc+bMeWPPrUlJSSY9NpvDgQMHxMXFxeTm46lTp8TLy0v8/f3l4MGDJvPr9XoxGAxy9epV+fHHH2XGjBlmP8/dv39fGjRoIKtXr5awsDDx9vaWhQsXSnh4uISHhys3hObNm2fWHK+Li4sTX19f+fzzz+Wbb76RiRMnyuHDh5Xpv/32m3Tv3l00Go3MmDHD5Gfv3r0rPj4+snPnTrPle9vxLCYmRtatWyd16tSRoKCgbOkoS6fTyaVLl+Ty5cvy8uVL5Rh44sQJ8fLykpYtW8q+ffve+Dvu378vkZGRZs1p7OH2xo0bJp+vXr1a6tevn6qgfP1YkhU900+cOFE0Go1069YtzYLS+N8lS5ZI+fLllesFc6/zpKQkCQ4OluHDhyufHThwQDw9PSUkJMTkvBEZGSkXLlyQbdu2ycCBA6Vr167i6uoq7u7ub+2ULqMSExPF19dX5syZIyKv1t+KFSukUaNG0qFDB+W4/ezZM4mMjJQDBw7I4sWLpXfv3lKvXj359NNPpUqVKmn21vy+YTGZBbp37y4//vijTJ8+XWrXri3Dhw9XDnzPnz+XJ0+eSFBQkJQvX17q1q0r7dq1y/KuoZOTkyUuLk7q1asn48aNE5G/LqaWLFkiIiJffPGF+Pv7K70EGu+Gd+rUSQ4cOGCWXMYLPp1OJ7Nnz5bevXtLSEiIrF+/XiIjIyUmJkbGjx8v7u7uJgXl6z27ZeZB2WAwyLhx45Q76+7u7lKrVi3p1KmTTJgwQXbt2qX0xBYZGSktW7YUf39/+e6775TfkV1dbBuXZ0JCgqxcuVLq1q0r7du3N1mn5qbT6cTNzc2kx7hbt24pT4CMjD3ede/eXfz9/eXcuXMmv+fveu/LqJTLYujQoRIcHKxcnP/222/SunVrqVOnjsmNgqxer/PmzRNPT89URUN4eLi0b99eqlevLkeOHDE58RsMhlS94ZmTwWCQ8ePHi7+/v0RERCjfOWTIEKUYf9sTe3Nuk7Gxsco2t2nTJqlQoYI8fPhQ6VV22bJlIvLqznzFihVN1rWI+da3cRnt2bNHqlSpIg8ePBCRv45jxoLSw8ND5s6dqyy/rB7aIiwszOTJlHF5nDlzRnlCtH//fmX+lE9YU85vbn379pVGjRrJiRMnpGvXribFr4jI3LlzxcXFJUt7WdyyZYv4+vqaPJWIioqSK1euyL179yQpKUnu3r0r3bp1E41GIyNGjJDDhw/LL7/8IsOHD5datWqleRMzo1Kuk7cNn2J8Qlm+fHkZNGhQlq1LnU4nY8aMkZYtW4pGoxFnZ2epX7++DB48WOlxNOUTypTXJlk9bNitW7ekQoUKynBMKb9/9erV0qBBA+nUqZNSbGbHDcm1a9dKuXLlxMXFRQICAuTXX39VpqVcpzdv3pSQkBAJDQ3Nkv3EYDBIYGCgDBs2zOTzAwcOiIeHh4SEhJi8ffO627dvm/Q6bA5arVZ8fX1l9uzZymfJycmyevVq8fLykjZt2siTJ0/S/Fm9Xi937tz5VxSSIiJZ2+PCf5Rer8fly5cxdOhQeHl54eTJk5gyZQp69uyJr7/+Gvnz58f69euxdetWrFu3DosXL4ZGozFrpuTkZJN/q9Vq2Nvbw8vLC7/88gtmzJiBQYMGYeDAgejSpQuAV+3Y7OzslA5trKysoFar0aJFC3z00UeZnlH+vwdUnU6HoKAgHDt2DDExMbCxscH48eMxdOhQPHz4EAMHDkTTpk2xd+9eTJo0CQBgY2Nj8ruMPc5lVFJSElQqFZo3b46GDRsid+7cKF68ODp16oQXL15g9+7dGDZsGFq0aAEPDw/MnDkThQsXxu3bt7FlyxasXbsWAMzSPkhea/78+r9Tfm9MTAw6deqE9u3bIzw8HP3798eLFy+UXnLN6ebNm2jUqBGmTZum9BhXvHhxuLq64scff0RCQgIMBgNUKhVsbW3RpUsXzJw5U2nXZ/y7Xl/HmSHlMkvZxq9r167IkycPDh48CBFBpUqVMHLkSBQpUgQrVqzA5s2bAUDpvdfcEhISAABly5ZFfHy8Mqi0MX/x4sUxceJEODs7Y8iQITh27JgyTaVSKduBudo4p1wGKpUKd+7cUXosVKlU2LdvH3744QeMHTsWIoL169crf9PrzNn+dNKkSZg2bRoAwM/PD4ULF0arVq3Qv39/jBgxAt26dQPwqmOvfPnyoUiRIiY/b652fsb14uzsDJ1Oh9u3bwN4dRxLTk6GSqXC/PnzUaVKFWzevBkrVqyAVquFlZVVlnae9cEHH8De3l5p32XsPdHNzQ0zZsxAZGQkli1bhoMHDwJ4tc8a923AfMvPyLgdjhs3DiKCLl264M6dO8idOzeAv86D9evXh42NDe7du2fWPCk9e/YM0dHRSudIP/74I/z8/BASEoLmzZsjJCQEiYmJmD17Nvr06YN9+/ahT58+GDVqFK5evYqVK1dmemdfcXFx+N///ocDBw4ASD0AvLENXUREBG7cuIG2bdti+PDh2L9/P0aMGGH2Y5+xc6KwsDB4eXlh9erVWLJkCcqVK4cDBw4gICAA169fR/Xq1TF58uRU25+x5/KsUrJkSdja2uLq1avK9xuXUYcOHRASEoK7d+9i2rRpuHXrVqrlbU7G7ylbtizq1auHb7/9Fnfu3MGsWbNw4sQJAKb75yeffAJ/f39cunQJa9euRWJiotmyGY8RFSpUwO3btyEiyvmhYcOGGDduHMLCwrB8+XL8+uuvys8lJSUpf1fp0qWRN29es2UEXrXdLFKkCC5dugTg1YgNarUaoaGhCA0NxaNHjzBgwADl+GhcZklJSbC1tcVHH32EggULmjVjlsnq6vXfLuWdJ+MdvCVLlkjfvn2Vz6dOnSqVK1eWcuXKZcsAv8aMsbGx8t1338mUKVOUdoUnTpyQunXrikajkdGjR4vIqztEERER0rx5c/niiy+yNGtycrKMGDFCgoOD5c8//1Syb9myRapVqybdu3eXFy9eyNOnT5U2lKtWrTJLlri4OAkODlbewT9//rz07NlT6tatq7xOExcXJ8ePH5eNGzfK2LFjxdfXV7y9vZWnmL6+vqnazWaF5ORk5a7nrl27xNfXV8LDwyUxMVFWrFghDRo0kObNm2dJm069Xp/mU8U5c+ZIlSpVlHH0srr9klarlTFjxkjnzp2ldevWcuLECeXObEJCgowfP1569epl8jMXL16U4OBgadq0qXz77bdmzZeQkJBq7MAbN25IuXLllFdtRUzvJt+9e1fat28vHh4ecujQoSy5821cb3FxcXL16lVJSkqSgIAA6dmzp4i8arOm0Whk6dKlIiIye/Zs8fLyMvsrZ2m5du2aVK1aVWl7tmPHDqX9mvE1uEuXLklQUJCEhISY9elLfHy8XLt2TXkVVOTVNpmyTa5x2RrfbDE+oaxbt65MnTo1W9pkN2vWTDlXvP7E2/iEMiAgINUrr1nJYDDIkSNHpEWLFuLi4iLbtm0zeZp/6dIlqVu3rslTGXNbtWqVeHh4SFJSkly/fl0qVqwokydPlsOHD8vy5cvFz89P3N3dlbeUHjx4ICdOnJCrV6+abV95+PChaDQaadasmckrtyn7A7h79664urrKmDFjROTVE8r169eLRqNRPjMHvV4v7du3l9DQUAkPDzc5PyQkJMjGjRvFy8tLPD09lTdejE8oW7duLbt37zZbNpFXT0z37Nkjf/75p0RGRir5GjduLNOmTRORv/aPlM0SVq1aJY0aNZKgoKBsadr0/PlzcXd3lzt37si1a9ekUqVKEhQUJJcvX5bhw4fL1atXTeZftmyZ3Llzx2x5Uh5jV6xYIVWrVlX21ZTnr5RPKFP2i5FVb9sYr2FGjhxp0t9DyuYPxteZ27ZtqzwlzerrmqzCYjITxMfHy5QpU5SN+PWN5ciRI1K7dm3lBDBgwACpWLGieHh4yJgxY7L0AsC4ocfExCiFTq1atWTMmDFK+6ndu3eLp6en+Pj4yNy5c2X69Oni7+8vfn5+Jq9ZZbbTp0/L9u3bTT6LjY2VoKAgk/Ysxu/+/vvvRaPRKB3c3Lt3L1Wbzsx07do1+fTTT00OXBcuXJCuXbtK9erVTV5jNdLr9RIZGSlHjx6VTZs2ma2h9Z9//infffeddO/eXTp16iR9+vSRAwcOyMOHD0Xkr21y9+7dyut7xoNhYmKizJ8/X/r27Wu2i+XY2FhZuHChUkin9SpVRESE1K5dWyZNmpTlr/vodDpp3LixtGrVSgYOHCjt27dXXs8zZomKipKaNWsqRZDRxYsXxdfXVwICAsx2o8DYvqp27doSHBwsI0aMkE2bNsmmTZukfv36sm/fvjSXmcFgkPDwcPH395eGDRuapQ1dyu81rletVisNGjSQadOmiV6vl2nTpomXl5fSPuerr75SLqimT58ugYGBZm/f9/q2nZSUJNHR0dK/f39p166dxMbGilarlW3btom3t7dUq1ZN6tatK40bN5bWrVsr+4s5ji96vV4aNWokzs7O0qRJEwkNDZUpU6bI1q1bpW7dujJ9+vQ3nicMBoN06tRJvL29zVqQa7VaWbFihfz6668mzTD69Okj7du3N5nX2ARC5FVB6e3tLd7e3iYFSmb7J8eMAwcOSLNmzaRu3bqyZs0auX//vly7dk1GjhwpDRo0MOkoxVyM28/Tp0+levXqMnHiRDl48KAMGzZMYmNjlfmOHz8ujRs3lr59+5p8bi7G9RUeHi41a9aUpk2bplpff/75p7i5ucnAgQNNjnXR0dGyadMmsxZD586dk8aNG8vPP/9ssi8b/39SUpJs27ZNqlatKj179lQ6dTpz5oxUrlxZ2rdvr3RYldkSExOV125dXV2ldu3a0qZNG5kxY4Z4e3tLhw4d5MaNG29smrFs2TLx9fU1a/u+lIz7il6vF71eL/7+/vLDDz+IiMj169fF1dVVatWqJRqNRmmbaO5mJYmJian24YMHD4qLi4uyX6Y8roj8VVB27NgxSx7MpHV9tH79+lQdORn38ZQFZWho6Btfef03yJx3//7j/vjjD3zzzTfQ6XSYOHEirKyslNdBDAYD8ufPD1tbW9jZ2WHkyJE4efIkVqxYgf3792Pz5s2wtbXFyJEjs2Q4BrVajYSEBPTq1QsFChTAyJEj8dFHH0GtViMqKgoPHjzAZ599hlKlSmHDhg3YunUrSpUqhQoVKmDMmDGwtrY2Gag4s8TGxmLt2rVwcnKCn5+f8vmLFy/w+PFjk9dn5P9fYwgMDMSOHTvwww8/wN/fH0WLFlXGKTRHxrx588LKysrkVShXV1f07t0bS5YswcKFC6FWq5UhIxITE2FrawsnJyfUrl07U7OkdP78eQwdOhS5cuXChx9+CFtbW9y4cQP9+vWDu7u70q28cfDrQYMGoXPnzsormdbW1ujduzfUajVUKpVZhrZYs2YNFi1ahGfPnilD5BiHFDC+8lagQAG4u7vj5MmTuHfvHooXL55lr+wtW7YMH3zwAebPn498+fIp245Wq1WGccmdOzeGDx+OH374AdeuXUO5cuUgIqhYsSKmTZuGXLlymW3s0Nu3b6NBgwZ4/PgxwsPDcfLkSWzfvh2Ojo54+fIlJk6ciJ07d+Ljjz9G8eLFUaFCBeTIkQPFixdHkSJFsGTJEiQnJyNHjhyZns24nRu3m8TERPz4448oXLgwmjRpAltbWwQGBmLnzp1Yu3YtAgMD0bVrVwCvxvY7efIknJ2dzZLNSESUY9/Dhw9RsmRJWFlZ4cMPP4S/vz+6d++O48ePo2HDhvD19UX9+vWxe/du5fjdoEEDWFlZmeW4Arx6bbl9+/YwGAw4deoUHjx4gO3bt0On08FgMOC7777Djz/+CGdnZ5QpUwZly5aFi4sL7O3tUbBgQaxcuRKPHz8223iSIoIRI0Zg//79AF6dR/Lnz4+SJUsiOjoadnZ22L9/P2rUqAG1Wm0y9JGbmxsmTJiAKVOmoHTp0mbJB7z5lW3jdhkZGYm4uDhMnjwZM2bMwLRp0zB58mRlrNolS5Yor91ntoSEBJw9exa1atVSrg9y5cqFtm3bYteuXfjpp5/g4uKCDz74QNnGatWqBS8vL+zatStVsxRzUKvVSE5ORvHixbF27Vq0a9cOs2bNQlJSEho0aICEhAQMHjwYHh4eGD9+vMk6/vDDDxEYGGjWY/W5c+fw7NkzeHh4mFwrqdVqpUlM06ZNcerUKRw5cgQvXrxAoUKF4ObmhpUrV8LJyQkODg5myWZtbY1Ro0bB2tpaOX/duHEDO3fuhIjg119/RYsWLVCwYEGULl0aZcqUgaurK/LmzYvChQujW7duCA4ONtuQNHq9HhcuXMDz58/h7OyMggULwsHBQRlupFy5cjhx4gSaNWuGsmXLws3NDceOHcMnn3yijN1pjmYlRrGxsQgJCVFebS1Tpgw++eQTZQzJy5cvK8dg4K/hiBo2bAiDwYCBAwfC1tYW1apVM9tQTTqdDp07d4aTkxNKlCgBd3d3lCxZEjly5IBWq4VOp1PmNeY0vvIKAAsWLMCoUaOwdOlSk+uefwuOM5kJwsLC0Lx5c4gIPvvsM8yZMyfVRXlwcDD+/PNPAK/GpXNzc0NiYiLmzJmD1q1bo1SpUmbNmDKLcTDrESNGwNPTE8+ePcPevXuxYsUKxMfHo3Dhwpg3bx5KlSoFnU5ncgA218UUADx8+BCFCxdGbGwsTp8+jXr16iExMREhISGIj4/H119/jTx58ijzJyQkoHPnzihQoIDZx6NLSkpCcnIymjZtiubNm6Nfv34my/TixYtYsmQJLl++jAEDBiAoKAgAzD5m49WrV9GhQwc0b94cn3/+uTIos8FgwPz58/Hdd98hf/78mDt3Lp4/f44//vgD7dq1MykWUxZs5irefvvtN3Ts2BF58uRB9erVMXLkSOTMmVNZPsbvjYiIgJ+fH5o3b45x48Ypf4u5b7QMGjQIOp0OX375JQDg0KFDWLp0KZ4+fQoHBwe0atUKPj4+UKlUStvn0NBQJCUlwcrKKstPDMYbP3FxcZg+fTouX74MV1dXXL16VWmX8eGHH8LR0RF16tTB2LFjzbIMHz16hMGDB+PLL7+Eo6MjkpKS0KpVKzx58gQ1atTArFmzlHn/+OMPdOrUCblz54arqyty5MiB06dPQ61WY/PmzUp7JnMty6SkJAQFBUGr1aJFixZo27YtHBwcYGNjo4z5O3/+/De2YcnqgdgfPnyI6OhozJ07F8ePH0fLli1x4sQJPHnyxKR9ad26dTFv3jyzj6X7+PFjaLVa3L17Fw8ePMCFCxcQGRmJ8PBwPHjwAFZWVrCxsUGhQoXg4uKC4sWLo3Tp0nByckKNGjVgMBgyfaw8vV6P48eP4+rVq4iJiYG7uztq1qypnLNSjv3bvHlzdO7cGf369QMAHD16FM+ePUO+fPlQtmxZs7Zdmjt3Ln766ScMHjwYdevWVT6/d+8eZsyYgaNHj6JixYr45ptvAPx1HF6/fj1Wr16NTZs2mZz7MlvKY6xer4ednR1u376Npk2bomrVqujWrRvq1q2LM2fOoFy5clk6TrJxHS5atAg7d+7Ejz/+iMTExFTFjfFvuHz5MoKCgrB8+XJ4enpmaRvilBITE6HT6bB+/Xp8+eWX6NmzJ65evYq7d+/i7t27yj5cvHhxbNy40Ww3grRaLUJDQ/HkyRPExMQgOTkZPj4+8PPzg4eHBwBg1qxZuHTpEtasWYMhQ4bgxIkTGDRoEKZOnQonJydMnjwZbm5uZskHAFeuXMHChQthMBjw+++/K20MjdeaFSpUQLly5ZQxJfPkyYMSJUooP//rr7+iUKFCZum7w2jv3r349ttv8fLlSzx79gwvXrwAABQpUgQPHjxAjRo10KhRIzg5OeGTTz5BgQIFYGNjA3t7e4gItm7diqpVq5r9Wj+78MlkJtDpdEhMTESDBg1w4cIFDBo0CLNnz1bu0gOvnmrkypUL06dPh6urKwwGA2xsbDBs2DCz5zMejOPi4nDlyhV88MEHeP78OcLDw/H111/jwIEDOHfuHJo2bYp8+fLh8OHD2LhxIwYNGmRygSIimV5IPnnyBDdv3oSHhwcKFy4Mg8GAefPmYc2aNZg6dSr8/f0xZMgQdOvWDV988QXGjRunHHQjIiLw8uVL1KxZU8lnrpOGtbU1rK2tUbhwYVy6dCnVhWWlSpXQq1cvLFmyBPPmzYNKpUJgYKBZiyCdTodly5ahdu3a6N27t7JcjNkGDBiAfPnyYf78+Zg9ezbmzJmDGjVqpPo9KZeZuZZfzpw5kZSUhHz58uH8+fOYOnUqRo4cafKEUkRQvHhxDBo0CNOnT0eRIkXQrVs3sy5D4zZjHDgcAM6ePYu+ffuiefPmqFu3Lm7evIl58+bh1q1b+OKLL5RBpGvVqqUU7+aQkJCAsLAwPHv2DNWqVYO1tbWyLPLkyaNcXHp4eEClUmHevHmwtrbGw4cPcfHiRdy7dw+XLl1C69atzbYMbW1tYW1trRQJ1tbWCAoKwpQpU3D58mX88ccfcHZ2BvCqM5k1a9Zgw4YNOH/+PJycnFC9enUMGzbMbG88pGRtbY2OHTvi7NmzWLFiBQ4cOABPT090794dPj4+mDt3LsLCwlCwYME0L1Yzu5BMSEjAqVOnoNPpUKxYMZQvXx4AlO8uXLgwChcuDF9fXxw/fhxdu3bF4MGDkZSUhBs3buD27dsICwtDcHCw2QtJAChYsCAKFiyIjz/+GADQpk0bqNVq/Pjjj+jfvz+GDBmCqKgo3LhxA6dPn8b+/fuRmJgIJycnbNu2LdOLNa1Wi27duiEmJgaRkZGwtrbGmTNnULRoUZQrVw7Aq3UWFhaGdu3awcvLCx07dlSOj56enpma523y5s2LmzdvYuXKlcqTPgAoVqwYBgwYgPj4eBw5cgRDhgzB5MmToVar8fLlS5w6dQqFChWCnZ1dpmeKj4/HmTNn4OnpqbylIiKws7NDbGwsBg8ejLJly+LWrVuYNm0abGxsUKtWrUzP8SbG84JxvytcuDDu3r2Lq1evwsXFJdX8xmOcg4MDVCqV8jaTuc5p8fHx2LZtmzIwfaNGjfDhhx8q+6+VlRVy586NBg0aYMGCBahQoQJ69uwJ4NWNmfv37yM8PByurq5mKyQTExMxYMAA5MqVS3kLbf/+/Vi7di0uX76M7t27o0WLFmjSpAlOnz6N0NBQXL9+HYsXL4abmxvKlCmD3r17m72TmPLly2PZsmUAXnV29vLlS4SFheHGjRtYtmwZ7ty5gydPnmD9+vUAXu3XJUuWhJ2dHZo3b46OHTuaNR8ANGnSBE2aNEFsbCzi4+Nx8+ZNREZG4vr16/juu+9w8uRJnDx5EgBgZ2eHPHnyoFixYsidOzfq16+PgIAAs2fMVln5Tu2/1c6dO6VJkyZy584dmTVrlnh4eMiAAQNM3v+OiYnJlsbVKTvDqF27tvTr109ERHr06CEuLi5SqVIladGihfz888/KzwQHB8vIkSPNni05OVnWrFkjHTt2lNu3byuDv4aHh0u/fv1Eo9EobSg3bdoklStXFl9fX5k+fbrMnDlT/Pz8pEWLFmbp7jsmJkZmzJghO3fulDNnzihd8w8fPlyCg4NN5k3ZluDChQvSs2dPKVeunGzbti3Tc6X05MkTqVOnjqxZsybVtJTv9k+bNk1cXFzk7NmzZs3zd942RE7K8Q4fPnwovXr1kvr160t0dLRZ2k/GxMQoY1aKiGzevFm8vLzkwIEDMm3aNBk3bpzJep0zZ464uLgo7Ur69u0rY8eONVv7XK1WK506dRJ3d3fRaDTi7++vjAH2eruNrVu3ikajkT///DPV7zF3Y3+DwSA+Pj6ycuVKk8+3b98uzs7OMnjw4FTDMBgMhlT7rDlyvu133rlzR/r37y+fffaZ1KlTRzZu3Cg1atRQOgkyt5iYGGnbtq24ubmJRqORGjVqyKJFi9Kc9+effxaNRiO//fZbqmnmHjZl+fLlMnLkSFm2bJnJ96fcJyMjI6Vq1aqydu1a5bOYmBh58uSJ/Pzzz2YZvkKn00nz5s2lY8eOcubMGdFqtRITE2MyZEFSUpLEx8eLu7u7dO/ePVs6JzIupzVr1ohGoxEPDw9p1aqV/PTTTybzhYeHy5gxY6R8+fLSsGFDCQoKks8//9ykA57MlJycLIMGDZKaNWsqHdOkbEfXtGlTCQoKksTERAkPD5caNWqkakNpznbtsbGxMmzYMJNhZS5cuCDVqlWTfv36KX0BpGXr1q3i7e1t1vavMTExEhgYKO7u7vLpp5+KRqORzp07p7mNhYWFSeXKlU32j6waRiUqKkq8vb1lw4YNJp8fP35cOnToIHXq1JFdu3ZJVFSUsn1euHDBJGPKzoIyU2JiooSFhUlERITJ+f/1tpn9+vWTli1byrNnz+Ts2bOybds2mT9/vnTq1En8/f3NOtZ0YmKiPHnyxGR7e33d6XQ68ff3lwkTJsjt27flxIkTsmDBAhk8eLD4+flJnTp1snTIoezCJ5OZIC4uDiVKlMBHH32E7t27AwC2bdumPKFUqVRwdHTM0ldDjKysrKDX63H+/HlUqFABnTt3BgAsXboUhw8fRqFChVCgQAHlztiff/6JuLg4FC9e3OzZ1Go1XFxcMHnyZIwePRqXLl1C/fr1sWDBAnTv3h0GgwHDhg2DjY0NgoKCULp0acyaNQv79u1D7ty5Ua5cOUycOFHpLj8znxyMHz8eO3fuVO5qOjo64qOPPlKGYdiyZQtcXV1RuHBhk6cprq6u6NGjB2xsbFCpUqVMy5OW27dv4+XLl3B3dwdg+gqysf2LlZUV+vTpgx07dmDPnj2oWrWqWTO9TcohcrRaLY4ePYopU6bg8ePHKFu2LHr27Al7e3sUKlQI/fr1U9qAZjatVov69evD1dUVixYtgq2tLTw9PbFq1SqsX78eer0eDRs2hI2NjXKXecCAAbh06RI2bdqEZs2aoWXLlihXrpxZXnvUarUICAhAnjx50K1bN9ja2mLt2rWYP38+atWqZfKUUURQuHBhAMDz589RsmRJk33BnE91jU8OPvvsM5w+fRoBAQHK+vLz80NSUhJGjx4Na2tr9OrVSzmmyGtvOMj/t3fKTMZloNPpMGfOHERERCAqKgqNGjVC7dq14ezsjBkzZuDRo0f4+uuvsWnTJlhZWeGnn37Cr7/+qrztYA7G12wLFy6MYcOGoUSJEpg4cSK2bduG5s2bpzr2fvLJJ3B0dMSFCxdQqVIlk1cSzfXarVarRUhIiNJePTo6GhqNBtOnT4ezs7PJ054PP/wQDg4OuHnzpvJZjhw54OjoiHr16mV6NhHB8uXL4ejoiJEjR6J06dLKmw1ly5YF8Nf6t7KywpYtW5A7d+5sOf8mJyfD2toaN27cgJ+fH4KCgjBkyBDldXovLy8AUN7IaNasGbZu3YrY2FiULFkSU6ZMMctrcWq1Gs2bN0dERASWLl2KpKQkNG/eHElJSfD394ejoyPmz58PtVqtvIbZunVrzJkzB4mJiWjYsKFZXx29efMmdu3ahXv37sHW1hZ169aFq6srfH19sX79epQqVQpt27ZFgQIFlGO0iCA6OhonTpzAJ598Yrb2kVqtVtlPZ8yYgU8//RSLFy/G9u3bcfr0adSvX99k/qJFi6JYsWI4f/482rZtm2Wvyxv32wcPHihtgRMSEmBra4tatWrBzs4OixcvxoIFC1CkSBGsXbvW5LrFeIwxR1tJnU6HoUOH4urVq4iOjoaHhwe6d++OChUqKN9nXK/58+fHlStXkCtXLlStWlW5hklMTERSUpJZ20iOHTsWV65cQVJSEtzc3DB9+nTlKb7xvzly5IBGo8HJkycxePBglC5dWjl/JCQkIDk5OUveHMluLCYzgaenJ6pXrw7gVdFhHJvs9YLS3K9xpcVgMKBXr144fvw4KlSoYNIBgvFEf+jQIURHRyMhIQE7duyASqVSxpY0JxFBlSpVsHTpUvTq1Qt58+ZFkyZNAACffvqp8krIwIEDkZiYCD8/P3z77bd4/vy5crECmKcd56hRozBx4kRcvnwZT58+xZUrV3D79m3Ex8fjxo0bGDVqFAAgV65cKFmyJCpUqKC8llazZk3Mnj3b7Os6f/78SExMxMWLF6HRaFJ9n7EDKAcHB2Xe7GA8ebq7u+PatWsAgAkTJmDatGnYtGkT4uPj0aZNG9jb2yvr0lzjrBovBCpUqIAJEyYoHccUKFAAs2bNQmhoKGJiYpTX5GxsbJRM+fLlw/379wHALBfJKfMVL14ckydPRrFixQC86qBgwYIFuHv3LkqWLKnMr1Kp8Mknn8De3h7nzp1D5cqVTQpIc17wGb+nevXqWL58OU6ePIlGjRopY4QFBARARDBmzBgAUArK1wtcc2Q0FpItW7aEg4MDPvroI9jZ2WH9+vXYuHEjJk2ahOrVq6NEiRIYO3YsLl26hN9//x0HDx5Ubs6Yg1arhZ+fH4oVK4bp06cjf/78sLKyQt++fdGvXz9ERUWlKiZz5cqFHDlyICIiAoD5x2ZMmXHMmDGoWrUqdu7ciSFDhuDKlSvKa8vAX/v2J598gjt37gAwb7t64NX2cu7cOXz88ccoVapUmmOmWllZISEhAWvXrkXNmjWV/SgrxMbGYteuXWjVqpWyHB4/fqx0BjNlyhSMHDkyVUGZK1cuVKtWDdWqVTNrPuOFcJ06dWBtbY3Zs2djxYoVMBgMWLVqFXLmzIk5c+agUKFCAF6tzxIlSmDTpk3w9vbGihUrUKtWLbN0mKXT6fDbb7/Bw8MDX331lbKckpOTUb9+fYwdOxZPnz7FsmXL8PTpU7Rv3145V1y6dAmbN2/GkSNHsG7dOrMUk8Z9w1hI5suXD1ZWVggJCcHWrVsRHx+f6mdsbGyQL18+peO+rGp3rVarUbJkSbi6umLp0qVwd3eHg4ODsn9WrVoVXbt2xZQpU/DNN99g3LhxyvirKWX28dl4s9TBwQGNGjVCbGwsvv/+e+TKlQtly5ZVXuk2FpUVKlTAhg0bcP36deX1ZhGBjY2N2ToF0mq1CAoKgrW1NapUqYLHjx9jx44dyJEjB7744gvlmGP8b/HixbFv3z6loyzjPpbZbcQtGYvJTGA86AKvNqKcOXOaFJRDhgzBzJkzs7yQBF4dCDp37owXL17g3r17uH79OqpUqaIMjqvX67Fu3TqcPn0aRYoUQdmyZTF37lyzPO1LSV4NSwOVSoXff/8dBQsWRGRkJNatW4f8+fPDzc3NpKAcNmwYDAYD/P39TXrce/0pR0YzqVQq6PV65aBqPLH7+PgAAE6ePInr16+jZ8+esLKywq1bt3Dt2jXs2LEDWq0Wtra22L17t9naQKRkZ2eHAgUK4NChQ6hTp47JdmikVquVjgDy589v9kxpMW5D5cuXx/r16/H8+XM4OTnhyZMnSE5OhpOTEw4cOIDKlSub9emBVquFv78/SpUqhSlTpijbkXGg93LlymHJkiXo168ftm3bBhcXF/j7+8Pa2hparRYvXrxAsWLFzNbpjrGQLFGiBKZPn46CBQsqJ6UcOXKgZMmSyJMnT6q2wTY2NnByclIuVrK6s4maNWuiWbNmmDBhAkqWLAlnZ2clt7GHxy+++AIvX77EF198YbYeM183f/585MiRAwsWLEDRokWhUqkwatQobNmyBTExMYiJiVF6361YsSIqVqyI4OBgAObpbCfl+p02bRoKFSqktOuKj49HiRIllDzG7zfeeS9evDhu376dqXnelNFYSM6YMUNpK1WvXj3kzZsXWq0WwF/HSuMyKlu2LLZt24a4uDiz3oU3GAx4+vQprl69qhRrb+qg68KFC5gxYwYmTZqk9Lxs7n1Dp9OhXbt2ePbsGby9vZXzSKtWrZS2pjVr1sS0adMwfPhwfPnllxAR5WlWyra6mZ03OTkZCQkJePz4MYoUKaK0gVSpVJg9ezbGjRuH/PnzY/369SbHYeO1QPHixXHw4EEkJCSYpZA0vjHi4eGBqlWrolatWpg8eTJGjhyJ5cuXQ6VSwcvLCwsXLsSoUaOwd+9ebNu2DZ988onSUaC1tTW++eYbs7RlN+4bpUqVwqRJk5TjM/CqQ7S8efMqxzbjNmlcnx9//DH27NmDqKgos3akFBcXh+vXr6NixYrKPtG8eXMsXboUixYtQv/+/U1u2tasWROhoaH44osvlM7RzCnlzdIpU6agaNGiAICYmBgcP35c6fwppXz58iEpKQkxMTHKZ+bcj1MepydMmKB09BMSEoJLly6ZZDTuo87OzoiLi0N4eDg+/fTTLBmZwdL89/7iDEg5PMWbGB99GwvKgIAA7Nu3D6NHj86ChEjVhbhKpYK7uzuGDx8Oe3t7TJkyBWFhYco0e3t7rFq1Cps3b8bq1auxYMEC5WmMuQrJ5ORkk4NB7969cfjwYaxevRoXL17E/Pnzcfr0aQB/PaH09vbGiBEjcOTIkVR/X2aIi4vD7Nmz0a9fP4wePRpnz54F8Nc6N/63atWqsLKywvPnzxESEoJx48Zhw4YNOHz4MPbt24cDBw6Y5RVhrVaLyMhIXLp0CXfv3oXBYECxYsXQrl07HDlyBDt37kRsbKwyf1JSkvL/L1y4AAcHB7i6umZ6rn/qbUPkNGnSBDt37sS8efP+0T72rt/fp08fvHz5EmPGjEHBggWhUqlw4cIFrFq1CmPHjsUPP/yAYsWKYfXq1ciRIwemTp2KHj16YOrUqRg4cCAuXLiAbt26wdraOtNPZnFxcfD19UXevHmVi/ikpCSo1WpotVps374dJUqUgJ2dHZ4/f27ys7ly5UKuXLnwxx9/IDExURk6Jyv5+fkhX758mDp1Ku7cuaMcBwEgICAAw4cPR1RUFPLly2e2DK9vO3fu3MFHH32EYsWKQaVSYe/evdiyZQvGjBkDlUqFb775Js0nCUDmPz2Ij49Hu3btoFarsXTpUhQqVAiJiYlQq9WIi4vDt99+CycnJzg6OiIyMlK5cDJ2/JUrVy48ePDApBfXzKbT6dCiRQt8/PHHJjczAODBgwdQq9VKQWTc/o3HmYIFC+LFixdKsWkuarUaDg4OyJkzJ3777TdlH3mdiKB69eooVaoUfvvtN7NmMjIWG/fv30dsbCweP36sTGvYsKHS06Qx27Rp0/DkyRMsW7YMhw8fBmD6SmFmHmN0Oh1Gjx6N4OBgNG7cGMHBwUpnITVr1sSwYcPw8ccfw8rKyuQca1z/xuFMihYtapYeM43Lrnz58hg2bBjs7e0BvOpcbMqUKXjw4AGWLVuGn3/+GQAwefJkLF26FKNHj0aNGjXw2WefYciQIVi2bJnJk/PMkpCQgJ49e+L58+eYN28eChcujISEBKjVasTHx2P27NnKK83nz5/HjRs3APz15CpfvnzK/m5OU6ZMQfv27XHq1CnlTSQ/Pz9UrVoV+/btw6pVqxAfHw9ra2vlWNKyZUvkzJlTueYxF2ORVqxYMcycORNFixZVjh+FCxc2uW4SEWXbK1u2LD744ANcvXrVrPmMGVPeTCtRooRyTV26dGnkyZPHpHMnI+M+YXw747+ITyb/RlxcHI4dO4bPPvvM5F1po5Q9pVpZWcHW1takoOzcuTNsbGzQtGlTs2c13m0yjin08OFDfPzxxyhUqBCqVauGGTNmYNCgQRg+fDimTp2qXBgAMDkAG8ceNIeUbZnGjRuH8PBwPH36FP3794efnx+WL1+Orl27YsGCBRgwYACqVq2KTz/9FL1790apUqXM0pucVqtF27ZtERMTAxsbGzx58gT79+/HmjVrTNoPiAiSk5NRuHBhk6cEycnJZm0Te/nyZSxevBjXrl3D48ePkS9fPvTv3x9BQUFo2bIlrl69qrRvadKkCYoUKaKsv9u3b2Pu3LnImzev0g24Obxt+A7jNGdnZxQuXBj169eHSqVShshxdXWFtbW1WXsdVavV8Pb2xqlTp3D9+nV89NFH2LNnD0aMGIH8+fPDYDDg+++/h7u7OyZMmICtW7fiq6++wtmzZ/Ho0SOUKlUK3333ndl6b7158yYePXqE0qVLIzo6Gvnz51f25fbt2+Pq1asoVaoUGjRoALVaDVdXV5QpUwZeXl7Inz8/WrZsiWrVqpnltZ+IiAjlKfib1KxZE61atcI333yD0aNHY+zYsSbHlJCQELRt21Y5EWf2ejYeV4yvoVeoUAHx8fHKSf/HH3/EgAEDMGDAALRt2xaLFy/Ghg0b0KpVK+XC1Zxu3ryJFy9ewNHREYcPH0aTJk1gY2OD+Ph4tG3bFr///jvy58+POnXqQKVSoXjx4vD09ESZMmVQoUIFlC1bVhlLzRwSExPRs2dPPHz4EHPnzlWKXRsbG+j1eowfPx5RUVF4+fIlNmzYgOLFi6Ns2bLKOHAFCxZEzpw5s+RGhqOjIzQaDY4cOYIePXogX758qZ4kpyx2jf8/q55mdOzYEYMGDUJUVBSAtJ8wpiwoR48erbTFqlOnjlmyBQUFIW/evGjQoAE6duyIX3/91eQcX61aNQwePBizZ8/GkiVLYDAY4Ovra3LNY66by7GxsWjVqhUKFy6svPoN/LXcjAXlyJEjsWzZMhgMBjRo0ADVq1dXmheZW3R0tDJO5ZIlSzBs2DDY2toiNjYWbdq0wR9//IF8+fKhTZs2yjGnSpUqKF26NNzc3GBnZ4ctW7Yo+4u5FC1aFHq9HhMmTMCYMWPg7u4OW1tbTJgwAQMGDMCmTZsQFxeHHj16KK8Bh4WF4YMPPkjzzabMYlzHBQsWxJdffgl7e3vlOlOr1eLYsWOoXr260uZepVIp+0z+/PkhIspr/uYSFxeH5s2bo2zZspg0aRKcnJxgMBhgZWWFqKgonD17Ft7e3ibHYGNG45P68PBws2a0aFnQyc97y2AwyKRJk0Sj0cimTZuUz429ORl70rtz5440a9ZMLl++bPLzxvnM2evZ698VExMj/v7+4unpKZUqVZJPP/1UOnTooPSKdvr0aalbt660atVKbt++bfZcadHpdOLj4yMhISEybdo0GTNmjOzbt0+Zfvr0aalUqZIEBwfLypUrpXv37vL7778r0zOz99aYmBjx8vKS9u3by8WLF0VE5KeffpKqVavKjBkzROSv9Wf879ixY6V+/foSGxtr9t4yL1y4IFWqVJFhw4bJunXr5OzZs7JgwQIlq4jI7du3ZciQIaLRaCQ4OFjmzp0r27Ztk3HjxklQUJAEBQUpPaRldi9yxmXy+joxfm5cPg8ePJADBw5I586dxdvbO1WPceaU8jtGjx4t7u7u8vPPP0urVq1k8eLFEhERISIiu3fvFldXVxk4cKCIvPqbDAaD6PX6VD3MmcORI0fE1dVVunTpIvfv31d6VfTz85M1a9bItm3bZOLEidKhQwdxdXUVjUYjGo1GfHx8JD4+3iyZXrx4Ie7u7tK5c+c39o6Ycvlu2LBB/P39pWbNmrJjxw55/vy5ybyZtb+kPKYav1+n00n9+vVl4sSJotfrZc6cOeLp6Sljx44VjUYjX331ldIz4Zw5c8Tf3z9Le/g8ceKEtGzZUnx9fZVeMZs2bSqBgYGyfft2uXLlinzzzTcyefJkady4sZQrV040Go00a9ZMIiMjzZ5v2rRp0qBBA+nbt6+yT8TGxoqvr69oNBpp1KiRVK1aVdnuatWqJZ06dZKZM2fKpk2bUvXaaw7GdX3y5ElxdXVN1ftuynPupUuXxMfHR7Zs2aJ8Zg4xMTFSv359CQ0NladPn4qIiLe3t0yYMOGt32v8/OjRo+Lr66ss88yk1+ulbdu2EhoaKnfv3k0zS8r99+jRoxIQECBNmzZVenk1p5iYGGnSpIloNBqZNGmS6HS6N8577NgxqVOnjgQHB5v0KptVPaM+ePBAxowZI9WrV5c5c+aIiIivr6+0bt1aTp48KVFRUXL27Fn54YcfZMiQIdKoUSPRaDRSs2ZNpTd4czt79qzUq1dP/Pz8xN3dXY4dO6acu2JjY+V///uf1KtXT0JDQ+XixYvy448/yvDhw6VevXpy//59s+VauXKlaDQaGThwoERHRyuf6/V6ad26tWg0GgkMDJT69etL27ZtZcyYMXLs2DEJCwuTp0+fSs+ePeXGjRtmy2cwGGT58uWi0WiU6760Mvbs2VNCQkKkd+/e8vXXX8uRI0dE5FUP9P379zdrz7KWjsXk3zh37pz07t1bqlevbtK9snEHvXv3rtSsWVO6d+8uWq02u2KKyKuNvk2bNtK+fXs5c+aM6HQ6OXbsmNSoUUNq1aoljx49EhGRM2fOiJeXlzRo0MCsB5A3Wbp0qfj4+KQ6eRoMBuWi6fz58+Lu7i7VqlWThg0bmm34D+NFwOPHj5WTklarlcaNGysnjNdPwIsXL5ZKlSqZdGdtDvfu3ZNmzZrJhAkTTL4rrZNnbGysfPfddxIcHCw1a9aU6tWrS4cOHWTu3LnKssvsZajVamXatGnSp08f+d///icHDx40uTg3XrjfvXtXnJ2dZeXKlfLixYtsGSLH6ObNm9K2bVvx8vKSHj16yJMnT0ymb9y4UZydnU2K9ax0+PBhqVSpknTu3FkaNWokrVq1Ui5QU4qIiJDjx4/LtGnT5Nq1a2bLk5SUJNu2bRM3Nzfp16+fcgx5Xcpt8tSpUzJ27FhxdnaWbt26yZIlS0Sr1crLly8zLVdcXJyST+TV8XjHjh3Srl07uXTpkoi82n+8vLxEo9HI8OHDlZ8NDw+Xli1bytChQzMtz9ukPH4YC8qmTZuKp6entGnTxuTYYxQTEyN3796V7777Ls0hXzJTyu+eP3++1KtXTwYMGCB//vmnNG/eXFq1aiXXrl0TvV4vERERcvHiRZk9e7b06tVLXF1dxc3NLcvPI9HR0TJjxgzRaDTSo0cPiYyMNPk7njx5IqNGjRJvb2+zZtPr9SbnEKNu3bpJ27Zt//bnjdtGbGysWfKdOnVKWrZsKadPn051I+fQoUMyffp0GTJkiMydO1e5pjl27Ji0bt1aateuLT/++KNZcom82sYbNGggbdu2lTlz5ki5cuVk1qxZad7gMS6n48ePS926daVt27Zy4MABs2V70/ffv39fRo8eLTVq1JAqVapIcHCwREZGKufWlNtgYmKinD9//o3HTHO4cuWKVK9eXY4dOyadOnWSmjVryi+//KJMT0pKko0bN0pgYKBoNBpxd3eXJk2amPUcIvJqWUyZMkXq1asnQ4cOVZanj4+PNG3aVCZPnixLly6Vbt26ibe3t3LDSqPRyBdffCHPnj0zaz4RkVu3bsn48eNFo9HIwoULldw+Pj5Sv359GTlypAwaNEgCAgKkYsWKys2+ypUry6BBg7Lkhp8lYzH5D1y8eFF69OiRqqC8d++ecrclO8awet3t27fFy8tL9u7dq5wYfvrpJylXrpxs375dTpw4IXfv3hWRVwfl3r17m/3JWloGDx4sn3/+earPo6OjZfjw4bJkyRIReTU+09GjR5WMmZlVq9WKl5eXdO/eXbnYMJ4IwsLCpH79+rJjxw6TnzFO37Nnj2g0Gjlz5kym5UnJeKDdv3+/+Pv7mzzxTnmySjkumPFu34sXLyQ6Olru3btnchGb2etZp9NJkyZNxN/fXzp06CCdOnUSjUYjAwYMMBnP8tatW8q4q68/pTInnU4na9eulTFjxsicOXOU8SFFXo1ZasxqZLwYePTokVSrVk15mpEdDh8+LNWqVZNy5copY0uKiNmeLv+dpKQk2bVrl7i6uv7jglLk1YXswoULpVGjRtK2bVsJCQmRhISEDD8hevTokbRu3Vq5wZKUlCT+/v5SrVo16du3r8m8YWFhUq9ePfnss89k4MCBMmbMGPH19RU/Pz9lnZvriVVa+6rIq4LS399fKleuLOvWrVM+Nz4JN/5NWSll1nnz5km9evWkSpUq0qJFC9HpdG9cRrdu3TLLhd4/WSdPnjyRWbNmiaurqzRu3FjmzZsnp06dkmXLlkmvXr2kZs2aZr9IFhHZsmVLqqf2+/fvFxcXFzl27Ng/+h3m2gbXrl0rrq6uyoWuXq+XR48eSZ8+fUSj0YiLi4tUqVJFXFxcpE2bNso+cfToUQkNDTXb02adTieVK1eWjh07ytOnTyU2Nla+/PJLcXZ2lpkzZ6Z5Y964jE6cOCGVKlWSTp06vfVJZmZ7vaB0d3eXUaNGKdNT3qzNjusqY4bg4GA5duyY3L9/Xz7//HOpVauWXLhwQUaPHq28YZecnCwXLlyQsLAwsxdBKa/fJk6cKPXq1VOe3LZp00bu379v8vaXVquVK1euyNdffy2DBg3K0qd94eHhypss8+fPlxYtWkjr1q1TPVm+efOmnDx5UiZOnChdu3Y161PT9wWLyTdITk42OSCkVVBevnxZZs2alal33NPj9SdNv/zyi2g0GuXJz/bt20Wj0ciiRYvk6dOn0rJlS+XiJeXFQ1Yf+EaMGCFeXl7Kk6uUWYYMGSKNGzdONVBuZmZMSkqSwYMHmywr4/fp9Xr5/PPPRaPRyLp162TlypWyefNmuX//vnLRdOrUKfH09DT73fgxY8ZI06ZN3zrPixcvpGPHjtKmTRsRSbvQMMeFysKFC8XX11e5OSEisnPnTqlcubKEhIQor3/4+PhIly5dsvRmi1arFR8fH2nbtq107txZevToIc7OzjJkyBBlAPVNmzYpJ6mU+9HFixelbt26cvToUbNm/Lt1cvz4cXF1dZVu3bpl6YnKmCs5OTnVMeKfFJSv/x6j58+fp/mE9V1ERUVJaGioyYXkjh07pEqVKuLl5ZXqqXJ4eLjMmDFDgoODpXv37jJt2jSzPa0XefXE5fUmDyJpF5S+vr6yZ8+eNOfJainX98KFC8XDw0O6dOmiDNhtPAYbDAaz5oyLizNpdpAW4/dHR0fL8ePHpX379lKtWjXRaDTi5eUl//vf/8z+BsTbburcunVLateuLfPnz//beTNbfHy88rfv2bNHateuLZs2bZKLFy/KsmXLpEGDBqLRaCQkJESuXr0qYWFhsnbtWqlTp46cOnVK+T3meloq8uqV0XHjxpkMBq/Vav9xQXny5EkJCwszW743MX5/RESEUlBOmzZNmZ5dRWRKwcHBMn36dBF59cCjY8eOUqFCBalatar89ttv2ZLp9YLS3d1datSoIdevXzeZ/vp+khXNS15nLChdXV2lZs2aSobExMRU6/f1OuG/jMVkCnq9Xu7cufPG6efOnZPu3buLu7u7SRvK7LwA0Ol0MmXKFBF5dae2Zs2asmjRItm2bZtoNBr58ssvlXk9PT1l5syZWZYt5cVHyuJw9+7dUr16dZk5c6bS1st4gTJhwgTp1q2bWXMlJibKhg0bpGHDhtKkSRPlCYdWqxVfX1+pWLGiNGvWTOrVq6e8auHm5iY+Pj4yZMgQWbZsWarXI81h5MiREhAQ8NZ59Hq99OzZU7y9vc1yYfwmw4cPl44dOyr/Nq7rI0eOiKenp/wfe98dFtWZt/2oSTbJZjfvbvImm7y7yfYDDGUEGcoICMjQhl4GpCNFQRCxoYJdxEaiGBUVe4uNWBNFIzYs2LChERVBBQuCzFAGGO7vD77z7DnMYLK7nNHdeF9XrsgwMD+e87Rfu+/w8HCUlZWhqqpKr9Hjjo4OpKenw8/Pj142VCoVNmzYAENDQ6SkpPB6hR88eIDS0lI8f/4clZWVmDhxIlxcXAQrTeI+I10XTO7BxJa8xsfH00NXaLyodFutVmPv3r0wNTVFSkrKTxojoS7Rfn5+WLx4Me+1AwcOQCQSYcSIETr38e77tBCXAKVSCUtLS8TGxuq8CHFfu3DhAnUouf1pL/M84Y5JXl4e7O3tMWLECBqE0YdTVF5eDicnJ9jb2/N65X8M9+/fx7Vr1/D8+XNBHaGfipycHIjFYprd08dzVSqVcHBwwIoVKwB0OeY+Pj4wMjKCSCQCwzBQKBT4+uuveUFwlUoFExMTFBYWCm4jN2DVHU1NTS90KIUew9bW1hfua9z9u6GhgfZQzp49m74utHPR0tKCkpISrTnOfu4XX3zBq7pJTEyEsbExLC0tcerUKUFtexG4DuWsWbNgb2+PjIwMWlXVUyWHUHjRZ9y9exczZswAwzD46quv6Ov6rgr6T8JrZ/L/Q61Ww9fXF2KxGPHx8RgzZgx27dqF0tJSXlaloqICCQkJsLW1xebNm+nrLys6sWfPHjAMg+LiYrS3tyMxMREmJiYQiUT0stXe3o6rV6/C0dFRL4cF+5lA1+EwZ84cDBs2DCtWrEBdXR3a2towatQoSKVSzJ07l7733r17UCgUmDZtmmB2cQljdu/eDQcHB3h6euLp06fw8fGBQqGgUd3GxkZUVVVh69atmDhxIpycnGBlZSUIcZGuktR169bB1NSUZvm6g33f1q1bIRKJ8ODBA71tdnPmzIGTkxO9kGg0Gvo3HDt2DObm5hg5cqTgfaXdoVarIZfLMXnyZN7rVVVVsLKyoj1WNTU1aGtrw4oVKyASidC/f3/I5XLY29sLVhrX2tqKxMREWsYN6K4QqKqqon0urEM5bNgwwUv2ysrKIBKJ6P63b98+lJaWapUn79u3D+bm5khJSeFlFvQBdry++uorxMbG0hItdu7t27cPIpEII0eO5DmU+lgXLJlXTEyMzgsp9/kuXboUwD96KH19ffWyNzc3N78wY8cNAAJd4+zo6Ijk5GRa6iX0WHZ2duLChQsIDAyEVCrFtWvXenxfd3tepSzBtWvX4OHhgWnTpukloKZUKjF48GAMGTIEtbW1dCyam5uRl5eHRYsWYcOGDVCr1byyQo1Gg9LSUsjl8n/Kef9nwRKZdScw7H6p/zGHUihoNBqEh4fDz89PJxkSa29lZSVSUlLw5MkTPHz4EJmZmZBKpcjKytKLnUuXLoVUKsXBgwdp7zgXe/bsgUwmg0ajwZgxY2BtbY19+/YhJiYGhoaGOH36tGC2tbe3v3Cus0mFjo4O5OTkwM7ODuPHj0dDQwN9XWj8WNCdnZ/V1dXIysqi1X0sXqU95lXCa2fy/6O2thaJiYk0E8VG81jWOoVCgTlz5uD06dPYuHEj4uLiMGDAAHzzzTcv1e779+9j8ODByMzMBNBVTiaXyyEWi5Gfn48HDx6gqKgIISEhCA4O1stC4DLLurm5wd7enjICpqen4+nTp2htbcXIkSMhkUgwaNAgDBkyBC4uLnrpZdLlUDIMA39/f7S0tNDP755ZaGho0GtJ88WLF2FiYoLU1FReOWl3xzMrKwsKhULQaF5zczNlBAa6DiypVIqCggJ6oHEdyn379oFhGF6votDQaDSora2FtbU1ZsyYAeAfG79arUZ6ejq2b98OMzMzzJo1C0DXWK5atQpLlizB9u3baQZGCFRWVsLPzw8ymQxr1qzh2c11NAwMDDBu3Dg6D48dOwaGYZCWlqZV/t2b+O677+j+Z2BgQDMZUqkUoaGhmDFjBvbv348zZ86gsLAQRkZGyMrK0htTIReXLl2CSCTCzp07AfBLL1mHMi0tTW+lcCxDdUJCghYZDABewMzExASTJk2iz/LUqVMYPHgwFAqFoOXgnZ2dtLxfl4PGzsG7d+8iKiqKBi8WLVqEwYMHIyoqSm/BA41Gg3PnziEgIECnQ8nd686fP69XMpaeoMvJnjZtGqRSKY4ePSro/qxSqSCTyRAeHs7rgevp4sxlvW1oaMDEiRPh7+8vGNFJc3Mz7OzsEBsbiwULFuj8HO74cB3K3NxcvbRJdHZ24uDBg5SlmNsvyl2/LOEK6zQ9fPgQ6enpcHFx0QtRzPfffw+GYeDt7Y0DBw7QfYQdv7KyMri6uiI8PBw2Nja0tPXOnTtITEx8YfXdv4PGxkaEhYXx+vy5YPeXBw8e4OLFi9BoNJgxYwYcHBwwceJEvQSelUolhg0b9qM21tbWorq6Gk+fPqU9lGwA8DV042fvTDY1NdGI8LVr1zBx4kQYGBhg165duH79Or799ltMnToV4eHhkEgktOSRyzalDwptgH8wcC9PbGbq7NmzALpK1YYOHQonJydK6R4dHU2dI304lGq1GiEhIYiKisLdu3dprTzDMBgzZgzNUO7btw+TJk3CxIkTsWzZMvo3Cm0j16EsLCyEj48PnJ2d6aHFdSSFtoXtWUlNTcWoUaNQUFBAx2HlypVgGAbTpk3TyohqNBr88MMP8Pf35/Vt9DZ6kshhS74PHjzI639lxyslJQVRUVFobW3Va+neqFGjIJFIcP36dWrX/PnzIZVK0dDQgBUrVsDAwACHDx/Wm00sbt68idjYWDg7O/McSqBrHgwYMABjx47l0acDXT2UQvWA3bhxg2bSDhw4AIZhkJSUhO3bt+PEiROYOXMmQkJCYGtrC4ZhYGJiQgMwDMMgJSVFr0x27FyaMmUKJBIJlZnp7lCampoiJiZGcGdXqVTCw8MDDMNQOQhA+yJ/+/Zt2NjYID09XSsodfbsWb1Ia5w5cwYKhULLQeMGMywsLJCSksK7wOfk5EAulwviTKrVapSWlmLVqlXYsmULzZx0dnbi/PnzWhlK7n588eJFBAcHw8TEBPX19XrdZ3RlRGtra2mWhUVgYCDs7e0F631WKpXw9vamdxFdGT/uvw8ePIja2lq0t7fjzJkzGD9+PCwsLAStfLh06RIYhoG7uzuCg4MhFosxY8YMrd507pppampCfn4+ZdfUV+ljcXExJBIJYmJieGvy7t27kEgkOgkXa2tre5RO6m0UFRVBLBZDJpPB2tqad/4CXfPSw8MDjo6OuHDhAu9nhepBZLPiQUFBOh1qbla3f//+lC21o6MDs2fPhlgsxpQpUwStelAqlRg0aBDCw8N1kgFybbSysqI2VlVVUZbXlStXCmbffzp+9s5kUlISgoOD6dc3b95EcnIyTExM6GWTneCPHz/GxYsXsWPHDowePRqRkZEYMGCAXqUOmpubtYgJKisrERAQgJkzZ9I6+tbWVlouV1FRQf8GffXVsQQTV69eBdAVjZo4cSLi4uJgZGSE1NTUHnW19FVGwB5ObW1tNNPm6upKLwP6GKvz58/D0dER/v7+UCgUkMlkSE9Pp9HDlpYWzJs3DwzDYOjQodi/fz+VUDl8+DCGDBkCPz8/wbO5XImcDRs20M8KCAiAvb29zpKb9PR0KBQKQexh0dbWhpqaGpSVldGs4u3bt+Hv70+p2/38/GBsbExLR2/evAmpVIr8/Hz6d3D/LxTY33/z5k3ExMTwHMrOzk4kJCRg+PDheiUrevToEQYMGIBt27bR19h+a67eIAA8efIE58+fR2FhIcaPH49hw4bB2NgYQUFBL6WXpLS0FAEBAQgNDaXledzs+M6dOxEeHi74BcXZ2RlBQUE0gs3VKePuZfHx8YiNjeU935cxbhcuXEBAQABsbGx4DmVlZSUMDQ0xduxYaiPXPiECBkqlElFRUZDJZBCLxZBIJHBzc6PyLp2dnbh48SL8/f0hlUp5xEYXLlxAaGio4I5Qc3Mzvv76a8oKzdoGdI0PN2slFotpjxX77G/cuAEfHx9BggVsaXVQUBA8PT3h4uLC0xbsjosXL8LQ0BASiQS2trYYPHgwvLy89MJ6m5WVhbi4OFy8eBG5ublwdnaGkZERUlJS8PXXX+v8GaVSidWrVwt6x+ro6NByvHU5lF999RVGjhzJW79Ck1HpQm5uLiIiIlBfX4+QkBBa8sotIT1x4oTeiNu4Wt2sQ80dE3Z93L17F1KpFKmpqVAqlbxy5/nz5wtaRcLaGB0drdPpZ+2trKyERCJBWloaL+B39+5dzJ49+2etI/lj+Nk7kzNnzoSPjw/v0L916xaSkpIgEol4pX26Dn59Xvw6OjqQkZFBy0W5JbZfffUVLC0tX9g8rs+Ly549eyASiShxyPz58+Hq6oq7d+9i8+bNYBgGkyZN0pLf0Ce4JZAA8O2338LBwQGurq50IxHSsS0vL4e1tTWmT59Oncfm5mZeSSvQtdFt3LgRtra2MDIygrOzM9WHSkhI0Fs2l8tozLIC19fXw9/fnzqZ7Lg9ffoUiYmJGDNmDK9HpzehVCoRERGBoKAgGBkZwc3NjV6Ea2trkZubi+TkZIwZMwY3btzg/ay7uzsmTJjQ6za9CNxLy40bN6hDuXbtWgBdB5a+JYaamppgaWmJVatW8V7nOpRsQEgX7ty5Q/eV3txfKisrf1JGcevWrZDJZAgMDNSKwnMhxN7X3NwMa2trREREoLGxETU1NZg9ezYYhqFsigC/N+xlEsNwg2dsRYOjoyN1JEpKSrQuUYBw54ZKpYK7uzsiIyNx5MgRdHR04Pbt2zzJJXY9lJWVUYeyvLwc169fR2hoKMRisaCOEMsM7ejoCEdHR1haWsLJyUmr5O1FWSsAlGiuN9HY2Ag7OzvExMSgvr4eDx8+hFwux+DBg3H8+HGdwdCWlhaUlZUhOzsbM2fOxO7du/Wmg7hhwwY4ODjQCqrLly/jwIEDlIXXx8cH+fn5Wo6jUM7ai4LF7e3t1KHkOiD6JLnrCUlJSQgPDwfQ1dYUGhpKHUp2nunrrsc6aVydVa6TyO59KpUKxsbGSE5O5q0PfYxndz1xro1c1vK6ujrKBK1rDb8Kz/5Vxs/emVy9ejUsLCy0oq63bt1CcnIyRCIR7cfQpQGmr2wGi3v37mHnzp1UVy0yMhJHjx5FbW0toqKieOLc+gLXRnZhnj59GklJSWhoaMC3334LhmEokczJkyfBMAwMDQ2Rnp7+UtgLuSUNMpkMJSUl6OjowJ49e+Ds7AwbGxtB+yPVajUmTJiA1NTUHiUTukeXb968iQMHDiAnJwdLly7FsWPHBM849ySRI5FIKAFVW1sbhg4dCltbW/j6+iItLQ0hISEYMGCAYJG81tZW+Pn5ITIyEmfOnEF5eTlWr14NsViMyMhInZ/LBg1qamrg7e2N1atXC2Ib0OWk5ebmYt26dSgvL0dtba3WAV9eXo6YmBgMGjSIOpSA/tg8Ozo6oFarERgYiKlTpwLgzzlWWiglJYXnjOsKWvRmIKOxsRFSqRRhYWE9yu9wx7KwsBBBQUGwtLTEli1btPZyoYIspaWlyM7O5pV+PnjwANnZ2VoOpZC9rj1BF209+3xbW1upOLhUKqXZNn1R8be1tSEtLQ1RUVG4f/++FikLABQUFCA1NZWW97MOpbW1NTw9PQXPSGo0GkycOBFhYWE0KHrlyhVMmTIFDMPQvuyGhgY4OTlplQYLjS1btsDPz4/nDN6/f1+nQ6nvM1atVtOx4D5TLy8vREdH8+w1NzfH8OHDoVAowDAMrKysMG7cuJ+s0fmvoKmpCa6urkhMTERWVhYOHTqES5cuobW1VYtR29bWFuHh4bzM8stkXH7+/Dkvi9fdodTXXqNUKiGTyXjONruO1Wo1nJyceHvguXPn9C6jxzqSsbGxdJ/m2uji4kITGmzL06ugGf+fiJ+tM8kuOJaZkF2c3EuKLodSn+BmnJ49e4aGhgZq98OHD3HkyBH4+vrCxcUF/v7+GDZsGCIiIvQWaeTayEZ4uAuRLZGLiIigTGcdHR3YsWMHzXj0xOj270CtVqO9vV3nBYW1FfhHScOoUaPoJtfe3o7t27draSj2Np4/fw5nZ2daatkT6uvrMW7cuBc2p/d2FPKfkcjhiq5v3rwZEydORGhoKCZMmCBoSUhRURGkUimPma65uRlz584FwzAICwuj86+2thZz587FnDlzsGLFCkyYMAFSqVSw59vZ2UlLkxmGgY2NDSwsLBAeHo7p06djy5YtKC8vh1qtxoMHDxAfHw9PT0+eQ6kPxkwWY8aMoRI03DJRoGeHUkhoNBrs378f1tbWSEhI+EkO5fnz55GdnQ1DQ0PExsYiNzcXDQ0NgvZyss54dzx8+FCnQ6lPFsCWlhYkJibi0KFDWoRira2t8PT0RHBwMIqKihAUFAQbG5sXZqB7GxUVFfD29sbOnTt1BmkXL15M109qairdjy5evIiAgAAYGxvrpTQzJCSEBlpYPHr0CEuXLoVIJMLMmTPpJbR7n7M+0N7erlWm/+DBg5/sUArhFDU1NcHR0RGJiYn0NfbZbt68mZYrl5eXw9LSEsnJyVRqq7CwECkpKZBIJKisrOx121hcvXqVx3vB/sdm2RYuXIiioiJcvXoVhw8fpuzkQtr0U9DTHsJ1KIuKigTJhHPR1tYGZ2dn2Nra0kwy10nz9PSEQqF4KeRsLFpbW+Hu7g4HBwd6J2b3QLVaDS8vL/j6+uLhw4evJT96AW+QnxHa2tqIRqMh77zzDnnrrbcIIYSIRCLSt29fcuXKFfLHP/6R9O3bl2g0GtKvXz/y17/+laSlpRFCCBk7diyZNWsW8fDw0IutGo2GvPHGG0SlUpGJEyeSyspK0tTURD788EMyduxYYmJiQgYNGkTs7e3J/v37yYkTJ8g333xDCCHk2LFjJCgoSG82NjU1kezsbHLr1i3S3t5ODA0NSUJCAvn0008JIYTU19eTP/3pT+T58+dEqVSSnTt3ko8++oiIRCL6e/r169crNrW0tBBXV1fyt7/9jYhEIhIVFUU++OAD3nv69u1LampqSHBwMLG1tSXTp08n7733HiGEkDfeeIP4+voSV1dX+poQuH//PmlsbCQmJiaEEELa29vJm2++qfW+EydOkF27dhFLS0sSGBhIOjs7SZ8+fUifPn0IANKnTx/St2/fXrOrra2NKBQKUllZSSwtLcn7779P7OzsyKeffkoMDAzIe++9R8zNzcnYsWPJ3LlzyZIlS0hnZycJDw8nISEhJCQkhLS3t5N+/fr1ql3dUVNTQxoaGsiHH35ICOkav3feeYf89a9/JZaWluThw4dkwoQJZP369eTjjz8mffr0IQcPHiR9+vQhn3zyCVm5ciX57LPPBLGtT58+ZNCgQaS+vp7s3buXMAxD3N3dSWFhIdm7dy9paWkhGo2GvPfee2TgwIGkubmZPHnyhKxfv55oNBoSExMj2Ng1NjaSt99+m+5/hBDy+9//npw+fZoQQuj+x84rHx8fQggh48ePJ/369SNxcXF03QqFvn37EldXV/Lmm2+SjIwMMm3aNDJlyhS6n3Dfx64Bc3NzYm5uTjw9Pcnp06fJ3r17yfnz54lGoyFr164lb775JunTp8+/bVtbWxsdu57m+CeffEKio6MJIYSsWrWKEELIuHHjSL9+/Xp1r3sRVCoVuXLlCrl79y75xS9+QQYMGEDefvttolarSUBAAPnlL39JFi9eTP73f/+XfPjhh2TWrFkkISGBrFy5khgaGgpu35UrV8i9e/eItbU1fS7suOTl5ZGlS5fSvSUtLY0AILNmzSJmZmZk4sSJ5JNPPiGffPKJoDaqVCrS2dlJ7WKf/UcffUQUCgXp06cP+eKLL8gnn3xCYmNjBbWFRWtrKzl8+DCprq4mn3/+OTEzMyOffvopb159+umnJD8/nyQmJtK1Y21tTd54Q/u61xtrgguVSkUCAgLIw4cPSXt7O1GpVOS9996jttnZ2ZElS5aQJUuWkHPnzhGJREKysrLIb3/7W0IIIb6+vsTHx4eoVCryq1/9qldtI4SQpqYmcubMGeLk5ERWr15Nhg4dSkxMTIi7uzv5wx/+QIqLi0l5eTlZs2YNaW5uJu+++y754IMPSL9+/ch3331HmpubSVZWFvnDH/7Q67Z1B7u3cdF972Df85vf/IZ89dVXJDU1laSlpZG8vDzi6OgomG19+/YllpaWZO/eveT48ePkt7/9LfnNb35D2traiJ+fH/nVr35FvvzyS/K73/3uR/8modDc3Ew0Gg158803yaFDh4hCoSBvvvkmaWlpIUFBQeSXv/wlWbRoEfn44491/ry+9ur/GrxMT1afaGlpgbu7OwYMGAC5XI6MjAysXr0a27dvh0QiwY4dO3r82YqKCkRERMDW1lZvmkdAV6bFw8MDCoUC+fn5WLZsGeLi4mBgYIBNmzZp2XLs2DEsWrRIr7XdTU1NcHNzg0KhwJQpU5CTkwMHBwc4OjrSns6xY8fC2NgYCoUCLi4ughLG/FTWuOLiYuTk5GiVXQhZvlJRUUHLpWpra2FhYYEvvvjihT9TX1/PYxYTGv+qRI6+9EtZHD58WCeT8pQpUzB58mR89913EIvFyM7Opt97+vQpGhoaBCtjaW5uRkFBAf364sWLSE9PpzqwQNd6uXTpEr7++mtkZ2fDz8+PMoGykXGhMhxXrlzBoEGDEBgYiClTpmD16tUoKSnB5s2bMXDgQDo3WXD3EbaHMjc3t9ft4uoFdheuLioqgoWFxQszlN1/D4vnz5/3amayqakJgwYNQlBQEHbu3KnFrtw9a8DNUM6fP7/X7HgR2traaBXDw4cP4eXlBScnJ5w/fx4qlQpyuRzBwcG80lxW1zEkJARGRkZ6yfht3boVZmZmWlI85eXlCAwMxN69e+l4rlixAgzDUIkDIdHa2srLPs2fPx/GxsY0M8pdE48fP8b06dPh4OAgqHYfC6VSCT8/P9jZ2cHQ0JDKWfVUAcLNUL6IlKc37XNyckJiYiIWL14MIyMjXjkmu7ZXr14NhmEQHx/P2+uEzg5pNBokJiZCLpfzNJENDAwwdOhQ3jhWV1fj3LlzWLt2LUaNGoXIyEgYGxtDLBYLKo/Tff51R3eeh+6oq6tDXFycYPIfXBs6OjqQmZkJkUiEzZs348mTJ3R/0ZWRbGpqwo4dO/Qi18Ta+OjRIwQFBcHZ2Rnbt29Ha2urzj2QhUqlwt69ewW3778RPxtn8vbt21iyZAmmTZuGxMREODo60hI0hmFgbGyMlJQUTJkyBZs2bcKFCxfo5UqtVqOmpkZvKXvupuvl5cXb5FjymkOHDtFeO12HhL4cyry8PHh7e/Oa5tesWQOGYXD06FE0NTVBo9Fg2rRpGDVqFGbNmiU4Ycw/wxqnLybP0tJSWFpaYteuXWhra0NjYyPCw8Ph6enZoyg30FW+YmVlJbjG0X+SRA7QxSyakJAAkUiETZs2Yd++fZgyZQpEIhFdL+PGjYOHhwelKhf6GbOleVyZlsuXLyM5ORkMw/AYU1k0NTVBqVTi5MmTKCwsFIy1UKPR4ODBgxgxYgRiY2Nha2vL05I0NDREVFQUsrKysHHjRly+fFmLfbKkpESQfYV1+HQ9n7a2NhQVFcHc3PwnOZSAcJdSVtuSJb+SSqVYsmQJjzAG4O9rDx8+RE5ODhiGwcKFCwWxi0VjYyN8fX1x8OBBOgYsKYuTkxOcnJwQHBzM69HmjtXZs2cRHR0t6EWUxbFjx+gltPtz787yPXLkSHh5eQluk0ajQXBwMBQKBXWoa2pq4OPjAw8PD1qKyV0D165dg62tLdatWyeobdzeLzYwkJeXh/79+yMtLa1HofgHDx7A19cXlpaWOHXqlKD2OTo6IiIiAnV1dbQ8nst8y+LixYtwcHDAlClT9E5qMnbsWLi5ufFeKy4uhoGBAaKionok8Wpvb0dFRYWgLUTc+ccN7HUvA7937x7i4+N7ZMMX2invPtcmTZoEkUgEa2vrHqVBVCoVJkyYAFtbW0G1nHWhtraWOpQDBw5EcHCwTmkQlUqFrKwsmJqa6k1P978JPxtnsjvq6upw//59lJaWIi4uDgzDQC6X0wuygYEBxGIxbG1tkZaWppd+l+4b65QpU+Dm5kYZAPfu3QuGYVBQUIBz584hMTFRS9NKaHQfh5SUFISFhdGvd+3aBUNDQxQUFODYsWOYPHmyzt8j5CHyY6xx3t7eOlnjhMK9e/dgZWUFPz8/3uusSPzEiRN5/Xvc4EBxcTHc3NyorIVQ+E+TyAG6CDlYdmMLCws4ODjwxIiPHz/eo0C7EKiqqkJWVhasra15GdErV64gJSUFDMPwMrgvkx3u+fPnuHfvHoqLi7Fs2TIwDAOZTEZJWdg90NPTEx4eHjhy5Iggdl++fBkMwyAqKgqpqanYuXMnTp48iSdPnvD2mkOHDsHKyuqFlyih8fTpUyQnJyM3NxeHDh1CdnY2zMzMYGlpiZSUFBQXF+vcj6urq5GbmytoDzGr89bdWQS6HEqW3GTXrl0v7FMXuteKRVtbG1xdXaFQKKi9uuy5evUqAgMDsXTpUr3IMBQVFaF///5ISEigz2vv3r0YPHgwFAoFdSi5mSE/Pz+MHTtWMJu4jJm1tbW8MZgwYQJsbGxeqHFYXV0NhUIhWL9fY2MjnJycEB0dTZ2tixcvwsDAgAbQuj83dp980bPvTbC/f/fu3TTTzOVVOHr0KAwMDBAbG8uTXtMXIRULdv4lJibyetRZOyorKzFgwACMHj1ar8zQzc3NyMvLQ2JiIjw8PLBw4UKaVe7s7KQVGAsXLtSq9mKdNLFYzJP2EcLGgoICjBkzBlFRUdi4cSMNVNbW1iIkJASGhoZYsmQJ/Rn2+bM2mpmZ6e2+8N+G/2pnUq1W49KlSzhy5IjWIc+N3syfPx8KhQKPHj1CS0sLLly4gMLCQsyfPx9Dhw6lGmZCgrVHqVRi06ZNAIDp06dDJpMB6JLaYBgGy5YtAwAcOXIERkZGOHPmjOC2dbdRpVLRz01LS0NoaKhOG/ft2wdDQ0Ncv35d7+xn/wxrHNcBEQJXrlxBdHQ0Bg4ciPXr1/O+t2jRIhgYGGDMmDE4f/4873s//PADgoODERsbK3i08T9JIqf7XLp165ZOWY39+/fDzs7uJ2WzessmVk/1pzqUQq+LZ8+eoaKiAvfv338hJXtAQACVhbhw4QL27t2LvLw8xMbGIjY2VjDH9+jRo9R5NTIyoplSa2tr+Pv7IysrC4WFhTh58iT27t0LkUiE8ePHC6LZ91NQUFCA/v3708xVWVkZNm3aBGtra1haWsLLywt79+7VchyFDBz8mM4b0OVQent7U1IWfV+SuWD3mH379sHU1BQpKSk6y5GfPXuGjIwMuLi46DWbUVxcDFNTUwwdOpSW5G3atAnOzs7w8vLiBTNYZmhWW7K3wTJmhoeH88aIdWa3bt0KiUTyo6XJQs2/9vZ2xMfHIygoiOfQVlVVQSwWa1XUsM/+8uXLsLa2xoQJE/RKTHXx4kUwDEOztJ2dnT/qUOr77sLOv4SEBJ5Dee/ePQwYMACjRo3S63mrVCrh7e2NoKAgqoUcExPDm3Pt7e3IysqCSCTC6tWrqaOpUqmQmZkJU1NTQZ00pVIJX19f+Pn5ISwsDL6+vkhMTMTdu3fp/Hry5AmCg4MxePBgbNiwgT7XxsZGmpF87Uj+6/ivdSaVSiViYmJoSZ6TkxN1gLpvXgcPHgTDMHpls+OCWwcvl8sxZMgQPHv2jPb/xcbGwsjIiMf8+c0338DJyUlv7GKsja2trbC3t0d4eDg6Ojrw9ddfg2EYZGZmwsjIiHd4rFy5EjKZTK/ZU/ZgeBVY465fv04jr9euXUNSUhKsra15JVFKpRL5+flgGAaDBg2iZYZZWVnw9/dHcHAwvfgJ6VC+6hI5utA9w1JdXY0TJ07gwYMHqKmpQWZmJgICAvQ2/1g7lEolJkyYAJFIxBOw5zqU+tBXLSsrQ2BgIPr37w8zMzNMmjRJq4SRZYMMCQnB0KFDtX4HNxvUm5e+8vJy6hAeO3YMDMMgISEBO3bswNmzZzFv3jxER0fD3t4ehoaGEIlEsLW1pY5nQkICXctCgzsGLS0t8PPzQ3JyMi33unr1KsRiMcLDw+Hn5weGYWBvb4+srCzB+w9fpPPW3t7OY4FmS16dnZ1x7NgxvWTGX7RnPXv2DIsXL4axsTEiIyNRVFSEuro6tLa24sCBAxg9evRPcpT+XXAdCvY5cx1Ktmpk165dkMvlEIvFWLx4MRYuXIiRI0fCyspKsD6wL7/8EgzDYN26dTQTxX1uaWlpkMlkL03OoKWlBWfPnuWdGxqNBvX19bC3t0dmZiYA7fOhpaUFPj4+8PX11Yvt7N719OlTODk5YeXKlbzXuzuU8fHxWsFdofCi+ZeQkEDn/6JFi5CRkaHXZ93c3AxfX19ERkbS0ltWXUAX2HNv9erVqKmpwZQpUwR30pqbm+Hl5YXIyEjcuHGD3pe4lVLsuLIlr6xDqVQqMW3atNeOZC/gv9KZVCqVcHFxgUKhwPr167FmzRrI5XK4urrqLA24cOECGIahzmZ3EgghUFdXx6s9V6vVWLFiBWJiYnDv3j1oNBq0trZizpw5VFYA6HLmbt26haCgICQmJuqV0ri1tRU7duxAUlIS7t27h87OTqjVakoyMnbsWDpeFRUVUCgUSEtLeymOxv3792FnZ4fhw4dTR7K2tlbLARJK96i0tBQMw/C0DHtyKIGuC/Xo0aOpMLZCocDcuXPpxUGoi99/gkQOC+48Yp9jVVUVNm3ahOfPn+PUqVMwMDCAra0t3NzcYGdnJ9hF9Mf6lGUyGWxtbSGRSLQylCNHjhS8x7S0tBRisRjJycnIy8vDpEmTwDAM1qxZw3sf+5xnzJgBJycn1NXV8QSdWfTmGn7y5ImWrExRUREl5eA6vHV1dbh27RrtiU1NTYWFhQWCgoJeCp17Z2cnFixYADs7O9TX1+OHH36ApaUlUlNTUVtbi5aWFhQWFkKhUMDW1lbQklyuzhvrWHfXeZs4cSLv2bEOpaurKw4fPizovvKiIAT7uXV1ddi6dSvs7e1hbGwMiUQCW1tbuLq6apGi9DZ02cWdU+yFPjY2lgYc7969i+nTp8PJyYn2CAopmaNSqZCSkgILCwsUFhbyzqtFixbBxMQE586d07JdaDQ1NdHP7WlvUCgUNOjMfc/z58+xceNGLF26VLDWCJVKhfz8fBw7dgxVVVU8ssLExETEx8dr/Qw7fmx7xIgRIwQt+/6p8y8+Pp4G3vSZxQW6WoZCQ0Nx8+ZNrc++efMmtm3bhu3bt/P6xidMmABTU1N4eXnB3NxccCdtxYoVUCgU+OGHH7Tm4qNHj1BUVISTJ0/SuVZTU4OgoCDIZDL4+/u/diR7Cf91ziQ3UsstjVm2bBlEIpFOcoG6ujqIxWKa+RNyU+7s7KS9N2yTb0dHB7Kzs2FlZQW5XM77/Lt371ICB4VCgeDgYHh5ecHPz08vGSsWHR0dyMrKgpGREYKCgnhOeXl5OXUoR40ahcTERPj4+MDHx4enQ6kvvGzWuNLSUohEIsyePVurWf3KlSs9OpRqtRpNTU2oqqri9eT09gGiVqu1gipsL8bu3bt1fi7rUIrFYsGJdtRqNU6dOoU1a9bgm2++QUlJidb3ga6yH5FIhKysLLoWSkpKsGnTJmzfvl2wi3xbWxt8fHx4WUd2nqvVanh4eCA0NBQXL15EZmYmrKyseA7lpUuXMG7cOMEuyuz8mzVrFm/ey+VyREdH65xPa9euhYmJiV5IxtRqNWxtbWlpILseWYcyLi7uhcyd1dXVOh3e3kJzczPy8/MxadIk5OXlaZXU1tfXw8bGBsnJyZBIJLy9nMXjx49fqA3776K9vR1ubm4Qi8U0YMJepH5M562mpgZ2dnbw9fXtkbjl34FarcbQoUN5jix3zrH/vnPnDrZv3w7gHwRgBQUFWLlyJcrLywXVbWxqaoJcLkdiYiIyMzNx4MABXL16VSvrU1xcDLFYjNjYWB4pSm1tLSXPEhotLS0YPnw4+vfvj507dwIAlixZApFIhP379wv++d3R2dmJ6dOng2EYLZZ09vtAVx++l5cX79mzvWkMwwiWzdVoNEhISKBVDP3794eXlxcmTJiAbdu2ISsrCwkJCVr2sj8LdJ0jQnIA/CvzjxsY1dd9KisrC0OGDOHdR5qampCVlcWrFBk8eDBt4ens7KRcBvpghh4xYgSSkpJ4rzU3N2PatGmUaJMl22RZl2tra+Hh4QEzMzO92PhzwH+VzqRKpSI+Pj7ks88+I3PmzCEff/wx6ejoIG+88Qb54IMPyB/+8Afy/vvvU60b9v//8z//Qz766CNy584dQggRVB+vT58+5IMPPiDNzc3k6NGjRKFQkH79+hFbW1tSUVFBTp48SXbt2kX8/PwIIYT88Y9/JMnJycTZ2Zns3LmTvPvuu+QPf/gDCQsLI2+88Qb9+4RGv379iFgsJrdu3SK3bt0iDx48IH/9618JIYQYGBiQGTNmEHNzc3L69Gny9ttvExcXF5KYmKhXGwkhpLOzkz4/c3Nz8vHHH5NPP/2UvPvuu/Q9Qj7fCxcukOjoaBIREUGSk5N5n0sIIcbGxiQlJYUsWrSIfPXVV6RPnz4kPDycENI1N959913ezwDoVa2j1tZW4u/vT548eUJ+97vfEWNjY8IwDPnVr35F+vbtS9rb2+l7uZ/717/+lYwaNYo0NjaSWbNmEQcHB/LLX/6y1+xioVKpyPDhw0l1dTV5+vQp0Wg0BACRy+UkIiKCmJqakrfeeovcvn2bhIWFETc3NzJ27Fiq0WljY0NsbGx63S4ulEolMTMzI2vWrCHvvvsuSU5OJm+88QZpa2sj/v7+5L333iPz588nn376KdWw2rVrF+nbty8ZP348MTMzI4aGhjytx97CuXPnSHR0NAkNDSXp6enk7bffJp2dnaSjo4N8+OGH5E9/+hMBQAjpmlusjt5f/vIX0tbWRu7duyeofh+rYfnHP/6RVFdXE0II1U4dPHgwWbx4MRkxYgTp27cvSUlJIcbGxvTn2Pn4+9//Xuu13oJKpSKRkZHk6dOnpK2tjTQ2NpLS0lKSm5tLPvjgA9LR0UH+53/+h/j6+pJVq1YRFxcXMmXKFKp3yu4///u//9urdnXHG2+8QWQyGVm5ciX59ttvyW9+8xvy8ccf/6jOm0ajIb/73e/Itm3bSFtbm9b+1BtQq9XkjTfeIKdPnyZz5syhGqUajYYQ0rWvVFdXEx8fH+Lg4EA8PT3Ju+++S3x9fXvdlp5w4cIFepYRQsj+/ftJU1MT+eijj8jf//53IhKJiIWFBfnss8/IggULSGpqKlm+fDmJjo4mxsbGPWrTCYG3336b5ObmkvT0dDJ79mxy+PBhcuzYMZKbm0tkMpne7GDRp08f4ufnRx48eEDPMjs7O/p9dk9hGIacP3+enjUqlYrMnTuX7Nmzh+zcuZP88Y9/FMS+vn37kszMTDJr1ixy4sQJUllZSS5dukSOHz9Ojhw5Qurr6wkhhHz//ffEyclJS7MZgOBnyL8y/woKCkhMTAwxMjIS9A5DCKFnBKu12tTURB49ekTOnz9PVqxYQW7fvk3+9re/kUmTJpE+ffqQtWvXkr179xJ/f3/y7rvvkhkzZpAxY8ZoaXz3Njo6Oki/fv1Ie3s7aWlpIWq1mpw5c4YsXryY3Lp1i/zpT38i2dnZpLGxkXz33Xfkyy+/JMuWLSMff/wxWb9+PWlpaSH/93//J6iNPxu8NDe2l9HS0gIHBwcEBwfT3hE2IqZSqRAQEICEhAQolUqd9M7+/v4IDAxEa2uroGWZ3LKy7kyn586do9ILXK2bnqJQ+ip54MpnHDhwADKZDAMHDuRlVll0L/8T0sbuz4n9rNraWhpV1Cdr3KNHj2gUkVuO9N1336GgoADTp0/HqVOnoFarUVlZieHDh8PKyopHyiN0SfCrLJHDMlKGh4ejuLgYKpUKly5dwqJFi2BhYQFvb28cOnQI7e3tGDlyJGJjY19ar1BtbS3mzJlD2ZUBwNPTU6d+1YMHDzB58mTBdBpZlJWVwdTUFFOnTgXA1228cuUKBgwYgJ07d+osbayrqwPDMFrSOb0J7tzOysqiem9sGRz7fTZDmZCQoNc+dlZ+ITw8HOfPn0dVVRX27dsHhmGwYcMG3nsvXrwIU1NTQZ/nT0FeXh4YhsHixYvx4MGDF2YkVSoVNm7cyCMX6W2wz/DZs2cYO3YsHBwckJ2dzXv2d+/ehVgsxtixY3n7pD7aIZqbm+n63LdvHyQSCcLCwpCfn4/i4mJMnjwZvr6+MDc3B8MwMDExgUKhgFgsBsMwGDZsmJYeq77Q0tJCS+SnTJmiVzZPXbh27Rri4uJgZmaGY8eOaX2flQe5desWJWjRR0mhrvtSZ2cnnj9/juvXr+Prr79GYGAgnJyc8P333/PeIzT+0+bf2bNnIRKJIJPJ6F3Bw8MDS5Ys4VVeXLx4EUZGRjpLTYXGypUrwTAMgoKC4OnpCYZh4OLigoULF/L4EjZv3gwHBwdeK8dr9B7+a5zJsrIyWFhYIDg4mPYcAl0XYH9/fzAMA2dnZ0ilUlhZWSEuLg6zZ89GWVkZbty4gS1btug13X3q1CmIRCKtEj5W62vgwIE8h5Lbd/AyehC5n3nw4EHIZDI4ODjQjZF1IoWyra2tDXfv3sWBAwdw7tw5LXY/1pGsrKyklyvgH6xxEydO1MsGkpKSAjs7OxqwyM7OhlgsxqBBgyCVSmlZplKpxK1btzB8+HCtkld9Ska8KhI5XEbK7uyrbW1tOHnyJKytreHl5YWysjI0Njby+mCEhlqtxu3bt1FSUkL7aBoaGmgJev/+/REaGqrlSHLJgWbOnCmoht+4cePAMAy+/PJLnpN9/fp1SkSmUChgaWmJ6OhoTJ06FadPn8bt27dRXl6OpKQkQeZefX29Vjllfn4+JBIJrwySS0TBOpSJiYm4ePFir9vUHd3lF1hUVFTAysqK19/JIisrC5aWli+NWZbFF198QedgYGCgljQI8A+dN33Yy+4RPTmUEyZMQEpKit4DQe3t7QgMDKTBFgDYuXMnpFIp4uLieJf0x48fo6SkBBs3bsSwYcMQHh4OQ0NDWFpaCqJBp1arXxikY8e0qakJKSkptORViDLlnuw7fPgwDh06RHslAd0OJfucT506BYZhcOTIEUydOlVQR5ItTR85ciT8/f2xfv16HklM97P/xIkTCAkJgYODA4qLiwWxqTte5fnHQtcd6fvvv0dgYCCioqIwd+5cPHz4UKvFaseOHXB3d39prO55eXnw8/ODj48PZs6cyWsVYm3dsGED5HK53tbMzw3/Nc5ke3s7SkpKMHDgQISGhqKuro72L/n4+NA+qi+//JKyBLK11A4ODoL2t3RHZ2cn6urqEBUVhYyMDC2R1zNnziAqKgp2dnZ6FYL/MehyKAcNGkQvX0I5ayqVCklJSVQDz8zMDLGxsVraWrdv34atrS1GjRpF+23a2toQEBAAT09PQZ0PduNqaWmhzGJFRUVwc3PDd999hwcPHqCzsxOzZ8+GSCSi2UhW1J7rUG7durVXo4+vukSOSqWijiSXobN7YKKkpAQWFhZIS0sTxI6ewDJDDxw4EAzDwNXVFVu3boVarUZdXR2+/PJLmJqaYvbs2TptZ/8tdJCgo6MDqampkEql+PLLLwF0OUNmZmZITEzE4sWLsXz5ciQmJsLZ2ZnufwzDYOPGjYLYefXqVUilUnh5eSEjIwNLlizBkSNHsGXLFkilUq3LJfezWZbtnJycXrNHF3QR2bCX91u3bmnpl7Lf++6776j8gdBVIi0tLdi1axeys7ORk5ODr7/+mrf/sVqhU6dO1TpPhNZ506X/yO4rdXV1GDNmDBwcHDB79mx69ukzEMRCrVbDy8sL06ZN471eWFgIKysrxMbGvlBq68aNG4IwCKtUKgQFBdEKh+7gShuo1Wqo1WraQ1lYWCj4WCqVSkRERNCAlKmpKS/4WV5erjNDWVdXhwEDBsDR0RGmpqaCVRkolUr4+PggMDAQERERCA8PR2xsLG7fvq31Xu55d+zYMUREREAsFuvs/extvKrzD9CuIOv+dVtbG29v5q73hoYGjBkzBsOGDRPUUeu+x3R2dvJs6t7DzH3WdXV1SElJwahRo3j9n6/Re/ivcSaBrslVUlICqVQKhUKhJYrMoqOjA/X19Thx4gSWLVv20spWVq1aBWNjY2zbtk3rAnf27FnExsaCYRit7OXLRHeH0t3dHSKRSKdOWG9ApVLB1dUVERER2LlzJ+rq6lBUVIQDBw7Q97S3t6O5uRnOzs5ITEykGwpra3l5uc6DpTfAjSazEbDDhw9DJpPB2dkZ8+bN09q80tLS4OzsTDNc169fR3JyMgYOHAgPDw9YW1v3GnnMf4JEzsaNG8EwDPLy8l640be3t9NL85EjR/RiG1t6GxkZia1bt2LHjh3w9/eHRCKh67K6uhrz5s3jZcT1DXb/6OjoQHJyMuzs7KgI8/Tp03mHbGdnJ1pbW1FWVoaCggJMmjRJEEe3s7MTxcXFGD16NIYPHw4HBweIRCKqJckwDIYMGYKxY8di9erVKC0txe3bt3nOyYULFwR1wtvb2+Hl5QWRSKTlaKnVagwZMgQmJibYs2cPvv76a5SVlfGqIhQKBXx9fQW9oCiVSgQFBcHZ2Rn29vY0qCGXy7F+/Xo6VqyMxIIFC+ils6mpSVCdt6amJkyYMAE5OTk4fvw4ysrK0NLSwntPXV0dMjIyIJVKMWvWLEHJk3oC+1kZGRkICwsDAN4zKywshLW1NeLi4niSEKx0jlBQKpXw8PCgRHHdHfPuFTdsuXVLSwuVGdq9e7dgNiqVSjg7OyM0NBTbtm3Dnj17EBgYCKlUyqvk0uVQ1tXVwczMDAzD4IcffhDEvubmZvj4+CAqKgo3b96kz7QnbeHu43v48GHExcUJRgbE4lWdf0DXHcvX1xezZs3C6dOntfay7my83Od+69YtTJo0CQMGDBDsGQNd8zA+Ph6bN2/mZcaBf4wjdz/hJofu3r2LSZMmQSKRCEqq9HPHf5UzCfzDoXRzcwPDMDzKYu6FS5/ofmhyF2Z6ejosLS3x/fffay3iEydOYObMmXq398fAtX/37t0YNWqUIDaq1WokJCQgJiYG9+/fp+PI/ax169ZhxYoVePr0Kc6dO6fXiHdNTQ2kUqlWpFGpVNIeOa4UA/t8i4qK0L9/f14Q4/r16zRj3lsZwP8EiRygK+KenZ0NIyMjrF69+oV07NeuXYOpqalWD5sQYHvoIiMjeaVF9+/fh7OzM4YNG0Zfe/ToEebOnftSHUquZhp70fTz8+PJRrA9irogdOZUqVSipqYGJ0+exJo1a2hvi5eXFy9L6uLigsGDB2sFjIRCQUEBjIyMMH36dBrEaWtrg1wuh5GREezs7HisgP3790daWhqys7Oxa9cuqkMoBLjBDPYse/r0KUpKSmBvbw+xWIzc3Fy6VrkOZVVVFSZPnixoeWFBQQEdFwsLC5iZmUEmkyEuLg55eXk4fPgwHj16BJVKhbFjx8Ld3R3Z2dl6dSi5n7Fw4ULY2NjobMtgL/RDhw7lXeiFdNQcHR2RmJiIjIwMeHt7877Pfu7du3chlUqRlpbGY7htbm7G6NGjBQuUcvc/bun3pk2bIBKJtNqCuA7l0aNHAXTJRwh5gS8oKMCQIUN0SldUV1dj3759KC4u5s3/7g6l0GWPr+r8Y7F9+3ban8n2XB8/flxnQHvnzp1gGAapqamIjY2lQS6hW8Rmz55N2zRsbGyQmZnZY3nyunXrYG5ujqysLKSmpiI4OBgODg6CVVa9Rhf+65xJoOtSVVJSAjs7OwwZMkTQw74n3Lx5E1u3bqVfdz802a+fPn2KoUOHwsLCAlu2bNHKonIFqF8l6Orf7G0bz549Cx8fH16TPPczvvrqK3qRWbVqld5Lp2pra2FlZQWGYTBr1ize92pqarBhwwaaseUedBs3boRMJtMq0y0vL++1fohXXSIH4D/L1tZWTJs2DYaGhli9erVWYIU7z2xtbTF//nxBbWNLb+Pi4nT2cA4bNgxBQUE8O1mHUiQSYd68eYLa1xO4DmVqaipsbGywcOFCrWx993/3Jurq6nDjxg3cvXuXd/ntvj+EhoYiKSkJTU1NuHz5Mg4dOoTly5dj+PDhSEhI0Ouexzq3OTk5uHfvHr1UXb16FbW1tXj8+DGKioqQn5+P8PBw2lMsZEaD20d8//59LZmNR48eISAgAGKxmBe0WrRoEUQiEZycnATTeVOr1VAqlbh//z7mz58PGxsb+Pr6ori4GGPHjoVcLqfVEIaGhlAoFAgJCYFYLIZUKsXUqVMFvyTrmj979uyBmZkZb1/m7nPshT4xMfGFJYf/LtiMX3h4OJ49e4b9+/dDIpFoyQU1NDTAzs4OSUlJeu1HYx3JiIgI6kiy47l//364ublRJ7F71mrYsGFgGEYnKU9vIy0tDUOHDuW9xspCODg40PuBk5OTVsvQz3n+cXHr1i3ExcVh6dKlWL9+PUJCQijpTkFBAS/offnyZeTk5MDT0xMhISH44osv9NIzXlFRAU9PT0yePBlbt27FwIEDYW1tjZCQEBQVFdE7TmdnJ65evYrk5GS4uLjAz8+P7umvISz+65xJ7gJmS15DQkKo6LDQ6OzsRHNzM2JjY2Ftbf1Ch5IFW3NuYmKC9PR0nDhxQi+2Aro31J+6yXIdJCGY5ZYsWYKBAwfqPETZCxPb08cwDFasWNHrNvQENsMTGRlJBay7ZyjZcayoqMCVK1fQ0tKCq1evIjg4GMnJyYJGvJ2cnBAVFaV1Edi2bRvc3NxQV1enFRDQaDSQyWQYP368IHaxaGpqwrx585CQkIARI0Zg69attCdj+vTpPTqUQBfj8eDBg3kBht5Ge3s7zexxe+i48z00NBRxcXFaZUiPHj3CtGnTYGlpKUjp90+ZM1xyhJEjR8LW1pZHyiNkkKCsrAx+fn4Qi8UwMjLC6NGjtdoI2HGMiopCeHj4C3+fPh3KVatW0Z6wgIAAnUQ2QNdeV19fLygRhkqlgpOTEwIDA3X283MdSicnJ7i7u/MudV988QVMTEwEyRio1Wr4+PjQzHFVVRUlolq2bBl9X0VFBc1CDxs2DEOGDKGXe4lEotXb2ZtoampCcHAwwsLCkJOTg8LCQpSVleHs2bMwMjLSOmO5a5vNvqSkpGiV7PaWbQMGDEB0dDQNKFZUVIBhGOzZs4f33pqaGnz77bd6dSRbW1shl8sp27JGo6Hj09zcjMDAQISHh6O2tpZyU3Bx48YNjBgxQrCMZEdHB9RqNd2nExMT0dDQgMePH2Pfvn2Qy+VgGAZubm4oLCzEhg0bEBoaiqioKKhUKr0QGL7K808XUlJSEBAQQO9x27Ztw5QpU8AwDGQyGcaMGYMffviBfp995voig6yvr8fIkSMpSVxtbS1Wr16NyMhIMAwDe3t7rFmzhteiU19fT+fJawiP/3idSfx/fSBCujS03njjDXL37l1SXl5OPDw8yLx588i4ceNIZmYmmTZtGvnzn/8sqD19+vQh77zzDklJSSFffvklWblyJdFoNCQkJIT07duXp4PI4v333yfz5s0jZmZm5MiRIyQpKYl4eHgQIyMjMmjQIPLb3/5WEE0/Vv8RXUEF0tHRQd566y06ni8COPqHO3bsIE+fPiUxMTG9qp3X0NBAfvGLX5B33nmH9/qJEyfI119/TebOnUvc3d1JW1sbqaqqImvWrCHu7u560Q3q06cP6devH/njH/9IAJC///3vZM+ePVTjipCu+ahWq8mSJUtIUVERee+998j7779PfvWrX5EvvviC9OnTR+d8+HfQ2tpK5HI5+fjjj8ncuXPJRx99RNdFU1MT2bJlC/nss8/IW2+9RR4/fkw+/vhj+rz79u1L3nvvPXL79m2iVqt/8lz4Z6BSqUhwcDDp27cvef/990llZSUpLi4m1dXVJC0tjYwaNYr06dOHzJ07lxBCSGhoKPnFL35BCCGksbGRbN++nbzzzjvEyMioV+3ior29nZiYmJDz58+T9PR0sn79ep5O3hdffEGuXr1Ktm3bRtcPi48++ogMHz6cJCcnk9/+9re9blv359FdaxEAefPNN8m5c+fIRx99RHJzc8nIkSPJtm3bSL9+/UhUVBT51a9+1et2EdKlcRkfH0/s7OyIq6srqaurI+vWrSN///vfyd///netv8HY2Jjs2bOHPH78mHz44Yda+yOAXtenbW1tJXv37iVXr14lAMhnn31G3NzcyMcff0z3rxkzZpC//OUv9HmztrC269qTehvl5eXkwYMHhGEY8uTJE/I///M/1A5279FoNOSjjz4ic+bMIeHh4eT7778nUVFRhBBC0tLSSHR0NP253kRzczOprq4mz549I4QQ8oc//IFERESQzs5O8sUXX5Bnz56RCRMmkL/85S/kL3/5C7G1tSVRUVEEAKmoqCANDQ3ko48+ElSD7vr16+Ttt98mHR0dZMeOHaSxsZEQQshbb71F+vbtS1asWEFOnTpF/vjHPxJjY2Py/vvvU31VPz8/8utf/5r8+c9/Jm+//Xav23br1i3i4uJCRo4cST766CNCSNcYisVicuDAASKTyUi/fv1Iv379yO9+9zvi5ubW6za8CFeuXCF9+vQhtbW1pLy8nO61bW1tJDw8nFy7do188MEHxM3NjbS2tpLPPvuMSKVSYmFhQT7//HNibGxMvvjiC0G0pZVKJRkxYgQZO3YsMTY2Jk5OTiQjI4PExsaSpqYmUllZST7//HMyYsQIEhERQd5//31CSNcda+rUqUSlUglyl+qOV3n+ccGeH+np6UShUJAVK1aQ1NRUEhgYSP7yl7+Q7du3k1/+8pfkyJEj5MCBA8TMzIz4+PgQOzs7vWitgqMF7+HhQVJTU8nx48eJo6MjiY6OJgMHDiQ+Pj7k3XffJXPmzCHvvfcekclkxMXFhZibmwui5fwaPeCluLD/AlpaWrB582ZkZWUhIyMD27Zt40XY2chOVVUVTExMMGzYMNojdOrUKRgbGyMuLk5LB1FIXL58GeHh4ZDJZNi8eTN9vaeSV6CLTv3s2bOYOXMmoqKikJKSosXA2RvganBOnz4dERERiImJQXZ2Nurr61+YveBGo9atWweGYXgyJv8u2N+/YsUKiMViLWmA5uZmrfr30NBQRERE9JoNPwZ2fPLz85Geno6mpiZMmTIFVlZWmDt3LoCusVGpVLhz5w6WLl2KxYsXo7CwkI69EBGzV1kih9uDw/ZjNDY2QqFQwNramlYP1NfXY+bMmbwMZXNzM2Wk1IeED6vJN2DAAISGhtLXFy5cCJFIRLMy+iQRqaqqwu7du5Gbm4u8vDytagB23ezZswcMw+DgwYPURjbTyq2U6E2UlpZCJBJh1qxZvNLWwMBAhIaG6tx3t27dCiMjI8HJL1golUoEBARg8ODBGDx4MJycnOh6yM/Pp1kANkM5ffp0wTRVe0JTUxOVIeH2J924cYO+pztBy/Pnz+Hu7o4JEyboJVPQ1tYGOzs7rVLzhw8fIjs7GwzDYM6cObz36wvdGR6BLpmFiooKFBUVIS8vD2KxGBKJBFKplGZKTU1N4efnB4VCgU2bNglim0qloqRdusYkNzcX5ubmVNJC31p43AzjyZMnERAQAHNzc1oi6OHhgcDAQOzcuRMnTpzAli1bMG7cOAQGBtJxlEqlPWb0/12w/cP+/v68z9i8eTM8PT0REBCA7OxsniwEOxe2bNkCuVz+wn783sCrPP+Arv1FV6+hUqnEiBEjoFAoAHSVK1taWiItLQ2VlZWorq7GtGnTIJFIIJFIBGOVBbrud+fPn9eSH+ns7ERsbCwlMCovL4dEIkFycjIePHiA06dPIzk5mc5DIW18DW38RziTSqUS/v7+kMvl8PHxgY2NDUaOHEk3OfYAraysRP/+/XWKIZ89e1Yvlxa2Bp616ac6lD1pSAopWdLU1ARXV1f4+/tj3LhxGDduHKRSKTw9PXHy5MkfLYFdu3YtDA0Ne+2C2v3z7t69C2NjY0yZMoW+pusQPnPmDDw9PakdQlyoLl26hMOHD2u9fuHCBQwaNAhNTU2oqqrCrFmzYG1tDTMzM4SHh/d4eAlFqvSqSuToKr3llgEzDIOdO3fS9zc0NFCHcvny5YIyUrK2tLS04NmzZ3TvUCqV1KGMiYlBXl4ejI2NsXv3bkFseBHOnz8PmUwGuVwOS0tLmJqaYuHChVolZrt374apqSmWLl3KK0/r6OhAbm6uIPOOdSRnzpxJHVyNRoO2tjbEx8cjMzOTRzjB2nDmzBkwDEPJOoQEl8iGDU41NjairKyMMlLPmDFDy6GcNm2aoOWs3ZGbmwuGYWhZ444dO37UoQQAX19fpKenC24fO9/Cw8Pp53HPsp4cSn2QyHHL5ydMmNCjrFZ8fDzCwsLw6NEjquWXk5ODIUOGQC6Xa/Ut9gbY/teRI0f22A9eXV2NgQMHYubMmXrXklYqlbC0tOSd5SdPnoSfnx/69+8PR0dHhIeH62RJ7ezsRFlZGfbt2yfYHYst++bK93DR1NTEO2u58+3Zs2cYNWoUUlJSBHUmX+X5x4JtCeIG/9m5VlJSAkNDQyxZsgRWVlZITk7mES8BXQ5c99d6GyNHjoRYLMaxY8e07nsFBQWwt7fH1q1bIZFIkJSUhMePH9O/QalU4vbt273Ghv8aPx2vvDPZ1NQEuVyOiIgIepFsbm7Wyk60t7dj+PDhSE1N1bsYck1NzQvlRS5evIjw8HAMHjz4hQ4lF9xojBDo7OzE3LlzERAQwGODy8vLA8MwOHXqFO9i2N2WtWvXwsDAoFccSfb3Pnz4kCc0DIBeTFhSmO6or6/HuHHj4O3tLdgmV1paCiMjI+Tl5Wk9j4qKCkgkEly6dAlAV/ZIIpHAxMQEGRkZ9H36rNt/1SRyWlpa4ODggODgYB7pEBt0KS8vh7m5Ob755hst/SqWxU0kEgnGxtbU1IQZM2YgJCQEtra2UCgU9FLU3NyMjRs3UqebvSDok2G5rKwMYrEY06dPR3l5OdRqNS5evKh1cbt9+zZlk+XuLd3nXm/aXlZWBlNTU2RlZQEA7bECujQmBwwYgC1btuikb1cqlWAYBmvXru01e3SBS2TD6r0C/OqMyMhIGBsbY+nSpXS81qxZA5FIhPHjxwt+gWJx7do12NjYYMGCBXSsCgsLdTqULG7cuAFvb2/e2SI0srKyIJPJdJKecR1KoYmyWCiVSsjlctjb28PT0xMikQg2Nja8QAV7MZ00aRLc3Ny0fkdLS4sgJG49saJ2h1qtRnp6OuRyOe1/1YdTybJ+h4WFoba2lveZJ0+eRHR0NBiGoRktVipCXw4vK11hY2PD6wvm2sDdV7jO5u3bt6kshJBO2qs8/7i4desWxowZAyMjIxoUZcdRrVYjPj6e9mty7wv6vL/cv38ffn5+cHZ2xtGjR3mf3dHRQXti09LSeLwE+g7AvAYfr7wzuXDhQigUCso+yZ0wjY2NKCkpQWlpKTQaDaqrq/XuSFZWVsLMzAyGhoYICAhAfHw81q1bh+LiYtTV1dEN5M6dOwgPD4enpyctYwL0X8rCRXx8PBISEujXe/bsgaGhIdasWYPi4mJeZLl7aWtvOZJNTU3Izc1FWFgYxGIxrKysEBYWht27d+P58+eora1FYmIiZVrkEimdOnUKGRkZGDBggGClj9zyPS6FOPe5KRQKHD58GB0dHfD09ISfnx/N8gpNZtMTXiWJnEuXLqF///4ICQnReUnKzs6GRCLRWR5VV1eHpUuXCqZhpVQq4e7ujuDgYMycORPTp0/H2LFjcfr0aWqjSqXC+vXrIZVKaYkNoJ8Dtr6+HpGRkZg8eTIaGhp4806tVqOxsRGnTp2iguY//PCDXh1dVgJnzpw5vKz2tWvXKJunv78/xGIxQkNDkZGRgaNHj6K8vBw3b95EamqqoOPY1NQER0dH+Pv7awWqgH88Q6VSCS8vLzg6OvKIQ/Lz82Fpaam3kqnW1lbExMTA39+fd7HsyaF8/vw5Jk6cCBcXFx5rc29BqVQiPT0dixYtwq5du3D+/Hm0tLQgLy8PMpmMl+nhPsf79+9TUp6FCxf2ul3dbWSdNfYcqKiogIGBAXJzc7Xev3nzZpiamvKcCyHJ0FxcXLTkhbqD/fyqqir079+fV40j5B2By/rNBvq6j0VxcTGCgoJgbm5OA3r62mPYZyuRSCASibBkyZIXZhc3bdoEhmEwadIkDBs2DAqFAoMGDRJUFuJVnn+6cPfuXYwaNQqGhobYtWsX73ubNm2CkZERzVy+LAetpqaG7sdHjx7lEf7k5+dDKpX2mPl9jZeDV96ZTEhIwMSJE3mvqdVqzJw5EzKZjJboeXt70wNfnwvg5s2bVFvJ1NQUSUlJVJTb3NwccrkcmZmZOHjwIPLz8+Hj4wOZTIZt27bpzUaAPyYajQbNzc2IiIigWnm7d+/msfFt2LABFhYWuHv3Lu9n169f32uOJHuBCwkJQVZWFvbt24e5c+fCz88PDMMgPT0dT548wcOHD5GZmQlDQ0NIpVJa8iyXyxESEiJYRq20tBSmpqaYNWvWC4MUI0aMwOjRo+Hn54egoCAolUo8evQI6enpiI+Pf2kBg1dBIgfoWq8nT56Era0twsPDebIkeXl5EIlEOH78OLW5O4Qav9bWVigUCkRGRvLGhlsiz16QVSoVNm3aBHNzc55DKfSlqqamBs7Ozti/fz/v9fv372Py5Ml0Dxw0aBDy8/N5/Yr6wvjx42FjY0ODT7dv34aZmRmSkpKQn5+P9evXIzU1lYqzs/+tXr2a7i1COZSXL18GwzCIiorC5cuX6eu6hOHLy8shEomwaNEi3u8QomddF9h5fvHiRRgbG/OCjgDfobx58yba2tqQlZUFMzMzwYJpCxYsgI2NDfr37w9DQ0MYGRnB3t4e7u7uYBgGX3zxBYqLi1FVVUWzBOzYPn78GAsWLBA8I8Q6Q9ysX3t7O+RyOc2Mcp/3oUOHwDAMrSYRChqNBiEhIejfvz9aWlro8y0rK8OaNWuQm5uL3bt3a5W9btiwASYmJj1W4/QWuE4Q60hypci4WofcHkq2Qkzova+xsREuLi6Ijo7GnTt3kJubCwMDAyxevLhH9vhz584hKysLLi4uCAwMxLx58wTXgX1V59+L8PTpU6SlpUEkEvHOls7OTgQEBNDeSX1B152drUr09/dHcXExXSeVlZWwsLCg7PmvM5KvBl5pZ7KtrQ0xMTEYMWIEgK7N5ciRI/D29gbDMHB1dcXatWvx1Vdf0U1HX7hy5QotEygtLUVaWhoMDQ2xb98+qFQqnD59GgsWLEBSUhIGDhxIm4LZkj0DA4MeRVd7G+xFTaPRUDIToOvQYhgGs2fPhpGREZYsWUJ/Ji8vDx4eHrzo+LZt28AwTK84wuxFPjo6GhUVFVqXyaysLBgYGCA1NRX19fXo7OzEtWvXkJ2djYyMDEyfPh3Hjh0TjF5epVLB2toacrmc9/o333yD7OxsjBkzBuvXr4darcbatWvBMAxiYmJ40ee6ujq9inNz8bIlcnTh+PHj1KFUqVTUkXxZEcaioiJ4eXmhrKyMV54JdK3p5cuXY+rUqSgqKkJbWxvUajU2btwIa2treHl56cXG0tJSMAxDnYUHDx5QLTKGYRAYGIicnBwMGzYMBgYGOHXqFAD9HLDcOTZ69GhIpVJkZGTAzMwM06dP1wrAdHR04Nq1a9i4cSOmTp0qaEZSpVLhyJEjAIDvv/8eDMMgLi4OZWVl9D3dHcqmpib4+fkhLS1Nr+u1+2fV19cjMTER0dHReP78Oe/SzjqUKSkpSElJEbSPGPhHad69e/dQVlaGrVu3YurUqZgwYQIvMMCeb7GxscjKysJXX30lOFGWSqXC4MGDER8fTy/y7FhWV1fD3t5e595SU1MDAwMDbNiwQVD7AODrr7+GsbExteO7776DmZkZnJyc6H1gxIgRvH25pqYGSUlJcHJywvPnzwVZy2wPcXR0tNbYqdVqeHl5YcqUKTxpCtahlEgkvHUkBDo7OxEdHQ0/Pz+aJFCpVD06lN3HiK1CENLhfdXnX3t7u5bTza7njo4OuLm5wdTUlJeh1Gg0WLFiBfr3749vv/1WUPtYO1jo0lRng6UuLi4oLi6mWekvvvgCxsbGvIqr13i5eOWcSbVaTcvd2L4+Nhvg5+cHAwMDODo6Yv78+byypYKCAnh5efGyCkKBjTqNHDmSvlZWVoaEhAQei2L3puB9+/Zh5syZSExMxJAhQ/RSKsLtCxo1ahQt+9i2bRsqKysxdOhQMAxDe56ArsxCYGAgxo4dy9ukL126pKWD9a9i79698PHxwblz53h9D9yL1bRp08AwjF57gbhYt24dTE1NUVRUBACYO3cuTE1N4ePjQ+v2R40ahcOHD2PevHm8pu/umWChoSvTcufOHXqYsaQ84eHhvB5ZIdDa2oqjR49i2bJlmD9/Po4fP06jiidOnIC1tTVsbW1hYmLyUktV2FI9Fh0dHairq8PIkSNhYmLCuyiz5UoqlQqrVq2Co6OjIGWF3aFWqxEYGIhBgwZhxowZCAgIgEgkgqenp1bmQqFQIDMzE4D+orXcPWzs2LFgGAY+Pj6UBZXti+1prxPCodRoNEhISIC3tzd97ciRI2AYBkOHDu0xQwl0jWFycnKv29QdLS0tOHv2LO817j6xa9cuMAyDc+fOAeCPE/s9IyMjQRw29plxM7Ldx+nevXtwc3NDXl4ejh49ilWrViEjIwNyuRxisRgikUgwnUHWnmHDhvHOW3aOqdVqhIaG0l77nJwcbN++HWVlZTQ4amxsjAULFghmH4unT59i/Pjx8Pb2RmFhITw8PLBo0SJUVVXh8ePH2LVrF/r374/Jkyfzfu7GjRuCET+1t7cjLi4ODMNo6RCr1WrI5XIEBARoOUhA1zkyePBgDBo0CGq1WpB9hn2O1dXVWq0PSqXyJzmUPREa9hZe9fmnVquhUCiwdu1a+pnsM25tbYWnpycUCgVKS0sxevRoGBoa4ptvvgHQFcxiGAZjx44V9I7a0tKCESNG8Jxq9p7AkgaGhISgrKwM3t7ecHJyQnFxMSV8YhgG2dnZem3reI2e8UrpTLa1tZGgoCBiYmJCkpKSyKeffkpGjhxJGhsbyeXLl0lLSwsJDg4mERER5A9/+AP5xS9+QbUS1Wo11VgTGv369SOffvopaWtro6+ZmpqSlJQUQgihGpOsPtQvfvEL8uc//5n8+c9/Jh4eHrzf1V0nTghb2XH7zW9+Q6ytrcnTp0/Jb37zG/L555+ToUOHkr59+5KtW7eSvn37EqVSSe7evUs0Gg3Jzs7maSGamZkRMzOzXrHr7NmzRKPREJFIxNM5JOQfYzJ58mRy48YNsnXrVhISEkII4euKcv8tBGQyGbl06RJZvnw5uXPnDjl27BjJyckhAwcOJP369SNHjx4lGRkZ5PPPPydjxozh/SzXrt7Wkfzmm2/I9evXSXt7O7GwsCD+/v5aY1ddXU18fHyIVColbm5uxNramsybN4/Ex8eT2bNnkyVLlgiyVlQqFUlMTCTPnz8njx8/Jv369SOnTp0is2bNIgzDEBsbGzJ//nwya9Ys8stf/pKYmJj0ug0/FR9//DGpqakhV69eJb/5zW/IkSNHyPr168m9e/eIhYUFGT16NPnlL39JiouLyaJFi4iHhwdhGIaEhoaSgIAA8utf/1pwG998802SkJBA1q1bR77++mvy3nvvkcTERCKTyQjDMISQrnXQ2tpK2tvbqa6gkOuCC1bvsF+/fmTu3Lmkb9++pLi4mGzcuJHEx8eT999/n6dJ233NCqFFx+qldnR00NcGDRpEli1bRoYNG0YIIWTkyJHExMSEZ0tFRQVpa2sjEomk123iQqPRkCFDhpDr168Tb29vEhwcTIyNjcnbb79N91q5XE62b99O8vPzyZdffkneffddOnbe3t7k3XffJZ9//jn529/+1qu2NTc3k5ycHPLDDz+QtrY2EhkZSXx9fUmfPn14WpufffYZeffdd8nNmzfJiBEjiL29Pf0dLS0tpKWlRRCdVRZ9+vQhI0aMINXV1aSgoID8+te/JlZWVqStrY34+/uThw8fEhMTE7Jjxw5SXV1NOjs7SZ8+fcjvf/978sknn5D33nuPyOVywexj7yUffPAB8fb2Jrdu3SKbN28mf/nLX0hERARdp97e3qShoYHMmzePBAQEEFNTU0IIoWtbCLS0tBAbGxty4cIFMm/ePJKdnU3eeustolarSUBAAPnlL39JFi5cSHUE+/btS+eejY0NycnJIR999JEgGn5tbW0kODiYhIWFkaCgIPo6+/nvvfceiY+PJ4QQsnjxYkIIIbGxseSdd97hrWX230Ltg6/6/Ovbty/5xS9+QRYtWkTeeecdIpPJyPvvv0/a2tq0nvH//u//EgBkwoQJBADx9fUlGRkZxM7OTtC76ZMnT8iFCxdIZWUleffdd4mfnx956623SEtLCwkKCiLvvfceyc3NJZ988glZvnw5iY+PJ9OmTSMzZ84ktra2JCYmhvj7+wtq42v8E3g5PmzPyMrKgqGhIXJycihZR0dHB+1D04WnT59ixIgRmDRpkt5Yp3JycjB48GA0NTXxIiOXL1+mGUpWi45ly+pJDkRo5Ofnw9PTUyddMkuPv3btWoSEhCAxMRHz58+n49jb48mOQVxcHIYPHw5A9ziwY8oyKnL77PSJ4uJi+Pj4wMPDA5mZmVrN/0uWLIGlpaWgUXgWr7pETlNTE7y8vBATE4PTp0/j+fPnePbsmVYZXltbG06cOAEbGxuEh4fzGPqEBle25+rVq7Q/l81E+vn5Yd26dbzew4cPH8LGxoaWTQoFpVKJ2tpaFBcX4+rVq7w5397ejnv37vWoe3j16lUEBATQcqXe3Ft+SpadS+E+fvx4WFtbY+7cuZSUR19lo6yt33zzDYyNjVFZWYn29nb6+dwMJbdUjyWycXJyEpRWnt1PS0pKkJOTAxsbG0gkEsTGxuLy5cu89bp8+XI4ODjQvYU7d4WAUqmEp6cn3N3dqZwVl8GYBXuWscyj3f82faK8vBxubm4YMmQITp48CW9vbwQHB+POnTtU7ufBgwfYtm0b5s6dCzc3N7i5uQmS0W1tbUV2djZ9Rtw1UVBQAIZhMHr0aPoa+77r16/DzMxM8LaXlpYWutc+f/4cGzdupKzjLS0tkMvlCA4O1pkRVSqVWL9+vaAl1UBXVVRYWBiMjIx4VVDd5333DCW3HFefeJXmH9CV0WPlW9RqNUaOHAkzMzPs2bMHdXV1PT7ju3fv0sqSoqIiQfcZtVpN+6vv3LkDLy8vuLu7Y//+/bTqT5eNNTU18PPzg1gs5hHkvcargVfGmeReNubMmUN7+XQd7FzWwKqqKkyaNAnW1tZ6udCzB+by5cshkUjopZM7sXtyKF8WMjIyEBQURB001pa6ujrMnDkTX3zxBQDw2EoBYfoN2Oc8ZcoUODs79+hIsO87d+4cDAwMcOPGDb2OIfezlixZAoZhsHTpUq3vFxcXQyQS4erVq4La86pL5HR2dmLBggUYMmQIKioqdDoP3S+bx48fh42NDaKiovQmXN8dZ86cQV5eHqZMmYLVq1dDpVLRec/+DUePHoVMJhOMURbocgaTkpJgb28PU1NTGBgYwNLSEl988YXOfY0t72pra8OtW7cQEhKC0NBQQdZsa2srlQPQBfb1M2fOUFtHjx4NW1tbLFiwgEffri+cO3cODMPgzJkz1EbWTtahjIuLw+XLl6FWqymRjZCsj2q1Gv7+/li6dCl9Tk+fPsXs2bPh5eUFAwMDDB8+nPbisyWHXAdEKHDlU1i22Fu3bsHFxQWJiYk6n/+qVatgZmaGmpqal3q+sRd6sVgMd3d3ujZ02fT06VPBCJUuXbpEmUTZz+aS62zZskXnHnL+/HkMGjRIq+y5N9HS0gILCwtIpVJKiPT8+XNs2LABpqamMDIygkKh0BmwV6lUmDRpEhiG0Uvg78aNG7SE9Kc4lMbGxpg/f/5Ldyhf9vxjCZU2b97MK7lNSUmBmZkZpFIpgoODeTwTXBsrKiowadIkQe/RSqUSgYGB2LJlCw223L59G3K5HO7u7rC3t4dCoejxzHjw4AFCQ0NfKvfDa+jGK+NMdu+rYWnFZ8+ezYtQLF++HK6urpg9ezYyMzMxZMgQDBw4UNBLwMOHD/H8+XPeZnXmzBlYWVnhwoUL1H5uJPLy5ctITEyESCTSol/WNzIyMuDo6Ei/5o7z6NGjIZPJ0NHRwXu9tzMb3cVnWSKJH2tCX7p0KWQymRbjnT7AdYi+/fZbnRvcvn374OrqKngg41WXyAGAsLAwzJgx44UOTVtbG9avX0/H6+TJkzAyMkJiYqLWHOkttLa2oqioCDNmzEBKSgrmzJmDwsJCLbt0oaGhAePHj0dYWJhgl4Dz589DIpEgPT0dmzdvRmVlJQoLCzFmzBiIRCLExsbS3jmgaz2PGDGC/k3+/v7w8/PjkSv0Fq5fv47k5GTKnMza0b0nae/evWAYhkfaMG7cODAMo8VKKiTYv/3JkydwdHTEqlWreK93dyiHDh2KpKQkwYlsWAwbNgxmZmZYt24d7fln2bXXrVuHyMhIGBkZIS4uDoWFhfj6668RHh4uKJlNd1ZPrg5nREQE1cvtfiawDOBCsWn/M7hx4wZkMhn8/Px4pBz61EO8c+cOjI2NIRKJkJaWptOhBLqYmCsqKlBfX4/79+9j/PjxkMlkOqWReguVlZWUZd7V1ZU+s+fPn2PTpk2QSqUYNmyY1lipVCpkZWVBLBYLFjBtb29HbW0tTp48iRs3bkCpVKKqqgrDhw//SQ7lrFmzIJFIXkrQisXLnn+sBE1ISIhWQECtViMjIwMMw2D58uVaSQMuhDqDWRudnJwwZMgQ6tCyd6zbt2/D29sbIpGI7tmA7rPsdY/kq4lXxplkwd14WcHy2bNn0/Kus2fPws/PDw4ODvD09MTUqVMFzWqUl5eDYRgYGhrC0dERKSkpWLx4MfLz83WWAHFRVlaGwMBAhISEABA+O8lmfjo7O3lZoKNHj8LS0pLX5M8u4jlz5iAuLk4wm5qbm7FgwQIkJiYiKyuLktkAwPDhw9G/f38cO3aMvsYdo7q6OqSkpGDChAmCNPv/lCZ97pgCXUGCS5cuobKykj7f2NhYwcv4XmWJHI1GgwcPHsDMzIw+3542/KNHj/IuCO3t7Th9+rRgZcxKpRLh4eHw9vaGi4sLYmJiqP7h0KFDcfXqVZ6tp06donvNpUuXkJGRAQsLC51i8b2Be/fuYfDgwZgxY4aWDqJSqURhYSGMjY0xdOhQVFdXo62tDSkpKbC3twfDMJDL5Zg2bZogZemlpaWwtLREaGgoIiMj4ejoCBsbGy2q/V27dsHU1BTLli2DRqPhjWdeXp7gjIorV67EiRMncP/+fd5FKT4+HomJiVo/w60qYNeN0Myj3M8eP348jI2NsW7dOl6VDdDlBJeUlCA0NBTe3t4Qi8VgGAbbt28XxB6VSgUnJyfExcVpEa7U1tZCJpNhzZo1On/2ypUrYBgGO3bsEMS2fxZshkihUPCyfPraB9nxSEpKgoODA0aNGsVzzIGuDOHq1athamqKQYMGwcfHB/b29oLPv5aWFmRmZkIul8PDwwMDBw6ke1p9fT2VI0lPT6fPX6lUIisrS9BAi0qlwsiRI+Hq6krXYlZWFtra2nDt2jUkJSX9qEOpUqleqiPJ4mXNP11aod3vI83NzUhLS4NYLMbWrVv1HmzmVj501zNlbb179y6dnzt37qQ/+zK12F/jp+OlOZNNTU2Ii4vD9OnTsWnTJpw6dQpNTU1akZG5c+dSh5KtBW9ra0NNTY3O9/c27t69i8LCQnz11VfIyMiAu7s7jIyMKK23gYEBwsPDkZSUhLy8PBw6dIj24qjVatTW1uplMXBZW+fMmYOUlBRs2LABDx48oGVcAwcOpA5lW1sb7t27h6CgIB6Ta29CqVRCLpdj0KBBcHd3h5mZGezt7elGe+bMGfj5+cHc3ByFhYU8naaKigpMnjwZNjY2gmX92P7H7vTZ3Z3MEydOoKioCA0NDViwYAHVEHV1dUVISAidg0I951dZIofF8+fPIZVKMWfOnB8dB0dHR0yfPl1wm5qamuDh4YGYmBicOXOGBqqqqqpQUFAAKysr+Pn50fVaVFRENRvd3d3h4uIiWH8LO7e2bNkCb29vnrPa3fkqLCyEoaEhZW/t6OjAs2fPcPPmTd7c7U2nrbS0FCKRCDNmzKDrsqSkBMbGxvjyyy/p+6qqqsAwDBYvXsx77t2dWqFK5uPj4+kl1NzcHD4+PsjMzMTOnTuRlZVFncnuGQL236dPnxZUB1EXOjs7MW7cOOpQcvtzWbsaGxtx5coVTJgwAXZ2doLYqNFoaAUQ2w/MZfUMCQkBwzDIycnBlClTkJubi9LSUrof37lzB3K5/KWVqOsC90Kvb9mAPXv2wN3dHXfu3MH8+fMhlUp5DiW7Pp48eYIlS5YgJycHa9asEbRHl4vi4mKYmJigoKAAMTExPIeSLXllHcrGxkZMnjxZcEfS3d0dUVFR2Lx5MyoqKrB582Ze7+j169epQ8mWfwOvrragvuffixzJtrY2XsWeWq1GamoqdSi5sm9C2/giPdOnT5/S58mWvHp4ePCCVK8dylcffYD/T9GmZ6xdu5bMnj2bfv3BBx8QtVpNPvvsM2JtbU1+//vfE0dHR/LJJ5+QOXPmkE2bNpGwsDASEhJCPvvss5dhMiGki02zo6ODVFRUkJycHPL48WPy0UcfkadPn5InT54QtVpN3xseHk4yMzMJIYSy9AmJpqYm4u/vT9rb28lbb71FKisriaOjI8nMzCTvv/8+mTNnDvnuu+/Ir371K/Lhhx+S58+fk3fffZds27aNvPHGG73KjqpSqYiPjw/5v//7PzJx4kTyl7/8hdTU1BCZTEYSEhJIeno6IYSQ06dPk4KCAnL8+HHyt7/9jRgYGJDHjx+TtrY28uzZM7Jw4UJiYGDQKzZxcfnyZVJQUEAePHhA3nnnHRIcHEykUillIGTZ+Pbv30/S09NJTk4O8fX1Jc+fPycHDhwg7e3t5MMPPySDBw8m/fr1o+/vLbS1tZHGxkby4YcfEgBk/vz5ZMuWLcTQ0JA0NzeT8vJy8sknnxBPT08SGxtLfvOb3xBCCFm1ahX55ptvyMaNG8mvfvWrXrPnpyAiIoI0NDSQDRs2kPfff18nUzEA4ujoSJydnUlWVpZgtgAgc+fOJZcuXSKzZ88mn3/+OenTpw99Tq2treTUqVMkMzOT/P3vfyerV68mhBCyZs0acvfuXdLY2EisrKyIvb09+fTTTwWzMyUlhTQ0NJD169f3+J7GxkYyceJEcuPGDbJ+/XryySef6Px7e2vtnjt3jkRHR5MhQ4aQ9PR08otf/IL06dOHVFdXk8jISDJ8+HASHBxM33/jxg3y97//XfD9jYv29nby5ptvkkePHpE+ffqQEydOkHv37pGLFy+SO3fuEI1GQ+rr60mfPn3IsmXLiIODAyHkH+MEDjOp0NC19wMgGRkZZP/+/SQjI4P4+fmRd999l2cjC6VSKdhaLi0tJYsXLyY3btwgS5YsIRYWFpTV89mzZ+Tvf/87aW9vJ1evXqVn269//Wvy2WefEVtbWxIXF6cXVuN/Bjdu3CBjx44lGo2GzJw5k5ibm+vlc7dt20YOHz5Mli1bRlQqFcnPzyeFhYVEIpGQBQsW8JjR9YXuc2ny5Mnk4cOHJCwsjOTl5ZG6ujqyfPlywjAMaWxsJHv27CG5ubmkT58+RKPRkI0bNxIjI6Net6ujo4OMHz+e1NXVkRkzZpDf//73WmuRHasbN26QvLw8cvjwYTJ//nzKgtqbe15vQl/zT6VSkcDAQPK73/2OLFiwgHzwwQd0zNra2ohcLid/+tOfSF5eHmXebWtrI2PHjiUlJSVk5MiRxN/fn+47QqCpqYn4+vqSzz//nMyaNYt8/PHHPBudnJyIXC4n48aNI4R0sdDevXuXpKamkj59+pDQ0FASGhoqmH2v0Yt4KS4suiINc+bMgZ2dHfz9/bFu3TosXrwYsbGxVJCbYRg4ODhg4sSJ9N9TpkwRTH+JRXV1NYqKilBYWEi15FgCAm6EPTU1FUOHDkV7ezs6OztRXl6OY8eOYc2aNZg1a5bgWVPgHxGbzs5O7Ny5E9HR0aiurkZnZyf2798PhmGQkJCA6upqqNVqHD16FBMmTMDkyZORn58vSHkcGy2LiIjgZRubmprg7u6OL7/8khdZVKvV2LlzJ8aOHYshQ4YgKSkJW7ZsoZno3saVK1dgbm6OlJQUZGRkYNSoUVQ8mluesmPHDhgbGyM/P/+FmZXezrqo1Wp4e3tj0qRJPGa2zMxMWq45efJk3Lp1i2ZX2ee3ZMkSyOVyvZIRcIlqjI2NMWbMGJ3fZ/Wh3NzcaJRZyAizQqHglYB2h1qtxpYtW8AwDNauXSuYHS/C8OHDERUV9aO9NWvWrIGpqSlvPQmB8+fPw8TEBNnZ2XRusc+vpKQEtra2OiPu3GcsNNRqNfz8/JCbm6tVGqzRaFBfX49r165h8+bNCAgIgLOzMy/boQ8bm5ubsWnTJlpOpiuyzmYozczMsHfvXq336SsaX1ZWhiFDhsDKygpnz56Ft7c3FAoFj2ylubkZpaWlWLZsGeLi4jBo0CDBSr97A1evXkVgYKDesn5AF9sktwS8sbFRZ4aS28ojxFxsa2vj7RNcFuDvvvsOoaGhuHz5Mi5fvgy5XA4HBwdehnL16tWQy+WClt7W1tbCz88PW7du1epn7o47d+7gzp07lINCqJLv3oTQ80+j0UChUIBhGK39WK1WUx1JXezfarUaMTExsLOz41VF9Dba2trg4+MDsVhMq3+665kqFArePY9b8mpvb4+goCC9aMe/xr+Pl+JMctkSZ8yYATc3N0yYMIFegGtqanD79m1s3LgRU6dORUBAAHUwpVIpj42qt3Hx4kW4ubmhf//+YBgGlpaWVOS6e7nK6tWrYWFh0SNdP6AfuvTW1laMGTMGsbGxmDNnDu97rEOZmJjYY6lUbzpDKpUKMpkMsbGxtHePHa/79+/D3t6eR9LBhT4ueRqNBhMnTsTQoUN5hCrffvstLCwsEBYWhmPHjqG5uRl2dnb48ssveZc6fZXX/KdI5HBRV1eHGTNmUAp8pVLJG68nT55g4sSJcHNzEzwg9PDhQ5iYmNCe5p6eW21tLVxcXDBu3LgXvq+3wDpo7JobO3YsnJycqNPRfS2yXx87dgwmJiaCMsqqVCoEBASAYRgtR6G8vBxWVlZgGAZz585FRkYGli9fjpMnT6KxsVHv823s2LEwNjbGihUreA5ldzuOHTsGhUKBQYMG8XqzhUZ2djYYhkFeXh4tJ9PlHLI9sNbW1nrv++LOddahZBgG7u7uvF5OXeRn3SWSXkW8DNI2Fuyz5jqUo0eP1kv/XGBgIIYOHYpNmzbpfE9ISAiSkpIAgArCOzg4UFKe+vp6QZ0MADh8+DAMDAxe2Cvf3t6O5cuXw8DAAM+ePcOtW7cQGRkJS0tLrbPlVYTQ82/fvn0Qi8VITU2ld7v29nZ4eHj0KPHC7o8ajUbwMxgApk6dCmtra2RlZdF7TGtrK7VRV3CUtfH27dt6lQ17jX8Pencm2U2W61DOnDkTAwcOxLhx43QeqGq1Gs+fP8fp06cFy1YBXX1CpqamGDt2LA4cOICtW7fCyckJvr6+OhuWDx06BIZhBL3g/RScOXMGgYGBNIsGgEdYwzqUycnJuHjxomB2dHR0ICwsjGoVcaFWq+llJT8/Hzk5Odi7dy8uXLig05kV6qBob2/HkCFDMGHCBJ7dQNfF087ODhEREbh69Sqvll9feJUlcjo6On40215dXY2ZM2fCyMgIfn5+WLp0KcrKylBQUIARI0bA2tpa0Ig3q4H36NEj9O/fHytWrACgO6jDvjZu3DiEhIS8UP6iN1BRUYHBgwfz+ljOnj0LIyMjTJs2jb7WfT10dnZi/vz5CA0NFbTaob29HSdOnICjoyMUCgWdR9euXYNYLIZcLkdsbCzCw8NhZGREq0ecnZ0hl8tx/PhxwWzThenTp8PIyAjLly/XIrLhrqPi4mKEh4dDLBbj5MmTerGtvb0dI0aMwMCBA7Fo0aIXOpS3bt2CVCrFypUr9WIbF9z5fuHCBSQmJsLCwgKXLl3S+v7rvqUu/NRx4DqUubm5EIlEWiRqvQ22B9bc3BxGRkaIiIjAzp07eYHTq1evwsvLi2brS0tLERAQADMzM731DxcVFcHExIT22/Y0pjt27ADDMJQ1/8aNG3pxgv5T8N1330EkEiE9PR03btx4YUZSpVIhPz9f624mBLjnVE5ODiQSCWbMmIG7d+/Cy8sLwcHBPdr41VdfvTRd8df416EXZ7KlpQVnzpzROvDZyI1Go8GsWbPg4OCAcePG0fcJUYLZE1jCiezsbF5afdKkSRg0aJBWs3JnZyeuXbvGc5z0yZ7ZHYcPH0ZQUBDEYjG9rLe1tVGbvv32WzAMg3nz5glq265duzBw4EDExsbi1KlT1A5PT0/0798fvr6+8PDwoBdRhmHg6+uL2NhYwUWbWWRkZMDX15dGX7llQEePHoVYLMaYMWO0iHn0gVdVIkelUiExMZEn9M5F9zV6+PBhBAcH0wz/oEGDkJKSIpij29TUpGVbeHg4vLy8eNFYXQgPD9fJ+tnbOH/+PAYPHgxHR0eaBXj69CkVi87Ly9P5c3fv3oVCoUBOTo7gNnZ0dODUqVMYOHAgIiIiUFRUBLFYjKlTp/ICfQ8ePEBZWRlycnIQHR0NPz8/vWcn29vbkZOTAyMjI6xevVorEMndj4uKijB06FC9kMVwySWGDRsGqVSq06HkMn06ODhQvV99o7tGcnBwMCQSCb3Av0ZXme/Bgwfp1933EnbPbm5u5mWk2Pc9f/4ceXl5gl+S6+rqMGLECBgYGCAnJwcBAQGQy+WQy+U4evQoHj9+jLa2NiQmJmLGjBkAuuZpSUkJwsLC9EamdO/ePfTv35+3p+kKXDx9+lSLfOfnju5zb9++fVT2xc/PT2e2T6VSYeLEiTA1NX0pjtrMmTMhkUggkUjg5+enMznDytAYGRnpRTP+NXoXgjuTarUavr6+6N+/P7y8vPDNN9/o3LDYDGV3h1If0VDWkZw5cyYttWUjK7m5uQgPD0dzczO1hf1/c3MzLC0tsWDBAsFtZMGtOb969SrOnz9Pv3fixAl4e3vD0tKSXla5DuWpU6cEu/Bxf+/evXthY2OD+Ph4nDx5ktbG3759G62trVCr1bh16xYKCwsxY8YMuLu7w8rKSrBN7vr167w+i40bN8LW1hYbNmzg9YWx47Rr1y4wDINDhw4JYs9PwaskkaNUKilzLOvwcA9+9iJ1584dGBoa8jQR7927h4sXL+LZs2cv1Lf6d6DRaJCZmQmGYVBSUkJt2rBhA0QiETIyMuh6ZvubWVRUVCA0NBRff/211t/V2+js7MS5c+fg6+sLOzs7GvQpLy9HZGQkDA0NkZWVhfv376OtrQ0qlQrHjh1DSEgIAgICtGRqegN3797Ftm3bkJubi6tXr9L1cOrUKTg6OoJhGIwaNYoXWOmp/w8QNvCniy1WrVbD2NgYAwYMwIoVK7QCltyxEmr+AV1nwerVqzFt2jSsXbuWtka0t7dj+PDh1KFkL1HcZ3n58mX4+Phg//79Wjb3BpqampCbm9tjBQ27ftng2o0bNxAaGgqJREIzlD9ndHZ2YubMmWAYBlu3bqWvd6+yunPnDry8vHDlyhXez+urn5j9nPr6eoSHh0Mmk2Hv3r3YtWsX0tPTYWxsTIO2p06dgpGREe2302g0eu2zVyqViIiIgFQq5WXKuu8tu3fvhlQq/dk7F83NzVi7di1Gjx6NlJQUZGdn4+bNmzRAdfDgQZiZmWHo0KFa9yh9aIUCXUmjrVu3YurUqRg7diy2b9/O+7w5c+ZAJBJh9OjR9B7DrgnWRn3p/b5G70NwZ/Lp06eQyWSQy+XIycmBgYEBZDIZcnJycOvWLd7lo6OjAzNmzICDgwMyMjK0yBWEQEVFBdU26u4slpeXQyKRIDs7G7W1tVr12y0tLZBIJMjMzBTcTuAfh5ZSqURoaCjtYcrIyKBlLMePH4ePjw/Poeyu0SjEha/7QbRr1y7Y2NjA1NQUnp6eL+zBaGxsFKxfqLm5GSkpKTAwMOBdBGJjY2FlZYWioiLquHV0dNAxjoyMpJpbQgY0XnWJHKVSiUGDBiEmJgbe3t48rVLgH2ulsrISUqkUycnJaGxs1HtJ3KlTpxAdHQ1jY2NaylhfX49hw4ahf//+mDp1qpZNT58+RVZWFlxdXQUtnwf4/dZnzpyBQqGAvb09XaPl5eX0wB8wYACcnZ0hlUrh5eWFuLg4+nx7s7/5/PnzcHR0hKWlJS2N27x5M4CuuXX69GnIZDIEBQXpDFT0JLfRm2CdNLZPnquhyxJNhISEYNy4cTA0NNRZ8qqPPjU/Pz9YWVnRTHxwcDB9th0dHUhKSsLAgQOxYMEC3l5YX19PJaeEKN9rb29HTEwMGIbBuHHjtMoY2fl07949iMViqu9WVlaG8PBwMAxDHeOfM86fP4/k5GRYWVlhy5Yt9HV2Xd67dw82NjZITEzUm+QCF921pRsbG6FQKODg4EA5CoqKipCWlkZJ+RwdHZGVlfVS7AW6ghZisRg+Pj46Sy/r6uowevRoREREaK3pnxPY/cXV1RV+fn7w9fWFqakpJBIJ5syZgydPngD4R4YyJSUFt2/fBvAPfVGhnTS2V9fd3R3Ozs60+szR0ZG2XgHArFmzIJFIaNCU/Vl92PgawkIvZa779u2DmZkZysvLUVZWhqlTp0IsFsPBwQEJCQm4cOECjVR0dHQgJycHxsbGmDJliqCX0s7OThQVFcHGxgYKhYLnvF67dg0DBgwAwzAYPHgwGIaBkZERgoKCkJmZiZMnT6KkpAS7d+/WS3kXN4Lj5uaG0NBQbNy4EStXroSBgQEmTZpEN9zjx4/D19cXVlZWgi7O5uZm5OfnIykpCaGhoZg5cyYOHz5Mbd23bx8kEgmGDBnCYxzjXkL14XRcuHCBXgQ2bNgAoGue+fv7w97eHgcOHOA5wxqNBuHh4Rg1apTgtq1Zs4ZX8mtrawsLCwv4+flhzpw52LhxI10bOTk5MDU1xZw5c7SE44UAy8obHh6O+vp6zJgxA35+fmhqauI9t0ePHsHc3Bypqal6F0Pm4ty5cwgPD4dIJKJkK0+ePEFcXBxMTEzg7++Pr7/+GiUlJVi6dCklPtGnYD2Lbdu2gWEYODk50c9vbGzE7du3sXjxYixYsAArV65EaWkpr2yyt1BaWgoTExNkZmaipKSE9stJpVK6j7Alr3Z2dlAoFC+lPCo3N5eW53P3Z7VarUXiwO2h5PaICQmuhtrZs2fR0dGBTZs2aQmtd3R0ID09HY6Ojhg+fDiuXLlCGawtLS0Fm4PNzc1Ui9Pd3R1jxoyhWR7WkaysrISVlRVGjRrFc3TPnz+PuLg4ejH9uaOsrAzDhg3Tcijv378PhmGQnp6u1/2vpaUF27dvR0ZGBoYMGYKIiAhs3ryZtjoolUqEhIRAIpHgu+++A9Dl/F66dAnR0dEwNDSETCZ7ac4kAJw8eRJisRj29vbIzc3F06dP8fz5cxw9ehQZGRmQSCQ0KPNzhFKphLOzM+VxALrOgaqqKkRFRdF5xzqU+/fvpw7ltWvX9JLtY22MjIzEQXMMUQAAnhBJREFUuXPnaDVccXExgoKCqFYtC65DWVFRgRkzZrx2JP8LoBdn8t69e/Dx8cHUqVMBdG2Czc3NGD58OBiGgampKQIDA7Fx40ZaTrVkyRK9XF5aW1tRVFSEgQMHIjAwEADwww8/wMzMDOnp6Th8+DAOHz6MTZs2IS0tDV5eXvTiHx0drZfyLhYajQbTp09HdHQ0jeqcOXMGvr6+dFNhL1EnT56EVCrF0KFDBbFFqVTCx8cHcrkcQ4YMQUJCAkxNTSm7GLdk1NbWlgrHs9A3sQ17EZBIJNi4cSOArqxAQEAAJBIJVq1aRS8CFRUV8Pb25omzC4VXVSJHqVTCxsYGMTExlD12586dMDQ01MrQl5SUID8//6U5kty5dOPGDepQsiWvz549w6pVq6BQKCASiSASieDm5oaJEycKdlG+efMmr6yc2wtbXl4OsViMpKQkeHt788TDe0JvBl24/eHcZ1ZQUID+/fvziBE6OzupQxkSEqK3niru35uRkQEbGxvMnj2bjmFPjIXTp0+HqakpFi1aJLhDyXUka2tr6Tx89uwZb5/hZpUXLlwIuVxO+4hjYmIEuyyz9lRUVMDJyQmenp5wdHREeno6dSgfPXoEqVSKlJQUnev3P4G1VUhoNBpeNYAuh/LKlSuYP3++XiUM2EyQn58fQkJCkJaWRi/uHh4etK2DW8X07bff0vuVUqlESUnJK8GWWV5ejpCQEIjFYnqHcHR05GX3f45gg7nR0dE65TMAID09HQYGBpg9ezYNCnz33XcQi8UwMzODhYWF4I4kKwP38OFDXh840PVshw0bBoZhsHTpUvpzs2fPho2NDRwcHAQvv30N/UBQZ5I76efNm4cBAwbQKHJlZSUsLS2RlZWF3NxceHl5wcDAAGZmZrxJpw+o1WocPHgQUqkUHh4eMDMzw5QpU7RKN9va2tDe3o6LFy/yMpL6dIyGDBlCG+ebm5sxY8YMpKSkYOXKlZQtjr38X79+vdc1EAF+JIq7UdXW1mL06NEwMDBAaGgoff5ch1KXRp1Q6F76w3Uo2XI+jUZDSTJYFksnJyf4+PgIHiB4lSVyNmzYgMTERF4z/+3bt2FlZYXVq1fz7O/+b6Ghy7FiS5U7Ozshk8lgaGjIy1Cyz7KyshIVFRVQq9WCUber1Wr4+PjAxsYGJSUlvHl09epVmJmZYfLkyVCpVCgtLYWfnx/PoRQyW3/+/HkYGxsjJyeH9hCyz47Vl+veW8iWvDo4OEAmkwnOpqhUKpGVlcVzsMeNGwdbW1vMnj2bVmb0VJqckZEBKysrQdskVCoVnJycEBcXR8eDfW7Xrl2DVCqlwYzuUCqVuHDhAh4/fiy4A9LZ2QmlUokZM2YgNzcXubm5sLS0RHp6Ou7cuYOHDx9i1apVr7XcOFCr1S8MZJ8/fx6JiYmQSCS81gl93QOam5vh7e2NqKgoXL58mbdf7Nu3D46OjrCysqK2NTY2IiwsDNbW1jhw4MBLzUT2hIaGBty4cQPr1q1DQUEBTp06Jej59qqDlSbz9vamWUcuuGdKbGwsLC0teQR0+/btg729vaBVN83NzZDL5fDx8Xlh0Km8vBzBwcFwcHDgqQlMnz4dlpaWr7Re7Wv8dPSqM9nR0YGGhgZehJO9sD1+/BiOjo7Izc3FtWvXYGlpieHDh9MNo7q6Gt9++y0CAwNfyuRiHUpfX1+Ym5vzGoS791JyoS8GQ1aMe8iQIZg9ezYAYOXKlejfvz8l4WFlOTw8PHgU+L150e8pWsY+54aGBsyZMwcGBgZISUmhB+zevXthZ2eHwMBAHmlQb+P+/fu4fPlyjwc7W84nkUhoySvQtfmy+nmLFy+mz1UoJ+lVlsgBuvpVdBGWhIaGIiwsjH6t797I1tZWRERE4NChQ1pVAS0tLZQa/eDBg4iIiIBIJMKJEyd02ivk5e/evXuQy+WQyWTUob1x4wbMzMy0+pRKS0vh7++PQYMGCRqhvX37NhiGwbBhw7SIYNj+8MTERFy7dg0lJSVoaGjgjdHRo0cxfPhwQQMHSqUSdnZ2GDp0KJ4/f877rHHjxoFhGB55UU9gNW6FQFtbG0JDQ2FsbEzPKnb/a21tRWhoKMzMzFBUVITCwkKcPXsWDQ0NetU97D632ZaD6upqbNmyhTqUQu8j/2lgCQPFYjHi4+MxZswY7Nq1C6Wlpbx7TUVFBRISEmBra0sDk4B+gmpLlixBQEAAj1CJ+7xPnToFd3d3DB48mDLyNjQ0ICYmBgYGBnqRhniNfw8PHz4EwzDw8fGhrPiAbuK7mpoaWjbKhZCEY0CX5JJIJEJ4eDjdj3u6Dxw7dgwMw9BqDRb61tZ9DeHQa85kU1MTJk6cCB8fHwQFBSE3N5f3fbVajXHjxkEqlcLMzAwjRozglQaxEHozftEFsrW1FQcOHMDAgQMRFBSkFwKgntDTOOzatQvnz5/H7du3YWJiQvtyOjs7ERkZiZEjRyI9PV2QcWxpaYGPjw+cnJx0blRcGvQxY8bAzMyMMhQCQGFhIWQymWAXmKqqKpiamoJhGAQFBSEqKgorV67E999/T8uCgS7WPTYbyXUou6O3AwWvukTOi+YM+2z37dsHCwsL2oOjb9TU1MDb2xtSqRTHjx+nY8eSsQQHB1NH4vz589ShZLNE+nR+q6qq4OrqCrlcjk2bNkEsFmPy5Mlaa4dleXVwcMDw4cMFs6empgYymYw6uGw0+cqVKzA3NwfDMJDJZLS0WiqVIjw8HGvWrMG+fft4v0uoigc2UMXNiHM/a+LEiZR4gt2f9VkZwtoydepUODk5YfTo0XS+sZF6ts/ezMyMjiXbK/nll18KJrnR1NREnxM7JlxirvT0dAwbNgwAkJ+fD4lEwit5fdVF4PWB2tpaJCYm0ufm4+NDNVVtbW2hUCgwZ84cnD59Ghs3bkRcXBwGDBiAb775Rm82RkdHIy0tTet17vM7dOgQDAwMeOQn9fX1SExM/I/Q8Pu5zkWlUonly5cD6Ao+9u/fH/7+/rwqh+4OZUdHB6KjoxEbG6uX5IZKpcLatWsBdDHju7i4IDo6mvbq9nSnVygUGDFixE/Sq36N/zz0ijOpVCrh7u4OV1dXJCYmUiYndiNjJ1dlZSVsbW0piYcuCLWJ1NXV0ctT98/oru13+PBhSKXSl+ZQsrY0NTVhwYIFyMnJQUFBAe89RUVFcHR0pCVW165dg4+PD084vLcvfBUVFXB0dISnpyfvcsm9oLP/fvz4MczNzbWYboXsq6uurkZKSgoYhsGAAQMQGRkJsVgMhmFgYmICNzc3jB49Gvv370deXh5CQ0MxYMAAFBYWCmYTi1ddIqe9vR0JCQk851/XWrx37x4cHR2RkZHR43uERlVVFSIiImBlZYULFy6gtbWVOpLdSzDPnTtH2Sy5Pbu9jVu3biE/Px9jx47Fxo0bafl3VVUV3Q8TExN7nP+dnZ0oLy8XPJhWU1MDuVwOZ2dnlJWVoaysDGZmZsjIyMClS5dQUVGBQ4cOYdmyZYiMjIS9vT29VAv5rLn9h2yZfk9Z5HHjxsHa2ho5OTk0sq2PedjU1ISkpCT6mTk5OZR5vKamhopxX716FS0tLaioqMDZs2cxc+ZMxMbGQiQSwdLSUpA+tZaWFirfk5qaiq1bt2qdsQcPHoRCoaDZ1EWLFsHKygpjx47tUTbk54KmpiZ6Dly7dg0TJ06EgYEBdu3ahevXr+Pbb7/F1KlTER4eDolEQs8YLnla94BLb0Oj0eDp06dwcHDAunXrAEAr281dB4mJifD09ERLSwt9/efqpP0nQKlUwtbWllZlAF0VIz/mUAJAfHw8YmJi9MJcbWdnh5SUFDr31q1bh8GDByMqKopmKHXZ4efnh9TUVEHte42Xh3/bmWSjyZGRkTTi1dHRARcXFyQkJPBKRJubmzFhwgTIZDK9Hl4VFRWwt7fH+vXrdTqUXPZRts+gqKiIshjqMxXPZW11cXGBo6MjpFIpRCIRkpOT6fu2b99Oda/27t2LsLAwhIeHC3IZValUWLlyJYAuYp/AwEB4enry2Aq5Fz92k8nIyICfnx8aGxupXUJsdpcvX6alRteuXUNGRgaMjIxQVFSEBw8eoLS0FMuWLUNKSgpl5mUzMex/bCmiUHjVJXKePXuGwYMHw87ODsXFxfR1Xetkx44dYBiGlksJfYC1t7ejvr6edwl/8OABwsLCYGVlBQcHBygUCl42izsfT58+jWHDhgmmVVZaWgobGxvIZDJIpVIwDMMr566qqoKvry8GDx6MkydPaq1RfVdnPHz4EJ6enrC1tYWZmRkmTZqk8zM7OjpQV1eHo0ePCtofzp4hUVFR9Bmyz0+tViMsLAyXL1/WKnm1trbG/Pnz9bY/Hzp0CAzD8HQEWYfS3Nwcfn5+UKlUPY5ReXm5YOW3hw8fhoODA0xMTDBgwAAEBwfDzc0NR44c4QVYQkJCkJCQQL9eunQpGIZBZmbmzzpbkJSUhODgYPr1zZs3kZycDBMTExw+fBgAP1B68eJF7NixA6NHj0ZkZCQGDBigFy1EjUYDDw8PpKenv/A9QBfJiaurq17Lq1/jXwMbTIuNjdXqwe7JoWRx9+5dBAUFYcmSJXqxsTvhGKDboeTqq966dQvBwcE0o/k6qPHfh3/LmeQyObHRZPbAT05OxqhRo9De3s672BUVFfHEf/VRdtbR0QE3NzdIpVJs3bpVp1D9vn37wDAMVqxYAaDrElNUVAQDAwO96UiyFzaNRoN58+YhJiYG1dXVuH//PpYtWwYzMzNeGVxSUhItvxkyZIggWnQAMGXKFFhYWNCvT58+jYCAAHh6emLv3r309e7PMjk5GaGhob1qCxdsRtnT05NX9nPz5k0kJSXBxMQEBw8e5P2MWq3GrVu3cPToUcycOROpqalQKBR6KQ95VSVyWAQGBsLU1BQODg68vpruG/+zZ8+oYy5k/yvQFchITk6Gi4sLGIbBiBEjqONfXV2N5ORkMAyDXbt2af0s126hBLlZZtTZs2dTZ3fdunUwMDDgaddWVVXB3d0dMplMp0MpNLpnJh4+fIiQkBAaFOjeI6yv/vDm5ma4u7vD09OTksCw+5harYaXlxc8PDzo+cK1ISMjAwYGBli4cKFe1kdZWRkYhuEFW4Aucjlra2te1pLrfOurtHrHjh0ICwuDm5sbli1bhilTpsDExAQRERH0vL169Sr8/f15WbSCgoL/iNJHITFz5kz4+Pjw1uWtW7eQlJQEkUjEO0d0PU99MFl3dnaitbUVI0aMgIODAy5duvTC948ePRrh4eGC2/Ua/x6ampogl8vh6+vLC4gCfFZUXQ5lY2MjJk6ciEGDBgnKzMvayAbMutsH8B1KtuSVa6OzszOqq6sFs/E1Xi7+ZWeyqakJjo6OSEhIwJMnT3iH5tOnTzF48GCt0kwWiYmJsLGx0QtbF/eSFBgYSFnOuOxTrCO5fPlyrQwbqx2mLzQ1NWHVqlVIS0vjjV99fT1WrFgBExMTnkNZUlKCK1euCKJFx2Lp0qUYMGAAL0N26tQpnQ4le1mtqqpCeHg4cnNzeU67EEhKSuJF24Gui0BycrLWReBFz1Joh/JVlsh5+vQprK2tMX78eMTGxsLe3p5G5AFth/L48eNwdHREXFycYJFvpVIJFxcXhIWFITc3FwsWLICRkRF8fHxo9PPOnTuIjo6GtbU1jh8/rnXRE3LecSU2ujMkshpp3DXDOpSurq5aLK+9jXPnzmHs2LE4c+YMdcRYsGvg4cOH8PLygrOzM44fP/5SMlPnzp0DwzAYMmQI75LEkilxdSRZcNdwVlaW3hyh9vZ2ODo6Us007ryfM2cOBg0axNN80zc5FQBs2bIFfn5+kMvlqKmpwenTp5Geng4jIyMkJiZiyZIlSEtL0ztj+quO1atXw8LCQivLzT1HuldicOehEPtMW1sbampq6BpmHdbLly/DzMwMo0aN4q0N7vqtrq5GZGQkli1bJph9r/HvQ6lU0j71IUOG6JTx6MmhbG5uxuTJk2FmZsZz3oSw0c3NDQzDIDk5WauqkLvPsQ5ldHQ0PaMzMzMFt/E1Xj7+JWeytbWViklzG7yBrgOWjXiPHz8eiYmJmD59Or755hs6ufLy8nj9fkKD61AGBQVRh7KlpQUNDQ0IDg5Gfn7+C5ke9eVQnj17lpZerlq1ive958+fU4cyKSlJ62eFsvHChQtgGIbHEAvwM5S7d+/m2Tlp0iTY2dmhsrJSEJu4yMnJgaOjI5RKJe+56boIsOju4Ap52P4nSOTcunULtra2KCsrw/HjxzFkyBDY29vj0KFD9D2dnZ28cdq5cyfu3bsniD3ckhruPlFcXAwDAwPe2rh37x4iIyOpQ6mPixObpZo1axbPqWD/vWjRIjg6OmqRTVVVVUEul8PCwgKXL1/udbvY7MWgQYPAMAx8fX3h6emJ7du363S6Hj58SHsojx8/rjd2ai6+//57DBw4EGFhYZQ6vqceWKAr4KZviSH2/+Hh4YiNjaXf465ttuQ1PT2dOiVCOZQajQYtLS0oLy9HdXU1z6nYsWMHXF1d4ePjQ7MVZ86cQVhYGAICAuj58pqS/x/rdd++fTA3N6d97Nzn9qJzRCioVCqkpKTA1dWV9mfu3r0barUanZ2dKCgogJGREcaNG6f1HGtra5GVlQVHR8dXQkfyNXRDqVRi8ODBiI2NxaFDhyCRSBAeHs4ro2ehy6GMi4uDqamp4DqSTk5OiI+Px8KFCyGRSJCWlqY153pyKIcNGwYzMzNBbXyNVwP/tDOpVCoRFRWFnJwcjBgxAra2tpQRs729HR4eHrCzs0NycjKGDh0KZ2dnengZGBjAzc0N8+fP1/smxx4aHR0dCAgI4Okw1dbW6r3sTBfYS8upU6dgaGgILy8vLea/58+fo6CgAAzDYP78+Xqxq6KiAubm5ti2bRsAvtPKdSjZHsqsrCy9RKLYZ7p69WpYW1vrZHd8GReB/0SJnLKyMkqqA3RlHsPCwnQ6lEJnXNhDlu3NYMEKiHt6eiIlJYXn+LAO5cCBA/H9998LHhyYPXs2GIbBli1bdLLt+vr6amXLWVRWVgrGuMxi/fr1CA8PxxdffEHLQWUyGWbMmIFbt27xHGDWoZTJZDh06NBL2QuLiooglUoRFhYGJycnhISEaGVUga5L9qRJkyCVSlFfXy/Yc9ZoNDoztcuXL6ds1uw4cccrJycHzs7OSExMFKyXs6mpCTNmzEBQUBDMzMwgFovh7u6OhQsX0oqGXbt2wc3NDV5eXnQfqaurQ0lJCVJTU2Fubv6zdTTUajUdJxaVlZXUYWPRveQ1OTkZYrFYcKIdpVIJV1dXhIeHY9OmTbh06RIWLVrEC+Y+ffoUy5Ytg7GxMWQyGTIzM7Fr1y7MmDED8fHxgmsMvsa/h9bWVpiamvL6xM+fP08dSl0SUex8vHHjBr1TC60j6eLigvDwcLqXFRQUUIfy5s2bvPdz7wUbNmyAubk5BgwY8Doj+TPBP+VMKpVKDBo0CJGRkVCr1bhy5QqGDRsGW1tbrF27Fj4+PlAoFDwZBpb2ft++fUhJSUFgYKCgjeqXLl3CsGHDMHfuXBQXF+vsK2hra4NCoUD//v1phpK1VZ/o3lej0Wjoa2wGJiYmhidGC3SVvO7evVuvWYSQkBCMHj0aQNemxh0r1qH08vKCQqEQNBJVU1ODJ0+e8C56ZWVlGDBgAE+PiTs2P/zwA0aMGAEzMzMeaZAQeNUlclpaWlBUVIQNGzbg4sWLaGhooN/rngV6kUMpFNra2hAcHIwBAwbwbGPHQ61WQyaTYfLkyVrZ5aqqKvj5+cHFxUUwja1bt27h2bNnqKurw8SJE2FsbIxt27bxLqfx8fFwdnb+SdkpoZ7zyZMn4erqik2bNgEATpw4gczMTEilUkgkEsTGxuLMmTP0IlNTU4OBAwciJSVFEHtYqNVqXLt2DUVFRTh69Cjq6uro+Bw4cAC2trYQi8W8dcolJWMDVULqcTY1NSEkJATR0dHIzc3F3r176dooKyuDkZGRVsUF9zlOmTIFcrlcqzy3N6BUKiGXyxEaGop58+ahqKgIa9euRUxMDAwNDREbG0vP18LCQnh4eEAul2tdOl+m7NXLREtLC9zd3TFgwADI5XJkZGRg9erV2L59OyQSCXbs2NHjz1ZUVCAiIgK2trZaZe29BbVajaFDhyIiIqJHZ58bCDpz5gwN2kulUnh5eSEnJ0cnU/hrvFpYsWIF5Uhgce7cOUgkEoSFhb3Qobxz547g5f0PHjzAnDlztO4nK1eu/EkO5bZt217Pw58R+gAA+QloamoiPj4+5E9/+hOZNm0a+fTTTwkhhFy5coUsWbKE/L/2zjyuxrz9459KY4x1MGMZzIwxc7cXKSUkS5JEUYkopBJlJ9Ewhexbxr4O2cfO2MZghkEhQnYtQpLktJ2W8/n94XfuOUfxzDzTSR73+/V6Xo+5zzmd69z3d7u+3+v6XOfOnUONGjVw5MgRfPzxxyguLoaWlha0tbXFv1FQUACSqFy58t/5yn9McXExRowYgePHjwMAdHV1UblyZdSsWRNffvklWrduja+++grW1taoUqUK3Nzc8PLlSwwePBg9evRAlSpVNGLXm2zV0dFBTk4O5syZg+TkZBQVFeG7776Dn58fPv/8c5w6dQoBAQFo3bo1RowYARMTkxJ/p6ioCJUqVSoTm3JycrBkyRLUr18frVq1QtWqVdG4cWMAwA8//ICEhARs3bpVfD9JaGlpAQBiYmIwadIkZGVlYcOGDdDT0ysTm1S5c+cOevbsieLiYnz22Wf47rvvYGJiAoVCgXXr1mH+/Plo3759qffj3r17CAsLg46ODjZu3FjmtgFAdnY23N3doVAo8NVXXyElJQX37t3D6NGj4efnJ96vpKQk9O3bF/Xq1cOmTZvwySeflPhbqve2LO0bMGAAXrx4AZlMhpycHLi5uSE4OBh16tQR36dQKMR++8cff2D58uV4/Pgxxo0bBwcHhzK16XUePXqE1atXY/v27QgODoafn5/YVwBg/vz52LhxI3bt2oWvv/66xOcfPnwILS0tfPHFF2VuW2pqKjp37oyIiAj06tUL6enpmD9/Pvbv348ZM2bA2dkZAQEBuHXrFpYtWwY9PT2NPMe3ofp9ISEhOHPmDH755RdUq1YNALBixQosWrQIderUQXp6OoyMjNCrVy+0adMGn3zyCWrVqiXe67ImJycHo0aNQlJSEpKSkgAAX375Jezs7DBq1ChUrlwZx48fx9SpU/H1119j6NChaN26NYBXbXf27NnYu3cvtmzZAgMDA43YCADHjx/H9u3bkZSUhKdPnyIvLw+6urpo1KgRvv32W1y8eBGRkZFo166d2v1W7TcZGRlqfaosKCgowODBg6GtrY2IiAg0adJEfC0tLQ2//fYb5syZAxMTEyxcuBA1a9bEjh07sGHDBujq6iIyMlIcl8u7XVYU7t+/jyNHjiA9PR2PHj3C7du38ejRI9SuXRvPnz+Hrq4u7OzsULt2bQiCAD09PVStWhXfffcdCgoK8Pz5c5BEgwYNNGLf7du3ERISguDgYLRr105t/fTzzz8jNjYWt2/fxpdffglfX18YGBggNzcXWlpaeP78OerXrw8AGuvDEv+O3Nxc/Pjjjxg3btwb33Px4kUEBgbi22+/xcSJE2FoaKj2uup8qCkbDxw4gJ49e+Kjjz4q9XvXrFmDlStXonXr1hg6dCi+++478X2q46DEB8Tf8Tizs7PZo0cPmpqairueqjuxV69e5dChQ9mqVStxJ5wsXwGC+/fv89mzZ0xMTKS/vz9tbGw4cOBA7t69myEhIXR1dRVrDhoYGLBfv34MDg6mIAh0cXHhunXryl1COycnh/b29uzZsydDQ0M5bNgwdunShS1atBB3pU6ePEl9fX0OHjxY43lCERERauUylCcYixYtYmRkJO3t7UvstqvuWF26dEmjoVO3b9/mwYMHuWLFCk6aNImurq40MjISa+EZGBiwb9++HD58OOfNm8djx44xJiaGcrmcOTk5TE9P11ibrOglcgoLCzls2DAOGjSICQkJlMlkXLduHQVBYGxsrNp7X8+NPHPmDLt3785u3bq9tfTBv0G176WkpIhhpKp5o1FRUWrhyuUtcPLs2TOamJio5Yk/e/aMEyZMoJGRER0dHdmhQ4dyz0W7efMmT5w4If638r5cvnyZNjY2YlkfZTmhxYsX8/79+1y9ejW7du0q5rcr0cRpqVJool+/fty9ezfj4uJ48uRJ9unTh0ZGRvTw8BAjRI4cOUIbGxt6eXnxzJkzVCgUDAsL03h+kFwu5+XLl/nrr7+qndieOHGCUVFRHD16NLt06UJDQ0N6eHiUquCpyTZ58uRJOjg4qAkVqT6r/Px8/vzzzzQ0NBRFgshXJ5Rt2rRhnz59pDIRr5GRkcGHDx8yJiaGvr6+FASBTk5OYg1JPT09mpmZsXXr1hw5cqTGQ8CV872ynefm5jIpKYmDBw+mIAg0Nzdnly5daGNjQxsbG967d0/8rFRLsuKjFHscP378W5+X8oTyTSGvmkSZRrV8+fISEQyq49vbTiglPjz+ozOpTMBt0aIF27dvz5CQEFHCXTWU8OrVqwwICKC1tbWYQ0mWz4Lv8ePHNDY2FnMZHjx4wEGDBrFbt26Mjo4W33f79m1evHiRK1asoL+/P11cXETHKTAwUOODsGrdHfJVfo2rq6uaAzZz5kwKgsDjx4+LoXonT54UxT40gVwuV5O0v3btGrdu3cqIiAg6OzuzTZs2NDQ0pCAIYt6kKu9q8pLL5czMzOTdu3fp5eXFVq1accCAAbSzsxM3DpT/i4iIED9X1m3yfSiR8+LFC3bp0kXNEZLJZGzTpg1PnTrFZ8+eif1aaY/qc/3zzz/VwtfLksLCQrq6urJ///7iNVWHMjo6mitWrKChoaHG85XeRHFxMfPz8+nh4SGGgqqqV//www+io1aeTq5SsGvTpk0lvlcul7Nv37709/fntm3bKAgCFyxYoFYmJTk5mb/++qtGQ+aV/cPLy6tEG5LJZJw1axYNDAzo7u4uqmwrcyiVgjflITTh5eXFNm3aUBAEGhoaikqYqjx//pz79+9nmzZt6OHhoRaKrWnmz5/P9u3bv/U9GRkZHDlyJNu2basWYrZ///4POkcyLi6Ov/32W4nnpdpn5s6dSw8PD6alpTEvL4+XLl3i7t27OXfuXA4ePLhccr8uXbrE1q1bc+bMmTx27Bhnz57Ndu3aiWq8Dx48oEwm4x9//MHOnTur5XhKVHxevHjBTZs20cTEhGPHjn3r2knpUHp7e//HMjBlzcyZM6mvr89ly5b9LYdy9OjRUm7kB85bnUmZTMZ27drR19eXjx494rRp02hra8sJEyaIg7LqIkSZQ2ltbS0WkS8PHj58SFNTUzWFx/v373Pw4MG0s7MroTirRC6X8+7duzx9+rS4+NeEY6Qac6769/39/env7y/m//3yyy/iwvDkyZP84YcfxNyMuLg4jSz4CgoK2LdvX/r6+pZ6opKfn8+srCyeOHGCo0aNor6+Pnft2lXmdryJ5ORkHjt2jJs3b35jiQOSHDduHAcNGiTmnj548IC//fYbN23axIiICI2VPXgfSuQoFAomJyfT3NycS5YsEa+npaXR1NSULi4uNDMzY8+ePblx48YSn9U0MpmMixYtoomJiVrOXkpKCqdPn04DAwOxHuK7JiIignZ2diXaU1pamphDuXPnznIpsxETE0MjIyNOmzatRB1NZd84f/68eP+WLl1aQnhEFU2ML8roi8GDBzMjI0OtPSn7SU5ODufMmUNDQ0NGRESItp84cYKGhoY0MzPTqNCETCZjx44d6eXlxePHj4v5/YIgqC3Wlfbm5+fz0KFDorObmZmpMdtUmTZtGp2cnNRy60tj37591NfXlxZ3fPVsBw4cSEtLSwqCwA4dOvD8+fMkS57AHz16lIIglOtJUE5ODnfv3q3WfydNmqS2ETpgwAAeOHCgxEl427ZtuWjRonKzVaJseP78OTdt2kRjY+P/6FBevHiRgiDQz8+v3KMKpk+fTj09vf/oUK5du5aCIHDixIlS5MMHzBudSYVCwdGjR3PgwIFiknBxcTEjIiJoa2vLkJCQNzqUw4cPp76+fqmnWJqiS5cuokCMsnM+ePBATE5fuXKl+F5lg399J19TzlpkZKRYW5D8S/zAw8OD/v7+JP8Kf1CG9a1YsYJt27YtsZusCRvnzZtHa2trjhkzRk0c6fXJNjk5mWPHjqWenh737NlT5na8zuXLl2lvb8/mzZtTEAS2a9dODPV9/ZR38+bNNDIyeuvue1nfu/ehRI7qRDVp0iSamJhw7ty53LRpE52cnNijRw9u3LiR27ZtY0BAAAVBKJdn+zovXrzgqlWraGhoyOHDh4vXk5OTOX/+fOrr679xU6g8UI4ZmzZtoqWlpXiSr3p/nz17xpCQEBobG2vcoVStcalciJZ2IpqWlsZ+/frRxcWlhNiDplEoFBw/fjwFQeD69evF66rjitLmly9f0svLi3Z2dmqh9GfPntWo0IRqVIHq9967d4/u7u50cXHhy5cvSyz45HI5jx49SkNDQw4ePLhcTqPnzJlDc3NzZmVlkSw5Piv/+8aNGzQxMSlRyulDQ1mn1sPDgxs3buT69evp5OTELl26lLqpoiyBpXQ231YqrKxQhhSuXbtWTdRn//793L17t1oNZ6UdxcXFvHHjBl1dXXnq1CmN2CVRNhQWFvLJkyc8f/48r1y5IkZevHz58m87lHFxcWrhzGVNUVERnz9/zps3b5YIV50xY8bfcih/+uknjdooUfF568nkkydPxF1X1UK9/8mhvHz5MkePHl0uxaSVE+jYsWPp4eGhpoxK/uVQdujQQc2hLK9QtKKiIs6ZM4dt27blDz/8wPbt23Pv3r0kyQ0bNtDExESU7l+xYoV4H5ctW0YnJyeN7nqr3oPly5eL4Qqvq+2qDnSJiYkMCQmhIAglJrqyJCYmRrw3R48e5b59++jo6MgePXqUukg/fvw4BUEQcxA1faJW0UvkFBQUiH1Deb9evHjBqVOn0snJiY6OjrS1tVVTpbx37x67d+/OkJCQcikP8fqGwNscyhkzZpTqtGuKmzdv8vDhw3z48KHabmtiYiItLS15+PDhUj/37NkzTpw4kYIg8LffftOIbUpHUvVEUnkvr1y5wg0bNqi9X5krqVQ7Ls/SHzExMezbty+7du2qFtGgOvYo/x0bG0tBEHjkyJFysS07O5udOnXikCFDSlVeDQkJYdeuXd+4256fn8/jx4+XyzxHvnq2ZmZmHD9+vHittGe5adMmdu7cWeMRDxUZ1Rx21dDq5cuX09DQsNRnlpGRQTMzM3GMKY81QkZGhhhSuHr16r+lEpuZmcnJkyfT0dFRI4rBEmVDdnY2g4ODxVqhgiAwNDRU3Az6Jw6lJm0cM2YMnZ2dRRuXL1+udgr+dx1KiQ+bt8qA1qtXT/y3lpaWqOY0ceJEAK9U7yIjIzFx4kTUrFlTVBY1MzODgYGBmhKUJlBVl2rVqhWOHTuGJ0+eiEprJPHVV19h8uTJmDZtGrZv3w5tbW1REa880NHRwciRI5GamoodO3agRo0aaNWqFQDA0tISxsbG2L9/P7p06QI/Pz8oFAqkpKTg6NGj0NPTQ82aNTVmm7a2tqi85e/vj48//hhLly6FlpYWAgIC0KxZMwBQU/378ssvMWTIEOjq6mpEsRUAYmNj4ePjg379+mH48OGoXr06ACAuLg5HjhxBfn4+dHV11T7TpEkTaGtr4/r16/j22281qlaYnZ2N7t27o0mTJhg1ahRu376NH3/8EUuXLkVxcTF27dqF6tWrY+XKlaKqKElcunQJaWlpOHz4MB4/foyePXuKarllSVFREcLDw1GvXj0MHToUurq6IImaNWtiypQpKCoqwqJFixAbG4svv/xS7LdNmzbFp59+iszMTI2qxSn7rbIPKp9VzZo10atXL5DEwoULERQUhKioKDRu3Bj9+/eHlpYWFi9ejPz8fAQHB2vMvrS0NPTu3RuFhYWoUaMGPvvsM5iYmKBFixaoW7cuatWqhdzcXDXbldSpUwejRo1C48aN0aZNmzK37eLFixgyZAj69OmDiRMnQktLS+zDN27cQJ8+feDo6Iji4mJoa2tDS0sL9vb2iI6ORlRUFPT19TU6prxOy5YtMXbsWMyaNQsrV64ESbi6uqqNPcp28Pnnn0NXVxcymUzjdpHE2LFjkZKSgvHjx4tzXWFhoTi2pKeno1atWlAoFKX+jcqVK6Njx45lbptcLsfvv/+O27dvQ6FQoGvXrvjmm2/w1VdfoWPHjjhy5AgaN26M4cOHQ0dHR03ROzMzE7GxsTA0NCxVJfpDIDs7Gz169ECTJk0wa9Ys1KtXT7xHderUQePGjVGzZk2x7yr/v1atWvj8889x//59ACiXNULt2rUxfPhwFBUVYe7cuQCAfv364eOPPxbfEx0dDUEQ0KRJE8THx+PQoUM4deoUNm3apLZGk6g4KJXS69WrB29vbzRv3hxXr15Fw4YNUaNGDQBA9erV4ezsDACYOXMmAGD27NnlprKck5MDDw8PfPrpp+jRowcaNmyIp0+fom7duqL6NwBxvb9o0SIAED8DlE8fkXhP+Kfep2qhZuUJ5YQJE8TdFk3XPrx//z4vXrzItLQ0tV2RGzdu0NjYuIQ6pnK3JzExkX5+fjQ1NRVPBssD5amQt7c327Rpw7Zt23LGjBni62fPnmW/fv1obW3N8ePHc+zYsezZsyd79OihVoeyrFHd0VbdebewsKC5uXmpJ5SqaCqELzY2VgzfU4YiKe/DwoUL6enpSblcXiIHNTs7m9bW1pw1a5ZG7FKSnZ3Njh070tfXl6mpqeJ1pQCVmZkZ27VrJ54YFRUVlSqMogx30RQ9evRgx44duW7duhInlCQ5fvx42tnZic9eGY7j6enJqKgojdmVk5PDQYMGccKECYyOjubFixfF01mlndnZ2eIJZUBAgPjZhw8fMiwsjC1bttR4nbyzZ8/y9OnTYhFwW1tbCoJAOzs7CoKgVmz6bf2zLMdDhUJBd3d3MRRZ9W9fu3aNpqamnDJlSqkhfAsXLlQ7ndQ0r7f5S5cu0cPDgw4ODmq1/FTHodOnT7Nz5868ePFiudh47do1duvWjR4eHiWUshctWkRjY2MxJL28Tg1kMhn79OnDbt260dLSkqamphw2bJgYVp2cnMxevXrR3Nyc33//PfPy8kTbbty4wUmTJtHa2lqjtZwrMnl5ebS1taW7u3sJMbTs7Gz26tWLfn5+lMlkpZ7qubq6snfv3szPz9fIM8/NzeW6des4ZcoULliwgDdv3qRCoWBeXp6Yo7Z69Wpxfnjw4AE7d+5MQRBoYmLC9u3b093dXVLPrMAUFhZy9OjR9Pb2ZnJystqaWfU9SmQyGTdu3EhjY2NOmDChXE77CgoKOGzYsLfWM3096kHZPpcvXy6ORxISSv6xM0mWdCg7duzIoKAgNTVITZCcnMwWLVpQEARRTj48PJwHDx5kYmIi27RpU0JAhPxrIXD37l1GRkaWS5jX699x/fp13rlzh2PGjKGtrS2nTZsm2nXt2jWuXbuWrq6uHDp0KOfMmSMONmW5GM3NzWVoaCjT09NFG5V25ubm0snJif369ePs2bPfGPKqSe7cuUNBEBgSEqLmjJFkQkICLS0t+f333/Px48e8c+eO2mflcjmtrKw4duxYjdn3PpTIUTqM/fv3pyAIdHBwUHMolf+/Y8cOtm7dmqNHj2ZqairPnj3LsLAw2tjYaLTQsFJkSqmYqSxB4+HhwfHjx3PXrl2Mi4tjcnIy165dS1NTUzEXmnxVSFnZfsuTtLQ0JiQkcN++fRwzZgxbtWrFadOm/S2HsiyRyWTs1q0b27VrJ4rD3L59m6ampgwLCyvhSCoUChYVFTE5OZkTJ07U6Ngnl8vVNlhI9bb/JodSoVDwxYsXHDNmDN3d3ct1oZKQkEAHBwe6u7uLDqWyBM0vv/xS4jdokpycHDo4ONDb25vnz5+nXC5nfHx8CfGh+/fvc/jw4TQxMaGNjQ09PDzo6urKHj160NHRUaNiRRWdK1eu0NzcnO7u7kxKShKfnVwup6urKwVBYMeOHWljY8NWrVrR19eXkZGRvHLlCm/evMmtW7dq7P7JZDI6OzvT3t6ebdq0oZGREbt27SqW9Xnx4gUjIyNFh1I592dkZHDz5s1cs2YN//jjD2khX8FJS0tjjx49uG3btv94IKAUe3r+/Dmjo6MpCALDwsI0buP9+/fp5OTEAwcO/Me5S7U/zJo1i4IgcM2aNVKIq4QaWiT535xoKkPViouLERYWhvj4eKxevVqjYRfPnz9HUlISEhMTkZiYiPPnz+PevXuQyWRiuFTPnj0xbdq0Ep99vZCqJgu/KkNq5HI5zp8/jxo1aqB+/fqoX78+MjIyMGPGDFy8eBGdO3fGpEmT3vh3ytrGPXv2YOrUqTA1NcWCBQtQu3ZtAK+KYbu6uqJq1ar48ccfUbduXaxYsQJr165FmzZtEBgYiG+++abM7HgT58+fR3BwML799ltMnz4djRs3FkNXvb29kZ2djS+//BLJycnQ0tKCnp4evv32W3Tt2hUff/wxnj17BgcHBzHkqyxRhk69ePECNWrUgJWVFUJDQ1G9enW1MLP4+HgsXboUV65cwbBhw9CvXz8A5VvIt6CgAC4uLrCzs8PVq1fx8OFDeHt7w8vLS2xPBQUFmDdvHg4fPoy0tDTUq1cPVatWxfz58zUSvpyfnw+ZTIY6depg7969mD59OvT19eHq6oqHDx8iNjYWKSkpePz4MUiicePG0NLSQlZWFrKysmBvb4/FixeXuV2vQ5WwVb7abIO2tnaJcNaIiAgcOHAA3bt3x5AhQ8o13Cw7Oxu9e/dGQUEB+vTpg2XLlqFHjx4YN24cqlatqvbeR48eISwsDNbW1vD19QUAtfZaVuTn58PDwwM6OjowNzeHr68vatasiY8//lhtHLt8+TJmzZqFrKws+Pn5wcXFBXK5HOHh4fjll1+wZcsWCIJQprb9J27evIlRo0ahbt26aNSoEQ4cOIBZs2bB0dGx3GxQKBSYNm0a7t69i+nTp6Nhw4ZqY39eXh5u376NmjVr4quvvkJubi5u3LiBgwcPIicnBzo6Omjbti3Mzc0/6NDHoqIixMTEYPz48WjcuDGWLFmCatWqwcXFBbq6uvDw8MBHH32Ehw8fIi4uDvfv30daWhoAoH79+tizZw9q1apV5nYp54/GjRsjLCwM1atXR2JiIsaMGYOvvvoKGzduBAC8fPkSS5cuxYYNGzBmzBj0798flStXLnN7JDTHb7/9hqFDh+LXX38V01xehyTWrVuH+fPn49y5c6hWrRpevnyJI0eOoEWLFhpfbynXgmfOnEHVqlVLTQsiienTp+PcuXPYtWuXmLa2YMECODs7l8uaUOI94t94osqdiaKiIo2fFpS2eyKXy5mXl8fLly9z27ZtHDFiBAVB4MKFCzVqy9tQ2imTyeji4kILCwsaGxvTw8ODV65cIUmmp6dzzJgxtLOzY0REBG/fvs2NGzcyJSVFIzYpTyMKCwu5bt06dujQgf369RN3zRwdHenu7s7Hjx+r3efly5ezdevW9Pf3LzcxpbNnz9LGxoaenp6UyWS8fv26eDp18uRJ/v7779y0aRNDQkLo6Ogonm7169dPtL2sQ63flxI5Sp48eUIbGxteuHCBGRkZ9PT0pJ2dndoJpdLmlJQUHjt2jDdu3NCYYIdcLhdVY8lX93PLli00MDBgZGSkeApdWFjIixcv8siRI5wyZQr9/PzYoUMH8SRTU2ITqampf1vpVPX+hYeH09rammPHjtXYacHdu3e5fPlyhoaGct++fWI/lMlk7Nq1KwVB4KBBg8SoENX+m5qaSnd3d3bp0kXj6Qfnzp2jIAgcPHgwu3XrRisrKwYHB/PKlSslShrExsaKJ5TKerampqYarSP5n0hISKC9vT319fW5ePHid2JD3759OX/+/BJqt4sXL2bv3r1paGhICwsLhoeHl7sy7/uEQqEQ5xEPDw926dKFHh4eJdYoRUVFzMzM5B9//MHly5drLHRUWavby8urxBi2Y8cONQVZkszKyhJPKNetW6cWbfCuajpL/GeU6+HffvuNxsbGYvRSaSd4xcXF3LdvX4nUA00/X+Xf37dvH5s3b/4fI8+2bNlCQRAYHx+vUbsk3n/+lTNJaj4EKDExkTt37mRYWBhXrFjB2NhY8bXXQ7bS09O5aNEisTh3eaIMJyNfhRr6+PjQy8uLJ0+e5LJly+jo6EhHR0c1h3LcuHFi/avu3btr5F7KZDJ6eHjw8uXLJF89r7Vr19LOzo79+vWjvb09+/Xrp+bIqt7XBQsWsEOHDuWmGqdcCLRu3ZpOTk40MzNjWFhYqTma2dnZTEhI4O7duzW2WH7fSuSQZFJSEkePHi0u4jMyMti3b1/a2dlx/fr15armqaR169ZquZhyuZybNm2ioaEhQ0JC3qha/PjxYyYkJGhM9TYjI4OmpqYcN27cf+VQjhkzhr6+vhrpu7GxsbSxsWHr1q1pZmYm1vJSOq45OTns3r07bWxseODAAbU+kpqaSg8PDzo6OorXNf3cR4wYQRcXF2ZlZXHevHns3r07BUHgwIEDuXnzZr548ULsI0qVV0EQ3rkjqeTmzZui46GaQ6npBV5hYSGfPXvGli1bikq8GRkZPHv2LHv16kVBEEQ1cKWStrKmcnmUr3gfUc4jDg4OFARB7Xkq22B5jIO5ubns0KEDhwwZwszMTPF5Kf//6NGjNDMzK1GUPisrSwwpLC11R6LiUFhYqDb3Jycn09DQkPPnzxevqfZN5b+fPHlSburVr7f1U6dOURAEMZT/9deVNl64cKHEZoeERGn8a2dSk1y8eJEdO3Zk165daWdnRxMTEy5btqzETrdqR3369CkXLFhAAwMDRkREaNzG123Jz8/nzZs36e/vrzaB7d27l87OznRwcBAdyoyMDO7Zs4fr16/XiNiO6o7os2fP1CaytWvX0t7enmZmZmJx69Lk+klqXOzkdZQLAUdHR7Zo0YI3b94UX1Pep9IW75pyKCtqiZzc3FxGRUVx3LhxDA0N5alTp8RdbGW7VArsvMmh1PRmUFFRERUKBXv27FkiF0QulzM6OpqGhoYMCwtTcyjLyz6SXL16NQ0NDTllypQSOX9vorRaiWVpq7L8R3h4OOPj41lQUMBp06bRwMCA9+7dU4uAcHBwYNu2bXngwAExSsTd3Z1du3YVHUlNnkyq7si3adOGR48eJflqLD548KAoWtS7d2+1PNNbt25x4MCBJfKf3yXKHMrSRHnKmtcXcJMmTaKRkRGnTZvGQYMGsUWLFrSxsWF4eLjayff06dPp4uKiJkQmURJlpEvbtm3Zt29fJiUllbsNmzZtoiAIHDdunHhNVfBu8uTJbNeuXalz7IsXLzh//vwPVkzpfSA/P5+DBw/m4sWLxeeamZlJf39/2tjYqDmKr88P+/fvp42NjcbrM+bn59PHx0dtUzsnJ4f9+vVj69atxYME5XikOqZs376d7dq101jUnMT/DhXWmbxy5QqbN2/O8PBw0Zm4fv16qQvz1ztpeno6IyIi2KdPH41OtpcvX6a/v7/aqUlQUBAFQaCNjU2JDrhv3z46OzurnVCq2l6WCz6lI+nt7S2q2qlSVFTE1atXi86mcrFS2iL5XVBYWCiGKr2rhcCbeJOicWkO5Ztq1P1bsrOz6ezsTGdnZ/bv3592dna0srJS25h43V6lQ2lvb8/ly5eXqxBVaGgo3d3dKZfLSygJqzqUynuoSV5XAibJjRs3UhCENzqUqpsISodd9fOacCRnzJjBnJwc8frvv/9OExMTXr16leRfbUvpUNra2nLTpk3l6kiqohQY8ff3F6+lpqayefPmDA4OZq9evWhoaEgrKyv6+Pjw5s2b5WbbPyEhIYFOTk7s2rWrxpRl8/LyOHz4cG7fvl28Fhsby1GjRtHAwIDm5uYcM2YML168WKKW6OTJk9m/f3+N2PW/gmq7Us4jffr0Uaurq0mUY0NqaioXLVpEfX39EptpUVFRNDIy4oULF0iWPoZImwUVG+U83KFDB65evVock8+ePUtTU1M6OTmVqEmsUCiYkZHBsWPH0svLS6O1xMlXIj89e/akra2tWm3w7du308LCgs7OzuJaVbW9ZWRkMDg4mAMHDixxaCIh8ToV0pmUyWQcNGiQ2omPErlczpcvX/Ly5ctqOQ6vD7qZmZmlLhrLkt27d3POnDlq3/HkyRMOGjSIgiBw+/bt4kJAyf79++ni4kJLS0uN7UgpHckBAwaIjqTq6YkyN055Qvk2h7Ks+U/PQqFQqE2q72Ih8Hd4lyVyioqKOGzYMHp5ealtZHh4eNDJyanUsiNKe58/f04nJyc6OztrzHF7/RmS5I8//khzc3NxslVtB6oO5ZQpUzR+El5cXMzCwkLxWSl5k0Op/C0FBQXct28fx44dq7Gw78uXL9PQ0JBz5swRC5grv3/nzp20s7Pj06dPS3xOJpPR0dGRgiDQxcWl3B1J5fccPXqU+vr6jI2N5d27d2lhYUF/f3/m5uYyNzeXJ0+e5OjRo2lmZlahT1yuXbvG3r17a2xH/tq1a+zYsSO7d+/OPXv2iNfz8/OZnJz8xrDutLQ0BgQEcM6cOSwuLpacjf9H9T4ox7r79+/z4MGDJF/NI23atKGXl5fGT4JevnxJLy8vPnz4kOSrze0FCxZQT09PdCiXLVtGQ0ND0T6J9w/lmCeTyejt7c327dtz5cqV4hx39OhRNm/enG3btmVUVBTT09OZm5vLU6dOMSQkhBYWFiVK2WmKCxcucODAgbSxsVErjRcVFUVLS0va2NjwxIkTYpv9888/GRISwlatWlWoyBGJikuFdCYfP37Mjh07lhhok5OTOX78eLHmmyAIjIyMfOvfKo/JNjc3l+Hh4WIidXp6Onv16sV27drxxIkTJU6ntm/fzgkTJmjEaVM9kXz8+DHJvxajcrmc/fr14759+8TFpqpD6e3tXS5lF15/JqXlE+zZs0dcZClDlby8vCrUAvRdlch5/vw5nZ2dxVwa5aR28uRJCoLAM2fOvNXe58+fi5NGWZOdnc3AwECGhIRw06ZNPHToEB8/fszdu3ezQ4cOYptUonzeubm53Lx5MwVB4PTp0zV2Kn7r1i3Onj2bLi4udHJyYkhIiJpj+CaHUi6X88iRI2zXrh3t7Ow00neTkpJoYGBAFxcXcWNH2U+VpXF69OjBY8eOcffu3YyLi2N+fr54r2QyGUNDQzXqSBYWFjI1NZW///477969WyLqITk5mc7Ozhw8eDDNzc05dOjQEjWBSZbLCfS/RVNRBUpiYmLo7u5OR0dH7t69u9T3qG6sPHnypFzK91Rk8vLyuGXLFoaFhTEkJIQ7duxQa1vKfpmcnExjY2MGBASITveff/5JIyMj+vr6aqxOskwmY+fOneni4qImaKZ0KJX6CEZGRjx27Ng7yV+XKHtevnzJAQMGsH379lyxYoW4oXvmzBkOGjSIenp6NDU1pbGxMe3s7Ojh4VHutULPnTtHHx8f2tjYqG1gbd++nR4eHhQEgdbW1rSxsWHHjh3p4uLyQZcZkvhnlH0NhTIgLy8PL1++RH5+PgCIpQNmzZqFzMxMWFpawt3dHY8fP8b69evRtGlTuLu7l/q3Xpc71gTnzp1DdHQ0UlNToaurC3Nzcyxfvhx+fn744YcfMGXKFNjY2IjSym5ubnBzcwNQtuU/5HI5fHx8UFRUhFWrVkFXVxeFhYXQ1dVFQUEBevXqBS0tLbRq1Qq6urpiuQpvb29oaWkhKioKkydPxtKlSzVSxuLOnTs4cOAALl26hI8++ggdO3ZEnz59xO/i/8tTHz58GBMmTMDw4cMBAFZWVpg9ezYGDhyIjRs3YurUqWVu23+DsjSOjo4OJk6ciNzcXMTHxyM3NxfVq1fX2PcWFhYiKSkJBQUFACCWeWjQoAGAV+3gbfZ++umn+PTTTzVi2759+3D//n3k5eVhz549AICaNWuiuLgYMpkMq1evRsOGDWFpaYlatWqhXr160NXVRZUqVeDp6YlKlSqhRYsWGml/Fy9eRHBwML777js0a9YMtWvXRnx8PO7cuSOWU/Dy8oJCocCMGTOgUCgQFBSEunXr4uTJk5g2bRrq1q2L7du3Q0dHp8zLvdSvXx8tW7ZEcnIytm7digEDBqBatWqIj4/HgAEDkJ+fj/r164v9AgC+/PJLGBsb46uvvsKgQYMwffp0AJop/5GTk4PJkycjISEBiYmJqFSpEjw8PBAQEIDPPvsMANC4cWM4OTlh3rx56Ny5M2bNmoUqVapAS0tLrdRKjRo1ytQ2TaAcrzVFy5YtMXr0aMybNw+rVq2ClpYWevToIb6+cuVK/Pnnn/Dw8MDt27dx/fp1XLt2DatXr8ZXX32lUdsqItnZ2fD29kZBQQF0dHTw9OlT5OXlwdLSEk2aNAFJ6OjoICkpCS4uLnBwcEBYWJjYR1u1aoW1a9fis88+g66urkbsc3Z2RpMmTTB79mzUqVNHnNPq1q0LT09PAMDOnTthZmaGTp06AdBsmTKJsiU/Px+HDx/G7du3xRJNenp6qF69OpYsWYLhw4djy5YtAAAfHx+0bt0agiAgMzMT58+fR0FBAYyMjNC0aVPUqVNHIzbK5XL88ccfuHPnDqpUqQITExM0b94crVq1gra2NpYuXYo5c+YAAHr06AE3NzfY29vj7NmzSEpKwsuXL2FhYQFDQ0N8/vnnGrFR4n+P/7rOpCZ5+vQpgoKC8OzZMxgaGiIxMRG3b9/Gd999B2dnZ7FeWn5+PoYNG4ZmzZph4sSJ5WZfaYvI3bt3Y86cOTAwMMDQoUNhbm6OjIwMDBkyBJmZmZgyZQpat26t0QXKzZs3MWXKFDx69AijR4+Gi4sLgFf3qXfv3qhWrRoWLVqkVodM+VuKi4uxdetWtGnTBl9++WWZ23bx4kWMGDECTZo0Qa1atZCUlIS2bdti8ODB4kIUAA4ePIgxY8Zg/Pjx8Pb2FidZhUKBGzduQF9fv8JNvKr3MDMzE3Xr1tXo92VnZ2PEiBGoVasWxo8fj3r16kGhUODBgwfo1q0bli9fjvbt27/RTk2iXBjl5+cjNTUV6enpiImJQUFBAVatWgXglXOZlZUF4JUz1KxZM9SsWRPOzs6wsrLSiF0JCQnw8fGBs7MzBg4ciIYNGwJ4VW9T2SePHz8OGxsbVKlSBT/99BNmzJgBd3d3GBoaYunSpfj000+xc+dOVKpUqcydNeV9KygoQHBwMK5fvw5fX18YGRnB19cX3bp1w8CBA9G4cWPcunULGRkZOHr0KBITE3Hp0iUYGRlh+/btGnu+2dnZcHNzQ926deHg4AAbGxvExMRAR0cHrq6uau9NSkrC6NGj0bBhQ8ydO1eqk4dX7ezixYu4desWiouL0bRpU7Rr1w46OjqIjY3F7NmzkZOTAz8/P9GhnDt3Lg4fPoyHDx+iXr16aN68OYKCgj7I+m65ubnw8PDAp59+ipCQEBgYGCAvLw9JSUlqdXGLiooQHBwMXV1dTJ8+HdWqVSsX+5SOZOPGjTFnzhx8/vnnavNCYWEhPv74Y6SlpWHr1q1Yvnw5evfujYiICACSQ/k+kJ2djcGDB0Mul4uHHS1btkRYWBhq164NHR0dyGQyDB8+HMnJyejbty+8vb01vin1uo1+fn7IzMzEixcvkJmZCU9PTwQGBorrrJiYGCxduhR37tzB+PHj4ezsXG72SfwP8w5PRUXS09N58+ZNxsXFieIWf/75p6g+aWtry6VLl5Ya4ujh4cHw8PBys1UZOiaXy3n37l21ePIdO3bQysqKgwcPFkuYPHv2TKwRplrWRFNcunSJvr6+bN26NQ8dOkSS7Natm1hH8nWys7P5xx9/aNSm69ev08LCghEREWLoYF5eXgl7srKy6O3tzeXLl781zLEihgaVt1hRbGxsCUnxxMRECoLAX3/9VbyWm5vL3377Ta1WWXlQ2v0YNGgQg4ODeePGDf78889csWIFfX192blzZ5qZmWksfyQ3N5ejR4/mqFGj1FQxVXNLg4KCaGxszCVLloh5zj/99BMNDAwoCAJ79eol9n1N5SEq27VcLqefnx9btWpFIyMjhoSEkHxzyP6VK1c0qn4rl8vp7e3NgQMHvlHtdtWqVaJaoEKhYGhoKK2trcX8tHcp5vWukclk9PLyYrdu3WhpaUkTExP6+vqqtfeYmBi6ubmVCHlNS0vjrVu3KJPJyr0PVyQWLVpEDw8PUYBPtS+8fPmSZ8+eZUxMDIuLi5mSklKugiEymYwdO3YsVaNALpfTy8uL0dHRos3p6elcuHChWg6lRMUmJyeHjo6OHDBgAC9fvsysrCw+efKk1DDQrKws9u/fn+3bt+eqVavKrTRTdnY2HRwc2L9/f164cIEymYx3794ttazH2bNnxZDX/fv3i9c/5HFa4t/xzp3Jy5cvs1evXrSysqKRkRGdnZ1Fp/HJkyd88eJFifwu5aAcHx9PV1fXN+ablDXKwUAmk9HHx4dWVlZs164dJ0+eLL6nNIcyLS2NEydOLDcn6MKFCxw8eDBbt25Na2vrUoslk68GH2XdMk0VXS8oKODEiRMZHBysJvxDvrqPDx484O7du3n48GEWFBTw+fPnGstn+V9FeT/j4uLUaqrJZDJOnDiRXbp0KVW0pSwoKiqiTCZjTEwMb9y4UcLZUG3zgwYNoqenZ6mva1LR7unTp+zQoQOjo6PFa6qTZkBAANu0aSNuXi1cuFB0NDds2EAPDw+NOJKqpR1URX6Urw0fPpzGxsZcuHCh6EiolhB63bnU1Phy+vRpOjs78+zZs+I11T66bNkyMYddqU76+PFjmpubc9KkSRqx6X0hJyeHzs7OHDBgAM+cOcNnz57x8ePHavdSiapDuWvXLvG6tMAj/fz8GBoaqnZNLpdz2rRptLe3F9ufs7OzONaVh16CXC5n165d6eDgIDqwqo6ko6Mje/XqVSK3OD09nYsXL6YgCJw2bZrG7ZT471EoFJw5cya9vLx4//79Ev0xJyeHMTExTE5OFtdZSoeyY8eOjIqK0viapri4mN9///1ble9v376ttpZWOpTt27fnzz//rFH7JP73eac5k7GxsRg0aBAcHBzQt29f3L9/H7t27cKQIUOwb98+MRyzZs2aAF7lUlapUgXAq/y7adOm4aOPPkL37t01biv/Px8jJycHvXr1Qo0aNeDn54ebN2/iwIEDKCoqQmRkJHr37g0AmD9/PlauXAkfHx9YW1tjxowZAMo2nEUZOnXv3j1UqVIFTZo0gYWFBSwsLKCtrY0VK1YgJiYGdnZ24r1Uht7k5ORg1qxZOHr0KHbu3InatWuXiU2vU1hYiLi4ODg6Ooo5Atra2rh37x5mzpyJS5cuIScnBwDQrFkz/PTTT9DV1RVzTSTw1nvB/89BA4Bnz54BAOrUqYPCwkLMnj0bBw8eRHR0tFoocVmRk5ODGTNm4Nq1a7h16xa0tbVRtWpV9OjRA126dIGFhQV0dHTEvF0DAwPs2bMHL1++RLVq1dR+k7KPa4IHDx4gPT0dBgYGANTDfQMDA3Hp0iX8/PPPqFu3LkaOHIlt27ahZcuWsLGxwYABA9C/f39oaWmVaWjrlStXsHjxYrRq1QpdunQRQ8uV+c0fffQR5s2bh+DgYOzcuRO6urrw8fHBJ5988sZwZU2FyV24cAF5eXlqIcjKnLMff/wRK1aswIQJE3D79m2EhYUBeJUXbmpqirNnz0Imk5V43h8CCoUCixcvRvXq1TFlyhQ0bdpUfK1+/foA1HNbW7ZsiVGjRmHBggVYt24dioqK4ObmpvHQ9IpOYWEhCgsL8fLlSwCATCbDxYsXsWDBAty6dQtfffUVQkNDkZ2djT179mD8+PFYt25dubS3pKQk1KlTBwkJCbh+/bqYl1ZQUABXV1dUr14dCxcuLJF7VrduXfTr1w+6urpi7qRExURLSwvXr1+HIAj4+uuvxetFRUWIiorCyZMncevWLVStWhVWVlYYNmwYDAwM8OOPP8Lb2xtHjhyBl5cXatWqpTEb8/PzcevWLdjZ2aFJkybi9eLiYixZsgR//vkn4uLiULt2bVhbW2PevHmwtraGjo4OZs+ejVWrVsHe3h5Vq1b94MZpibLhnTmTsbGx8PHxQd++fTFixAhUrVoVwKscqqlTp2LHjh0YOHCg+P7g4GCQRPPmzZGYmIirV69CW1sb27ZtUxNC0RRaWlpQKBSYNm0aGjZsiMjISNSvXx+nT5/GlStXcOzYMRQWFmLu3Lno3bs3tLS0MGnSJHz99dewtrYW/05Z2ZidnY3AwEA8evQIz549Q1FREXR1dWFpaYnw8HCYm5sjMDAQS5YswZo1a1CrVi24urpCW1sbMpkMc+bMwd69e7FlyxZxka0JFAoFqlWrhsePHwMA7t27hzNnzmDRokXIyclBhw4d0Lt3bzx9+hRr1qxBREQEFixY8MEPaHK5HE+ePMGXX35Z6r1QtvfCwsISORnPnz/HunXrNPp8s7Oz4eHhgdq1a6Nbt26YN28ebt++jatXr2L9+vU4e/Yshg8fDkdHR9Hx+Oabb5CRkYHHjx9DEAQAf/UHTT7vTz75BAUFBUhNTYWZmZm4OH/x4gUsLS0RHByMBg0aQEdHB97e3hg4cKCaPUrxmLJwJEmiqKgI8+bNw4ULF1BcXIwff/wRffv2hZWVFWxtbcXn+dFHH2Hx4sUICgoSRR0GDhyIKlWqlOtmS2ZmJj7++GNxDFTev6NHjyIqKgoLFixA165dkZycjHv37mHFihVwdnZGYGAgPv30U42KUVVkiouLERcXBwsLCzVHUhVlm4qOjkavXr1gbW0NXV1dTJ48GTt37kTXrl3LLe+vIlFQUICXL1+ibt26qFSpEvT19bF161Z4eXkhNzcXCQkJaNCgAYYMGYJBgwaJgmIff/wx9uzZA5lMVi7t7ttvv0VISAjmzZuHoUOHYsOGDTA2NoaLiwuqVauGhQsXihsHSnJycnD16lU0b94c/v7+H/xcV5EhiczMTKSnp6Nly5YAXs0b8fHxWLBgAW7cuIH69esjMDAQz549w4kTJ7B//34IgoDq1avjp59+QlZWlkYdSeCVM/ns2TMUFxcDeDU/37x5E7NmzUJ8fDwaNGgALy8vPH/+HCdPnsTcuXMxduxYWFpaIjQ0FPXr1/8gxxmJsuOdOJNxcXHw8fHBwIEDERAQgKpVq4qLo44dO2L+/Pl48eKF+P7c3Fx899132Lx5M/744w98++23aNWqFcaNG6cRMYw3oRQ5adu2LerVq4eXL1/i9OnT+Oabb9CyZUvs27cPkyZNwvTp09GrVy989tlnsLGxKXM7srOz4eLigvr162PSpEnQ09NDVlYWtmzZgkOHDmHgwIGYOXMmzMzMMHToUCxbtgzz5s0DALi6umrckXzy5AmAV7vvn3zyCfT09HDgwAFcuXIFmZmZyMjIgLm5OZydneHh4SF+7sGDB7h27ZqaKMqHiEKhwIABA6ClpYXw8HB89913AP46oVQ6ksnJyZg2bRq+//57NGrUSJywpk+fjrt372rs+RYUFGDEiBGoW7cupk2bhi+++ALa2tpo1qwZHB0dYWFhgdDQUERFRaFWrVpo3bo1AKB27drQ1tZGenq66EyWNapOltLxqV+/Pr755hvs3r0blpaW4iltrVq14OXlJY4d+fn52Lt3L4yNjWFpaan2d8tqwaelpQVdXV14e3sjPj4eXbp0gZ2dHdauXYvNmzfDxsYGnTp1QteuXVGlShXRoQwODsa2bduQk5ODESNGlEv/ULazjz/+GOnp6cjKykLNmjXF+2pvb4+tW7fC1NQUANCkSRPk5eWhWbNmqFy5MszNzTVuY0UmKSkJt27dQlBQEIDSFXZJ4sCBA4iIiECVKlXg6uqKli1bIjIyEp999tkHucArKCiAm5sbjI2NERgYiIYNG2LEiBF4+fIlrl69iry8PLi7u6N///5o3LgxKleuLN5buVwOkhpRa30dZcSFoaEhxowZg9mzZ8Pb2xvVq1dHo0aNMHfu3FIdyR9++AExMTH4+eef8fHHH2vcTon/Hi0tLdSuXRtWVlZYvXo1Hj9+jIcPHyI+Ph41atSAt7c3AgICxM2MKlWq4Pfff8fYsWNRVFSEatWqlUsf/uSTT/DFF19gx44dePr0KZ48eYLz58+jSpUq6NOnD4KCglCnTh0UFRVhwoQJSE5OFufKFi1aaNw+iQ+A8o6rffjwIfX09Ni9e3e13AZlLtCjR4/YqlUrLl++vMRnnzx5wqSkJLXkek3mIb7+tzMzM9mqVSuuWLGCJLlixQq2bNlSrBfUsWNHCoLAzp07q+WOlWWelbKOZP/+/UsI2GRnZ/PAgQO0s7Ojg4OD+HpMTAx9fX3Zrl07urq60tTUlNevXy8zm5QoFAo+fvyYxsbG/P7778V7oMxt8fHxYd++fRkdHa1Wg0t5f8LCwjh06NAyt+t95NixY2zevDn9/f158+ZN8boy9yIxMZEtW7bkmDFjxHy6u3fvUhAEmpmZqX2mrImJiaGTkxOPHz8uXlPmkSjzlH777Tfq6ekxJCRE7EcZGRnU09PjkiVLNGZbbm4uc3Jy+OTJE7U6gZGRkRQEgZs2bXrj+BEfH08XFxcuXrxYY/YpSU9P58CBAzlhwgSSr+pIbtu2jS4uLhQEgU5OTty0aROvXLlC8i8hD39/f43mgin/tuqYFRcXRyMjI06fPl28VloNxtOnT6vVBy6PnLWKTEZGBlu0aMGlS5e+9X1Pnz6lpaVlubS794WwsDDq6+tz5syZTE5OJvlXfvbr+YdKnj17xuHDh3PSpEkaE8nKy8vj5s2bGRoaytGjR4uiU+QrobmgoCAKglCiRjb5an4OCwujmZkZr169qhH7JDTD8+fPGRISQkNDQ1pbWzMkJITXrl0Tc+uV8/K8efPo4eHxTmxMSUlh9+7d2bJlS5qZmXH06NG8dOkSc3JySP41poeEhNDLy+ud2Cjxv0u5OpPKYu5Dhw5l27ZtuWDBghKqa4MGDaKjo6P4329bkGhysaJcZObl5XHbtm08dOgQU1JSePz4cV66dInXrl2jnp4eDxw4INrSs2dPBgUFqS2gy5Lc3Fza2dnR39+f6enpagt45b2Qy+U8ePAgLSwsOGjQIPGzly9fpqenJ21sbDReiHbq1Kk0NDRkZGSkuBBQ2qpsA69z9+5d9u7dW6OOxvvGyZMnaWJiQj8/PzXnMCkpiS1btuSoUaNK9J8NGzZovBjy6tWraWVlVWrhedU+uWTJEpqYmIiKx6mpqXRyclJTQC5Lrl27xqCgIHFTZ/To0aL6Y2FhIb29vWlhYcENGzaoLUiLioqYkJBADw8Puru7i31X087Q4sWLaWZmxsTERPHali1baGhoSAcHB+rr67N58+aMjIzkqVOnqFAoSjjtZUlOTg6nTp1KLy8vdu/endu2beOTJ0+YnZ3NwMBAtmjRghs2bBDfr7pgf/78OceOHUtXV9c3Lvb/18nLy2NCQoL4bDIyMtijRw96enqK42BpyOVy2tnZqTnrHyqq4iazZs2iIAiMjIxkSkpKifeqinYlJydz0qRJtLKyKlX1vSyQyWR0dXVl9+7d6ezsTHt7e3p7e/PBgwfie65cucJBgwaxefPm/PPPP8XrSkfSxMREIxu5EmVDYWEh09PTefLkSZ47d67Es3r48KGamJ3qOJyenk5/f3+Gh4ezuLhYY/NHUVERc3JyeOXKFcbHx1Mmk4kO4/Pnz5mUlFRCGV1py+PHjzlo0CAuWrRII7ZJfLiUmzN54cIFenl5iadlwcHBbN26NefNmyfu6gwZMoTt27cXJ4N3XQIiOzubTk5OtLS0pCAI9PT0FJ3H6Ohodu3aleSrCfDatWvs1asXDx8+LH6+LO1XKBRcuXIlBUHg3Llz3/od2dnZnDZtGk1MTHj69Gnx+pUrV/jo0aMys+l1VG2ZOXOmuBAoTY1XVV32ypUr9PDwoKurq8Z2lN8HSnMWVB1K5SbA4sWLGRISouZIlqfi46JFi9i+fXu10hqlcfbsWQqCwJMnT4rXlJNeWRMbG0tzc3OOGjWKP/74I1etWsWwsDA1ZbukpCQOGDCAgiDQy8uLP/30E3ft2sWJEyfSzc2NHh4eZS7jrnyOqgsL5d/Oycmhvb09R48eTZL8+eefKQgCo6KimJeXx4MHD3LUqFEUBIF9+/YtofxalshkMjo4OLBXr1709fWlm5sb9fT0OGvWLJLk1atX6ejoyJYtW3L+/Plqn42NjWVISAgtLCw0eiJekVEoFPTx8aGdnR0vXbokPqvt27eLz1S1v6qOcxcvXmSXLl3EueNDPtFVKBRvnEdUI3FWrlzJLl26MDIykpMnT2bfvn3Zpk0b3rhxQyN25eTk0MnJid7e3oyPjyf5SqW9tE2ChIQEDhgwQHQoFQoFJ0+eLDmSFZzs7GyOGTOGTk5O1NfXp56eHo2MjBgcHFyiDBepHp2RmprKyZMn08rKStzA1JSNYWFhYgSLnp4era2tGRoaKrb9N40fT5484eTJk9mmTRu1DUwJibJAiyQ1HUobGxsLLy8veHl5YfTo0fjkk08AACNHjkRsbCz69OmDy5cv48GDB1iyZIlGBWH+E8rboaWlhenTp+P+/fsYMWIEkpOTsXr1ahQXF2Ps2LHIy8vDyJEjMW/ePNSuXRuLFy9GpUqVsH79eo0JAd27dw/R0dHYvHkzRowYgaFDhwJQV6fk/8fBJyYmokePHhgzZgwGDBigEXtehyQUCoX4+2fNmoV169bBx8cHPj4+Yv5ISEgITp8+DUtLS2RlZSEtLQ01atTAxo0boaur+8EVcC7t96o+01OnTiE4OBitWrVCWFgYGjdu/E7v0datWzFr1ixERUWhTZs2JV5XtkGZTAZra2vMmTMHXbt21Zg99+7dg4+PD7p06YLAwEBRmfhNubdz5szBxYsXcefOHVSqVAlmZmYwNzfH4MGDoaOjU6Y52AUFBdDW1kZmZqaaoi5JFBcXY+7cuTh//jzatWuHFStWYOjQofD19RUFyRQKBe7du4emTZtq7HkrC643adIE06dPx+eff46CggLMnDkT+/fvx5YtW6Cvr4+rV69i/vz5iImJQaNGjdCoUSNkZ2cjPz8fOjo6iIyM1Fgu7PvA1atXERQUhM8//xwTJkxAixYtUFRUhKlTp2Lv3r0ICAiAi4sLGjVqJH4mIyMDc+bMwbVr17BmzRpRdftDR7Xvzpw5E+vXr4ePjw+8vb3RoEEDxMTEIDIyEs+fP0e1atVgYWEBb29vfPXVVxqxJyoqCufOncP06dPRpEkTNYXdJ0+eICEhAdra2jA1NUWtWrVw+/ZtTJ8+HQkJCTA0NERcXByio6Pf6dpG4s2oCso5ODigffv2SEpKQkJCAhYvXoyaNWvC398f/fr1AwCsXLkSu3fvxqBBg3Dnzh3cu3cPt27dwqpVq6Cvr68xG93c3FC3bl1YW1ujdevWyMrKwsGDB3H8+HF88skniIqKQvPmzQG8arP379+Hra0tHj16hMuXL+PatWtYu3atxmyU+HDRuDN5+fJl9O/fHz4+PggMDMQnn3yithAeMWKE2BGWLVsmKma9C5SLSKV9kZGRaNKkiTiA/P7771iwYAEUCgVsbW1x//59HDt2DJ9++im+/PLLcnGGUlJSsGrVKmzfvr1Uh1K5kJfL5ejUqRNcXV0xatQojdgClL5g/08Lgbi4OERFReHZs2do2LAhWrRogUGDBpX5Qv59IDc3Fx4eHvjiiy/w2WefoW3btvjiiy/w5ZdfqiXunzp1CiNHjkSLFi0wbtw46OnpAcAby0SUFUVFRXj8+DFIolq1aqhduzZSUlLQo0cPtG3bFjNmzBAdn9fZu3cvFi1ahOjoaDRo0KDMbVO29XXr1uHMmTOYOnUqGjZsCC0tLWhpaSEjIwOXL1/GyZMn8fHHH6NZs2bo06cPgFcT88uXL1FcXIzGjRuLf7Ms++7NmzexefNmXLhwAc+fP4eFhQWGDBkCMzMz0faUlBQ4OzsjLy8P48ePh6enp1j+SPU3AqULuPxbsrOz0aNHD3zxxReYM2eOmjNz+/ZtuLq6IigoCH5+ftDS0kJaWhoSEhKwf/9+FBQUoGrVqujYsSNMTU1LlD/4EElISMCQIUNEcbTmzZvj0aNHWLp0KXbu3AkrKyt07NgRrVu3xrlz53DhwgXExsZizZo1Yp/+kMjNzcWIESPQpEkTNGvWDF9//TVMTEygq6urJqIzZ84crFmzBj4+PhgwYAAaNmyIwsJCZGRkoEaNGiXeX9YMGzYMVatWRWRkpDg+KMsvnTp1CsnJyQBelXcZO3YszMzMcOPGDURGRiI+Pl7ckJGoeBQUFCAoKAh5eXmYPn06GjVqpCa4du3aNYwcORJyuRwTJ06Eo6MjoqOjsWvXLty6dQv169dHy5Yt4e/vr1Y6pKxtDAgIQHFxMaZNm4YGDRqozQXbt2/HypUrkZOTIzqLq1evxsqVKyGXy1GnTh2YmJggKCgI33zzjUZslPiw0eiq/erVqxg4cCAMDQ3h7e0tnkiqlvJYtGgRxo0bh9OnT+OPP/6Anp4eqlWrpvFF8usoFApUqlQJ2dnZCA0NRXp6OhISEjBp0iTxPW3btgUALFy4EOfPn0f79u0REBCAp0+fol27duXiDDVu3Bi+vr4AgEWLFgEAhg4dCm1tbbWF8PXr11G1alUYGxtrzJb4+HiMGTMG3377LYyMjNCkSRNYWVnh448/Fp3JkJAQVK5cGStWrAAA9O/fH2ZmZli5ciVyc3PV5NuLi4s/KEcSAC5duoQ7d+7gzp07AIBDhw4hJycHn3/+Ob777jsYGhrC3NwcTZo0EesOrlmzBgMHDoSBgYFG+0hOTg4mTJiAO3fu4NmzZ2jatClCQkLEsjNz585FgwYNMHbs2BLPLSsrC2fOnMG3336rMYl+5YR/+fJl5Obmiic+xcXFuH79OsLDw3Ht2jVUqlQJH330EXJzc5GamooxY8a8UWWvrBzJixcvYtiwYWjevDnatWuHL7/8EidPnkReXp6a7Q0bNkSfPn1w5MgRmJiYqDmSqu8DUOZ9Qy6Xo0+fPqhRowbWr18vXldVca1atSqqV68ulkepV68e6tWrh/bt25epLe8rfK1Ei76+PlatWoUhQ4YgIiIC33//PczMzBAWFgZjY2OsXbsWs2fPhkKhQJ06daCvr48NGzagWbNm7/BXvDt27NiB33//XfzvOnXqQC6Xi3NJo0aNYGdnh3HjxkGhUGDz5s3Q1tZGnz590KRJkxJqqWUNX6UC4fHjx/j666+ho6ODtLQ0xMfHIyoqSqxzOWHCBOjq6mLx4sVYt24dFi1aBAMDA4SEhKBu3brSiXMF5u7du0hNTUVgYKC4sahcfxYXF8PIyAhRUVHw8vLCrl274OjoiD59+sDNzQ1JSUmimrkmlXlv3LiBp0+fYvTo0eL3qUaCubu7Q1dXFzNnzsSKFSswe/Zs+Pr6wtnZGY8ePUKDBg1QvXp1cQ0uIVHWaGzlrqwjWa1aNdy5cweHDh1Cr169xAWcqkM5Z84cjBgxAjt27ABJ+Pr6onr16uVWS43/X/hdLpejX79+yM/Px5dffon8/Hz89NNPaNq0qShz37ZtW5DEkiVLsGnTJtjY2MDOzg5A+TlDTZo0wZAhQ6ClpaXmUOro6EChUCAnJwdbt25F5cqVYWJiojE7jh8/juTkZCQnJ+PXX39FrVq1kJWVhQYNGsDCwgJNmjRB27ZtERQUhBcvXog1QT08PNCkSZMSTsaHFNqal5eHrKwstGnTBvPnz8cPP/yAb7/9Fu3atYMgCDhx4gSuXr2KTZs2YcWKFfjoo49gYGAAXV1d7N+/H9nZ2Rg1apRYNqSsyc7OhqurK2rXrg1XV1ekp6fj0KFDGDp0KLZs2YLevXsjJSUF69evx4sXL9C7d2+YmpqiUqVKuHz5Mnbt2oVTp05h06ZNGpVGVygUqFevHlJSUnDu3Dloa2vjt99+w9atW1FYWAhnZ2ex/e3atQunT5/GwIEDxVBYTXDz5k0EBQXB2dkZQ4YMEcNbe/bsKZ7i7tq1C66urtDR0YG5uTk2b96M69evo2XLluV2On/p0iUUFhbi+fPnSExMRNOmTdWiCjZt2gSFQoEOHToA+MuxfX1cLq9xuiKhXGwqnew3OZTTpk1DeHg4DAwM4OHhgfbt2yMnJwcpKSlo1qwZatas+UGW/1DSv39/pKWl4cCBA/jss8/Qs2dPvHz5EpcuXcLu3buRmZmJ8PBw1K9fHzY2NpDL5Th06BByc3MREBCgcWdSuVjv1asXIiIi8PTpU7x8+RL37t1DvXr14OfnBx8fH3E8qV27NmbMmIHHjx+jQYMGMDQ01Kh9Ev+eq1ev4vHjx2jVqpV4TblJq1xT6evrY+LEifj+++9x7tw5WFlZQUdHB82aNSuXsS82NhZPnjyBpaWlaJuWlpZon7a2NlxcXHD16lXs27cPL168wOeffy7+T0JC42giEfPChQs0NDQUhWL8/PxobGzMdevWlVCfVE22HzlyJNu0acOIiAiNCXW8jqoU/i+//EJfX18xOXnPnj20sbHhkCFDGBcXp/a548ePa0y19T8JMCgFOFJSUjh58mQKgiDKz+fm5nLSpEk0MzPTuGprcXExZ8+eTVtbW3p5efHIkSPcsmULJ0yYwE6dOtHQ0JCCINDS0pJ+fn4UBIFt27ZlWFiYWmmQD43CwkL27t2bU6dOFa/t2rWLNjY29PX1VVNjffr0Kc+ePcvo6GgGBATQy8uL+vr6tLCwKFEapqxQlp8ZMGCAmlDSnj17aGpqKgqwJCUlcenSpTQ1NWWLFi1ob29Pe3t7Ojk50cXFRSNiLKX1jcuXL9PU1JRmZmbU19enIAgcPnw4f/nlF7X3/fbbbzQwMNCoQIJcLuekSZMYHByspvqnKlakLB+gLAuivGZjY1Pu/SImJobOzs60tLRUa3dRUVE0MjLiqVOnSL57MbSKRG5uLidMmKAmtlZau7xx4wZbt25NHx8fZmVlkSxfoayKjrJNFRcXMyIigg4ODpw4cSLz8vJIvlKevHfvHqOjozl16lT26tWLVlZWFASh3PtKcnIyV6xYwc6dO9PR0ZFTpkzh7du3RREW5fNfsmQJu3fvLvWX94hNmzbRwsLiP7ana9eu0dDQkPv27Ssny/7qIwsXLqS9vT3J0sca5bhy48aNEqJ3EhLlQZluf5PEixcvEBwcDE9PT/j7+wMAVqxYAT8/P8yfPx9aWlpvPKFcsGABBg4ciIcPH5YI99IUyiLww4YNQ2ZmJqpWrYovv/wSANCjRw8UFBRg4cKF+PHHHzFs2DCxQHfHjh3RsWNHAGWbZ5Wbm4uoqCi4uLiUeuqk/K60tDRoaWlh5MiR4gllUVERcnNzRdEMTebgKO0YM2YM5HI5fv/9d5w6dQqhoaHo06cPsrKyUFxcjJiYGNy+fRvXr19Ho0aN8PDhQzx48ECjJ0MVHYVCAblcrraj6eLiAi0tLcycOROzZs2Cv78/LC0t8dlnn+Gzzz6DtbU1+vbtCwC4desWateurSboUlZkZ2ejV69e+OqrrzBjxgzUq1dPPHnp0aMHVq9ejfv37wN4dUI+dOhQODs749ixY3j06BF0dHRgbW0NfX19jdhX2i6wmZkZNmzYgKNHjwIATE1N0blzZ/G9CoUCJJGamgoTExONnmYUFhbiwoULcHNzE38/SVSuXBnAqwiCa9euoWfPnjhz5gzGjBmDefPmwdXVFUePHsWRI0fE56wJ8vLyEB0djcTERFSuXBmenp4ICgrC0qVL0b9/fxw5cgR79uzB8uXLMXv2bLRr1w7AhxU18J+4c+cODhw4gJSUFFSuXBnt27d/4wnl/Pnz4evri/Xr1yM4OLhcUzcqMsoTP+U8EhoaisjISBw+fBhTpkzBhAkTxH7atGlTAK/yxvLz85GQkIDGjRujTp06ZW5XYWEhUlJScO3aNSgUCnz66ado06YNGjduDD8/P/Tt2xcFBQUl5i8tLS08f/4cd+7cgaGhIYqLi8WTa4mKRVFREQoKCsSQz08//RR5eXk4f/48HB0dS7xf2a+/++476OrqiqkKmraxUqVK4rj79ddfIzk5GX/++Sesra1LvF85rtSqVQvAq/WZhES5ogkPVbWYq+oOnfKEcv369W89odRkLbU3ce7cORobG7Nly5Yl5MW3b99OGxsb+vv7MyYmRqN2KMspjBgxgvfu3ROvq0qmJyYmslWrVmJNxqSkJP7www8UBIEGBga8du2aRm1UPh+lvH1xcTGnT5/Otm3bcty4cczIyCj1c7m5uUxISHgnz7eioPztISEh7NevH0l1ifHdu3fTysqKvr6+vHjxoni9sLBQ4/eroKCA7dq1o6WlpdqpdmFhIYuLiymXy9mtWzdOmDBBtLk8T1qSkpK4c+dOBgUFMTAwkJMmTeLRo0fFenOv3x/Vk4+kpCR6eHgwJCREozbeuXOHhoaG4s6wqk0+Pj5s1aoVk5KSmJGRwRkzZtDQ0JAHDhxgfn4+w8LCNFoaRyaT0dnZma6urnRxcWGHDh3Ypk0bnjhxgn/88QcdHBxobGxMQRB45swZ6XTlLZw9e5bt27enh4cHf/vtN/G66vMuLi5mcXExQ0JC6OPjw9zc3HdgacUhLy+P58+fV6sPSVJtLJk+fTptbW05fvx48X3KPqHpslHZ2dkcNmwYnZycaGJiQhMTExoYGNDBwYGHDh3i8+fP1d6vWl4hOTmZkydPpqWlpcbqXEr8e+RyOd3c3Lh69WpxDSqTyWhnZ0cvL683rl1I8siRI+zQoYPGn29eXh4DAwPVavrGxcXRzMyMI0eOLFFqTZXdu3ezU6dOahFFEhLlQZltk6akpODy5csAAENDQ3EnXltbG0VFRQBenVBaW1tj3rx5+Pnnn5GdnS1+Xhn7rfyMQqEo1129Vq1aYdOmTSgqKsLSpUtx69Yt8TU3NzeMGjUKJ0+exK+//qpRO6ytrREVFYUTJ05g4cKF4imQMj4+KSkJ7u7usLa2Fkt+KBVnfX19sX//fo3kady9exdHjx7Fs2fPxF2wSpUqiaUPQkJC4ODggAsXLmDWrFl48eIFgFc7vcCrnbYqVapAT09PTGz/0HZtVUWlGjRogPv376OwsBAfffSRWJKmZ8+emDBhAq5du4alS5fi0qVLAMpefKU0dHV1RbnxI0eOICMjQ/xubW1tLF26FKmpqRg6dKiYV6dakkYJNSAQfenSJQwZMgQbN27E06dPkZubixMnTmD06NEICgoST+qBV/f59OnTyMvLQ35+Pk6fPo3x48cjLy8PERERGrOR/597XVxcjEePHom2KOnZsyfWrl2Lxo0bo3bt2ujcuTOKioqgra2NypUrIzw8HJUqVRLHy7KkqKgI48aNQ40aNTB//nxER0dj586daNiwIaKiomBhYSGqj1apUgX169cXT44k/oL/L8hibW2NGTNm4MmTJ1i+fDlOnjwJAOIJJfCqbyhz8QsKCjQq0FHRKSgogKenJwICAjBgwADs3bsXiYmJAKA2loSEhKBz5844f/48IiMj8eLFC1SqVEkUyNMUOTk5cHNzg0wmw+DBg/HLL7/gwIEDCAsLQ9WqVREaGoodO3aI89rq1avh6+uLOXPmYPLkyQgNDcWpU6ewfv16SS2zAqOjo4NPP/0US5YsEXMLq1WrhuDgYMTFxSEiIkJtXark5cuXOHHiBJo0aYK6detq1MasrCxcv34d27Ztw/bt2wG8irYZNGgQfvnlF2zfvh1paWkA/prHSCIrKwtnz55Fs2bN3qiwLiGhMcrCI01MTKShoSHt7Ow4cOBAxsTEMC0tTe09qqcvbzuhfNdcvHiRlpaWHDFiRImcrxMnTpTbbv2hQ4doZGTEoKAg3r59myT54sUL2tracuTIkaXeN2XB9bLm0aNHNDU1paWlJTt06MBNmzaVWny5uLiY06ZNK7Gz/KHnCZW2o75//36ampqKO6FFRUVq90l5Qunv78/z589r3EbV754yZQoFQeD8+fOZnp5O8lUOnaGhIY8dOybaW17Ex8fT3Nyc4eHhan0yPz+fERERYr6mcsz56aefKAgCO3XqRBsbG9rb23PgwIFi/9C07f369WO3bt3Unu3r5OXlcerUqXRxcRH7siZPnlNTU+no6MidO3eqXd+8eTMFQRCjGU6cOMHu3buzVatW4g68dEJZEuWzOnv2LG1tbUucUCpJTEzkgAEDOHfu3A96HHz27JmYTz1z5kzq6enR3t6eM2fO5J07d9TGyKKiIkZERNDW1pYhISElTgTLmoKCAgYFBdHLy6vEqU9xcTGfPHlCf39/mpiYcPfu3STJX375hd7e3rS0tGTXrl35/fff88GDBxq1U6JsKCws5NixY2liYsJNmzYxLy+PMpmMixYtoqGhIQcNGsTffvuNmZmZLCgo4J9//smJEyeWyCvXBKp6GC4uLuzSpQu3bNkivj5x4kQKgsDQ0FC1KLlLly5x8uTJtLCwENeLEhLlSZk4kzExMRQEgaNHj2ZAQACNjY3Zs2dP7ty5k0lJSeL7VCdTPz8/mpmZcenSpeUmtvN3UTqUI0eOLHXw0LTojnJiPXjwIAVBYEhICO/fv8+ioiLu2bOHL1++LPPvfxuPHz9m27Zt6e3tzR9//JFGRkZs06YNw8LCGBsbq+bEKhcC7du3Z0hIyFvDRj4EcnJy6O7uzn79+nHmzJncvXs3r1y5wgsXLtDAwIB//PGH2vtV29auXbsoCAKDgoJEUQpNovrd33//PQVB4MqVKzl79mwaGhry4MGDGrfhdbKzszl06FCOGjVKbVGpKnyxdu1ampub09fXl7m5uZTJZDx48CBDQ0M5e/Zs/vLLL+JvK8tQubt373LVqlUMCwvj1q1bxba+bt06GhoaMjw8nC9evCD56t6q9vGEhAS6uLhw1qxZZWbP20hKSmLLli3FhYnSFqVgw4kTJ8T3njx5kt27d6eNjY3GF0/vC6U5+qoOpZ2dHd3d3bl//37x9adPn3LSpEls166d5Gjw1XxmamrKhIQEXrlyhVOnTqWZmRltbW3p5+fHS5cu8dGjRyRf9ZeZM2fSyMiIU6ZM0agjnpKSQnt7e27btu2NGzoZGRns27cvO3ToII49ubm5fPHiBYuLizUegitRtuTl5TE0NJTGxsbcsmUL5XI5X758ya1bt7JNmzY0NjamtbU1bW1t2bVrV7q7u2tEUK40lHPVgwcP2KtXL3bp0oVbt24VX5s7dy6NjY1pZGTEbt260dbWVhS907ToooTEm9Aiyybe6/vvv0dsbCx27dqFw4cP49ixY/j111/x9ddfo23btvDw8ED9+vXVjt979+6NTz75BBs2bKhwIY+XL19GYGAg9PX18cMPP6gVNi8rCgoKkJiYKArtkBTLixQUFMDNzQ3Z2dlIS0tDu3btMGnSJHzxxRdlbsfb4P8nn+/YsQOzZ8/Grl27kJ2djcOHD2Pbtm2oVKkSmjZtCl9fXzRp0gRfffUViouLMW/ePKxbtw5BQUEIDAwsV5srErGxsYiKikJRURHu3LmDly9fAoAY2mpubg4jIyN89dVXMDIyQs2aNdGgQQPx87/++iuaNm2qsWLIqqi2P+BVn96+fTu0tLTwww8/wN3dXeM2vE5aWho8PDwQEBCAPn36qL2mDBsmiR9++AF79+7F2rVr0bx5c/H3qI4rZSmUdfHiRYwYMQI1atRARkYGsrKy0KFDB4SHh6Nu3boYMmQIzpw5Azc3NwQFBYmhUQUFBbh16xZmzJiBwsJCbN26FZUqVdJ4eY3MzEy4ubmhdevWGDt2LGrUqAHgVUHu3r17Y926dWrCDqdOnUJ4eDi0tLRw6NAh6OrqVrgxWtPk5+fj6tWrsLCwKFVgB/irjV24cAEzZ87E8+fPYW1tjSpVqiAlJQU3btzAmjVrNCqG9r6QnJyM4OBgNG/eHFOmTEF+fj5IYsyYMThx4gQqV66M7777Di4uLnBxcUGVKlWwbNkyODg4aHT8O3r0KEaOHIk//vgDtWvXfuNzPnz4MMaNG4fw8HC4urqqjSea7r8S/w7VFBOluE1OTg4sLS1Rq1YtBAYGws3NDR999BFycnJw5MgRPH36FMXFxWjVqhWaNm2qcdFAVRuVJZru378PZ2dnfPXVV/D29oabmxuAV+uKBw8e4M6dO9DR0YGNjQ0EQdCI6J2ExN/i33qjyp283377jW3btuXevXtJvjo5uHnzJjt06CBKeQ8ePJgxMTFqycEVWYzl3LlzHDx4sEZ2RQsLC+nl5UUPDw9euXKF5F87UkqhE3d3dz5//pxHjx6lkZERhw8fribKU57cuHGD9vb2nDdvHsm/xCX69OkjCv/Y29tz9erV4vPdtm3bBxsip1AoSuxWP336lHfv3uWxY8cYFRVFMzMzWlpa0sbGhoIgUBAEmpiY0MXFhR4eHty8ebPG7JPJZPTz8+OWLVvUhH5I9ZD0mTNnUhAELliwQAx5LU/Onj1LMzMzURTr9fak7JsymYwmJiZi2RJSc2NKTEwMDQ0NGRERwZSUFCYnJ3P9+vU0NDRUO2lUhsZ16tSJixcv5rp16xgUFMTevXuzb9++5RZ2q+TGjRtqJ2fKa4IgiCfkynYbFxfHw4cPMzk5uVxsq4gMHDiQLVu25IkTJ8S29LYTyoSEBM6fP58ODg50dXXllClTNFqG5n1Bdf6cM2cOW7ZsKc4RiYmJtLCwYFhYGOfPn8/u3btTT0+PpqamXLZsmUbtUj63kydP0tDQkFevXn3r+58+fUoLCwuuXLlSo3ZJlA25ubncvHmzmEageoIsl8vp5OREDw8PBgYG0sjIiNHR0SXEocrDxkOHDon/XVxcLM4Hubm57NGjB52dnenk5ER7e3u1kFcJiYpEmam5KhQKenh40NPTU7x25MgRGhgYcNmyZRw3bhzbtm1LQ0ND2tjYiLlXZMXOqVNOOJqwccOGDaJKrHJBL5fL6ejoSHd3d7U6gocPH6aRkRFHjBhRrjHxqr/7hx9+YKtWrcQw24MHD1JPT48bNmzg6tWr6enpSQMDAwqCwOjoaPFzH5pDmZOTwzlz5tDPz48TJ058Y3jokCFD2K9fP6alpYm1JGfOnMm+ffvSycmJd+7c0ZiNM2bMoCAI9PDwoLW1NcPCwt5Ym0oZ8rpgwQK12onlQXx8PAVBKFEzUhWl8+vo6MgZM2Zo1B6lIzlt2jQ1dc7s7GyOHDmS7dq1U9ss27RpE/39/WljY0MbGxsOHz6cP/30k0bCbv8Jyu8/c+YMBUHgpUuXSL5yykePHs2ePXuKIbofKqmpqWzfvj27dOnC48ePv9GhfH1uUP73hzbuKSkqKuKLFy/U8vqVffTp06e0s7Pj/Pnzef36dVpYWHDo0KFijb+UlBT+8ssv7N27t8bCCl/XFrh48SL19PTEfOI3PTe5XM42bdqINZ0lKjbKOS4qKorZ2dniddXN+rS0NBYVFXHs2LE0NjZmdHR0CT0KTW1KKtWLBUEokX6gtNHNzY35+fl8+PChmEOpDHkl/2qrFfEwRuLDokycSWWD/v3332lqasrTp09zz549ooiHklu3bnHJkiXs37//e5VjUNYdVXWy2r59O1u1asXhw4czJiaGTk5OdHd3Z2pqaonvP3bsGAVB4Pjx4zUmtpOZmcnU1FS1BbFyIfDgwQO2bduWa9eu5Y4dO0QHQ1kG5unTpzx9+jTHjBnzXj3fskQmk9HJyYnt2rVjt27daGhoSGtra7H4O/nXYmbSpEl0cHAo8Tfy8vLUJj9NcPfuXXbr1o3ff/89t23bxjZt2tDKyoqenp48duxYCSGKyZMn08DAgDNmzNBosfC7d++q5e+lpqayU6dO9PPze6vceVFREdu2bcsff/xRY7Zdu3ZNbPNyubzERtOMGTNob2/Ply9flliQPn/+vET+cEVwNpTj9L1791hQUMCwsDCamZmJ0RIfOo8fP2abNm1ob2/P48ePi9dLcywzMjLUFqIf4gIvJyeHoaGh7NGjB93c3NTmf/LVXDJ+/Hja2NjQ1NSUw4cP55MnT95Y1qeskcvl7N69u5pd+fn59PX1pbm5ubiBV9oi/ezZs3RwcBA3fj/E5/s+UVhYyOHDh7NNmzZcuHCheL20zfqCggKOGTOGzZs359q1a8tNGPLatWscOnQora2t+dNPP4l2q9qobGfJycns2bMnu3XrJr5XQqKiUKZ1Jh89eiQexxsaGnLBggVvFdf5UB2O13eyt2zZwlatWtHMzIxOTk5vFdj59ddfNVbn6OrVqxwwYABtbGzYpUuXEqc8+fn59Pf3Z/PmzWloaMioqKi3Lp4+tOcrk8nYoUMHDhgwQEyEv3v3LvX09EosqshXz93ExETtBLK8FijPnz9ncHAwAwMDWVRUxMePH3Pt2rXs378/BUGgra0tf/rpJ7UQ2NDQUDZv3lwjokoKhYLZ2dns0qULHRwc+Ouvv4qvRUZGUhAErl27Vq1vqC44z549y+7du/PPP/8sc9vIVyFHI0aMoCAIahO5cqOlsLCQTk5OHDp0qEa+X1MoRZ7i4+P5ww8/0NTUtFSl5g8BZS3Vx48fUy6Xi+3rdYdS2UdV219MTAwDAwO5b9++D9bJkMlk7Nq1K7t06UJ/f386OjpSEASuWLGC5F9jW2JiIlu3bk0XF5c3rg80dQ8fPXrE4OBg6unpiXaRr9S1ra2t6ejoWOr8+uLFC4aEhNDDw+ODF5V7H1Adl/39/dm2bVsuWLCAXbt2paenZ6mb9YWFhQwICKCNjQ2zsrI0atuff/7J48ePMzk5mffv32dAQABbt27NDRs2sEePHvTw8FDb0FWupVJSUtihQwf26tVLozZKSPxTytSZJMmff/6ZgiDwhx9+KPHa2/JO/tfJy8vj9u3b+f333zMkJIQbN25UU0rcvn07W7RoQR8fH7VTgfK6VzExMWzevDm9vb0ZHh7O3r1708DAgJs2bSL5lwOckJBAKysrBgYGlotd7wvKwscDBgxQO0FTOhlz584lqf48jx8/TkEQGBcXVy42KhQKte8/fPgwBUFQC2+9ffu2WKhbX1+flpaWDA0N5dmzZ0lSo6eS5CunsHPnznRzc+ORI0fE64MHD2aLFi24bt26EieU9+/fZ58+fTSW36zk0qVLHD58OK2trdXCuMlX6tSdOnUSJ/iKHLpPqkc76Onp0cXFhcbGxmKJkA+NnJwczpw5k+7u7mzdujWdnJx48uRJ0dl58uSJmkOp6kheunSJXl5eFARBo6HpFRnV8U+ZJ1pUVMTOnTvTz89P7A/FxcXMzc3lxIkTaW9v/07KGNy/f59hYWEUBEEtL3PFihW0tramlZUVDx48yFu3brG4uJjHjx/nhAkTyqU0hMR/T25uLlesWMEJEyZw2rRp4rymdBKVm6TKzYLS1laFhYVvjYD5t8hkMnp5edHW1lbUmtiyZQsvX75Mf39/mpqa0tbWVlRvV7VROeakpqZ+0LnsEhWTMq8C3LJlSxgbG+PJkyfIyclRU29Vqp19aKpn2dnZ8PHxgUwmEwub7969G1988QV69OiB4OBguLm5obi4GAsXLsTSpUsxdOhQmJqavlFFsCyJjY2Fj48P+vbti4CAANSuXRt5eXno0KEDEhISAEBUzaxfvz5atmyJhw8f4uHDh2jUqJHG7HpfyMnJgYuLC5o1a4aIiAjUq1dPVGZ78uQJXr58CX19fQDqbd/Q0BBaWlq4du0aTE1NNW7n622oc+fOaN26NVatWgVbW1vcvHkT3t7esLOzQ2hoKJKTk7Fx40b8/PPPOHHiBA4cOIA6depozD7+fzH46dOnY8KECVi5ciW0tLTQuXNnTJ8+HWFhYZg5cyYOHz6MTp064dNPP0VCQgLi4+NRXFyMZcuWQVtbW00Vryxp3rw5Bg8ejKKiIkRFReGjjz5C7969ERAQgNu3b2PZsmWoUaOG2McrMsq20KxZM5DE3bt3sWPHDgiC8I4tK3+ys7PRp08fVK1aFQYGBnB2dsbFixdx//592NraAgDq1auH7du3w93dHbNnz4aOjg7at2+P+Ph4zJ49G7du3cKePXvQrFmzd/xryp/s7Gw4OzujUaNGmDNnDj7//HNR6fS7777DRx99BIVCAeDVPFKlShV06NABu3btQlxcHL799luN9VnglYpzdnY2tLW1Ub16dXz99dfw9vYGSSxcuBAkMXToUPj5+aFOnTrYv38/xo4dC11dXejq6qJmzZpo2LAhNm7cKCqvS1QssrOz4enpiYKCAmhpaeHRo0fYtGkTZsyYARcXF0RFRWHMmDG4cOECfvnlFwwaNAiffPJJiXZXqVIl1KtXT2M29uzZEw0aNEBYWBjkcjkOHz6MqVOnYsWKFZg0aRJmzJiBy5cvY/fu3fD09ISWlpZoo46ODhQKBRo2bKgR+yQk/hWa8FAjIyPZvHlzcdexou/SaxKZTMaOHTtywIABoshFfn4+4+Li2KNHD5qYmDAsLEx8vzLk1d/fv1zyllQFRZS7YYWFhZTL5fT09OScOXPEa0r27t1LQRBEFbIP+fkqFApx1/Po0aMk1VV5PT09xVCvmTNncufOnbxy5YqYE2lkZCQq5GqC/Px87t+/n9OmTeP48eO5f/9+tfCYNWvWsF27dty+fTstLS0ZGBjIp0+fijuiMpmMd+/eZUpKSpnbVlqkgrIt3b17l23atGGfPn3UctWioqLo5eVFc3NzWlhY0Nvbm/PmzRPbZ1mGVt+7d487d+7ksmXLePjwYfF6XFycGJbk4ODAjh07llsNsrKmqKiIS5cufWcq0e+avLw89u7dmz4+Pm+sBXnkyBFR6fPRo0e0sbGhvb0916xZQ09PT5qZmX2w9d1ycnJoZ2dHPz8/pqenU6FQiH342bNn7NSpE9esWVPqZ/39/Wltba3RaIfXcziVESKk+gnlkiVLxOvZ2dk8e/Ys9+7dyy1btvD27dsfvBhVRUaZXjJw4EBxzfn777+zR48e7NChg1oebGBgIG1sbLh48WJxDi6P9Yvy5L5///5qJ5/37t1j79692bNnT5LklStXGBAQQGtrazEqrLxslJD4N5SpM6lcFGZmZrJdu3bvXf5QWaMcQLy9vZmamireH6UAS2pqKv38/GhsbMwFCxaIn9u+fTttbGzYr18/xsfHa8y+69evqwmKKMt9kK9Ct1q2bKm2kFelX79+tLW1leL2+SqJvlu3bvTw8OC5c+dI/qXG1rx5c/bu3Zv29vbU19enIAjU09Njp06d2L9/f1pZWWksdEomk9HDw4POzs7s1KkTe/XqxXbt2vHMmTPie4qLi9m1a1cKgsBRo0ap5QNpOsS6tHxbpTP46NEjGhgYsEWLFuzVq5foqJOvFohPnjxhcnKy2iRblqIdFy9epJWVFXv16kVzc3Pa2Nhw/Pjx4uvx8fEcOnQozczM1DYD3sdJvyIIAb0rNm3axN69e4ulZ0j10jjLli2jIAj09PQUc0kfPXpEOzs7CoJAS0vLD9aRzM/P5/z589XyIpXI5XKxbNSECRPo7+/P8PBw7tmzR7xfUVFRtLOzUxNCKUvelMO5fPly8T1JSUmiQymptL5/qK6xVMVqSHLHjh3U09MT52RS3aFcsmRJuQjtZGdns1OnThwyZEipIbQhISHs0qWLKGQYHx8vOpRSKRCJ94UyjStRhk598sknaNasmRjW+SGSm5sLDw8PVK9eHUuWLEHDhg3F+6OrqyuGK0yZMgXffPMN9u7di/j4eACAm5sbAgICkJaWprGwwsTERGzcuBFaWlqoU6cOPvroI2hra0NbWxsJCQkICAiATCbDjh070K1bNwQGBmLjxo04f/48AODrr79G06ZNUa1aNY3Y9z5haGiIuXPnIisrC4sXL8bZs2fh5uaGqlWr4ueff8b27duxd+9eHD9+HNOmTcOgQYNQqVIlpKenY926dRoJncrNzUWfPn1QpUoVhIWF4cCBA9i5cydWrFiB1q1bAwAePHgAbW1tuLq6onbt2ujUqZNaYWZNhlbfv38fvXr1wvHjx8VrhYWFqFSpEq5du4auXbvCy8sLixYtwvPnz7Fy5UocPXoUwKvxpV69emjcuLEYokRSLCD+b3n69CkmT54MR0dH/Pjjj/jtt9/QuXNnxMbGIjk5GQBgZGQEf39/WFlZYceOHdiyZQsAiGG27xNldd/eR86fP486deqIYegk8dFHHwEAli5diqVLl6J79+54+fIlZsyYgRs3bqBBgwbYsmULDAwMsGHDBujp6b3Ln/BOyM7Ohr+/PwoKCtC5c2ds2LAB0dHRAF4VhXdxcUFqaio6deqEZ8+e4e7du4iOjsaECRPg4uKCrl27Qi6XY8OGDahfv75G7HN2dsZnn32GZcuWYfny5di3bx+aNGmCS5cuobi4GADQpEkT+Pr6wt3dHYsWLcKKFSvEv/G+9eMPjZycHLi6uuLzzz/HokWLUL9+fWhpaaGwsBAA0LRpU1StWhVZWVkAXs0vOjo6WLx4MVq0aIFly5Zh8+bNGl2jksTYsWORkpICNzc3MYRWaSMApKeno3bt2mKbNDIywrBhw2Bubo7w8HDs3LlTY/ZJSJQZmvJSHz9+/EHXwDl9+jQFQeDAgQPF8Fay9JA+ZcmB14shv03V9d+gLEGycuVKUVBEGVJx48YNmpqa0tPTkzNnzuSYMWPEUC5BECgIAq2trbly5Urx+b6PpzGaICEhgQ4ODjQzM2PXrl3FOoSltf9nz55pLHSqqKiIYWFh7N+/P5OTk0uciJPk6tWraWNjw19//ZWZmZm0sLDglClT3mhvWfP777/TycmJHTt2VBMAio+Pp6mpKSdPniyGIcXExNDOzo5ubm5vPCkvS65cucK2bdsyNjZWvHbq1CmamJjw9OnT/OOPP8S+mZCQQH9/f7Zu3ZqbN2/WuG0SZYeyjE9ERARJ9f6xadMmCoIg1kOOjo5my5YtGRkZKZ5mfKgnujKZjO3bt+eAAQMol8vFk5S3qVEqFArGxsby4MGDDAoKYu/evTWmSq4aUpiWlkbyr2c1bNgwjho1ioWFhWoh8YmJiQwLC6O+vr5alJBExeXgwYMUBIFjxoxhYmIiSfW1SFhYGG1sbNSibVTr+44dO/aNoe1liWrkUkxMjNprixYtorGxsXharzqmXL58maNHjxYFrSQkKjIacyaVfKgTLvmqhpu1tTV9fX3VSiyoLtYLCgpYXFxMd3d3BgUFsaCgQKNOeExMDPX09Dh9+nSSVFsIzJkzh82bN2dYWFiJ8I/bt2/zzz//5A8//MDx48eLE7HkSKpz8+ZN2tvb08XFRW3ieF1JVZNkZWXR1dWVK1euLPU5LVmyhIIgsH379qJq5bp162hiYlJistMkJ0+eZJ8+fWhra8srV64wNTVVdCSVjriSmJgYdurUiR06dNC4jXFxcTQwMFArT7J9+3YaGxuL4Y3t27fnwYMHSb4qqTNs2DC1vFmJik9ubi6dnZ3p6+srXlP20ZiYGJ48eVLN4TA1NeWsWbPK3c6KRHZ2Njt27EhfX1+18gpXr15lQEAAzczM2K5dOzH/vqioqMQcIZfLxZC+subf5HDevXuXo0aNYosWLfj8+fMPchP8fUB102f9+vUUBIGhoaFqmxNRUVE0MjISVchV16HvomSZcqPZ3d1dnL+ioqJoaGjIX375hWTpaynVkHsJiYqMxp3JDxHVQWH37t20srKir6/vG08oSdLNzY3Dhw/XqF1xcXE0NDTk/Pnz1ZxF5ULA1NSU7u7u4vWioqISmwGqA/GHvFHwNpQTh4eHBy9cuCBeL6/Fyfnz58Xaga8zZ84cGhoa8tSpUzx79ixdXFzYtWtXrlmzhoIgMDIyUmPPNSMjgykpKWrCUqdPn6aHhwdtbGyor6/PGTNmMCcnR02cR/nvM2fOMCgoSOPtLjU1lR07duTp06epUCj48uVLOjk5MSgoiMePH+fZs2c5cOBAWlpaisJEFy9e5Jw5c6Q+UYGRy+U8d+4cjx8/LvaNhQsX0szMjKdPn37rZ5V57MrSKR+io5Gdnc0ePXrQ1NRUXLirtverV69y6NChbNWqldopfXltOP63OZyq+bIZGRniaaZExUMmk3HIkCE8ceKEeG3dunWiQ/ny5UsuX76choaG4mZfRUG5LvDy8mJISAiNjIwqnI0SEv8tkjOpAVSFbMi/HMrBgwernVCSrxYld+7coaurK9etWydeK2suX75MU1NT2tnZiUngpS0ErKys1GroSSeP/x2qDmV5nvaRr5xJPT098XuVz/Dly5cMCgri7t27xTa2f/9+CoLAP/74gxERERqr+xYXF0cPDw+2atWKhoaG9PPzE8PgTpw4QS8vL7Zo0UKttqRq23u9T2jaaXv+/Ln475ycHJ47d46ZmZniteTkZFpZWTEqKqrEZyWHsuLxen03Q0NDbtiwgb///jsNDAzo6+urJqSjeiKQnp7OoKAgDhky5INV9VQqZrZo0YLt27dnSEiIGOqtusGo3JgsbzVKmUxGb29vzpw5k8OHD2fr1q3F7y8sLKSjoyPbtm3LYcOGcfDgwezYsaOYtmFgYMB27dpx3rx5Ut+twCjDq728vJiRkaE2JygdSicnJ5qYmIjh6RWNhIQEUYxv8eLF79ocCYkyQ3Imy4CcnBweOnSohKqfajiGqkOpmouVlZXF0NBQdujQQWOFaJXlP+zt7dmhQweGhISIKqyqCwFVFTFJlvrfk5CQQCcnJ3bt2rXEJoImycjIoJWVFSdPnixeU068r4ePRkREsEOHDmpttayJiYmhqakpx48fz3Xr1nHatGk0Nzdn3759xfecPHmSnp6ebN++vVoOZUU5AXo9NCojI4M2NjYlTkAkKh7K8kxeXl48fvy4mLcnCALPnTvH3bt3UxAEBgQE8Pz582qfvXv3LidNmkRLS0uxxMCHhkwmY7t27ejr68tHjx5x2rRptLW15YQJE0Tn+k3zSHmoUZZFDmefPn0+2Of7PqDMg/Xx8VE7OVadHzZu3EhBENivXz+1XMiKMocouXnzJrt06VJio7mi2Skh8U+QnMl/SWFhIV1dXSkIAl1dXRkaGsobN26IO9uqA4RqyKvSuQgLC6OpqalaqE1ZEhMTQ0EQOG/ePMpkMkZERPythYAkKFI2XLt2jb1799ZIncY3kZuby2HDhtHKykqtPuLrk9W9e/fo5eXFadOmsaCgQGM5uoaGhpwxY4YYWp2Tk8Po6GgKgqB2Cn7q1Cn26dOnhENZUVDdVImPj6eDgwP379//Di2S+E+8rb5br1692LNnTxYWFnLlypWiuFhoaCjXrFnDCRMm0MvLi507d/5gy38oFAqOHj2aAwcO5KNHj0i+6gfKeSQkJOSN88jw4cOpr6/PHTt2aMy+ip7DKfHvUZ6KDxgwQHQklc+wuLhY7dn99NNPFASBEydOLBdxnf+Wdxm5JCGhCSRnsgwYOXIkTU1NuW7dOtrZ2bFVq1bs27cvf//9d7UJjiR37twpOpR+fn40MTER65eVNfn5+fTx8SmhQPh3HEpJUKTseBdJ9Hfu3GHz5s3p7OxcashPeno6J02axPbt2zMpKUkjNly6dImGhoacPXt2CUGne/fu0dzcvMTJ3qlTp9i3b1926NChQrW9+Ph4rlixgnv27OGBAwfo7u7O3r17S2FxFZi/U9/NwcFBXIyePHmSkydPpq2tLTt37sw+ffpw5cqV5boRVBF58uSJGOKt3HBSnUfe5FBqWo2youdwSvx7lI7koEGDxHqkyucnl8vZoUMHbt++Xe2ZKkNeJ0+erDHF4LLgXUUuSUhoAsmZ/BcoJ87r16/T0dGR27dvZ05ODpctW0Y3NzeamZnR2dmZ0dHRonQ1Se7du5eCINDMzEzjO94ZGRmiM6MccP+OQ3np0iVJUOQ95+zZs+LO/Jw5c5iSksLHjx9z69atHDFiBFu3bq2x9peUlEQLCws6OzuLIbTFxcXiv1NSUmhpaSnmCau2s9OnT7NLly4cNmyYRmz7b4iLi6OZmRmNjY3p6OjIoUOHir9F6iMVD4VCwYCAgBIbYqrh3IMHD6anp6dYgkaJTCZTy5mVUEfZ3v/OPKKpjbSKnsMp8e/Jz89n165d2bZtW3FTXtl/5XI5u3fvThcXF7UTcyUbNmygIAgMDw/XaArHv+VdRC5JSGgCLVKDFVv/R1EoFGKxdADIzMxEUFAQdHV1sW7dOhQWFkJXVxfz5s3DqlWrAAANGzZEixYtMGzYMHz99dc4f/486tati2+++aZcbS8uLoaOjg6Ki4sRGRmJ48ePw9raGiEhIahZsyaKiopQqVKlUj8j8f5x8+ZNhIeHIyEhAfn5+dDW1kajRo1gaGiIYcOGaaz9PXnyBOPHj8eTJ0/Qs2dPBAYGqr0+cOBApKen48CBA+I1ktDS0gIAxMXFwdjYuEK1u7S0NGRlZaFKlSpo1KgRtLS0Su0vEhWD69evY8KECahWrRrGjh2Lli1biq8tXrwYq1evxvbt26GnpyeOcaptEECJ/5Z4RWnziJWVFUJDQ1GjRg2N9ovs7Gx069YN3333HcLDw7F27VocO3YMVlZWmDhxYol57Nq1a/jxxx9x5coVBAcHo0+fPhqxS6Jsyc7ORr9+/SCTyTBgwAB4enqicuXKyMvLg5ubG6pWrYrFixejXr164mdU12YbN26EtbU1mjVr9q5+wt+ioKAAH3300bs2Q0LiXyE5k/+Q3NxcREZGolu3brCyshKvnz59Gn5+fpg/fz4cHR1x69YteHt7o3379ujYsSO2bduGq1ev4uXLl3BwcMC8efPe2UL5TQ7lxIkTUaNGDcl5/B9DJpMhMzMTt2/fhpaWFszMzFClShV88sknGv3eR48eITw8HLdv34aLiwuCgoIAAH5+frh79y5WrVqFb775Rq29vb54r8ht8fVNJYmKx82bNzFq1CjUqFED48aNQ8uWLbFkyRIsX74cc+fOhYODg/Qc/0ten0dOnjwJAwMDTJ8+HdWrV9fId5LE2LFjkZmZienTp6NBgwZQKBSYMWPGWzdGr127hhUrVuDXX39FeHg4evfurRH7JMqWzMxMBAcHIzk5GQEBAXB2dkafPn3wySefYNGiRahfv77a+3NycnD48GH06tXrHVksIfFhIjmT/5CrV6/C3d0d1tbWGD58OMzNzQEAGRkZGD16NBo2bIiePXsiKCgIlpaWCAsLQ7169ZCbm4vExERs27YNffv2hSAI7/R3qC4EZs6ciV9//RV6enqYM2cOqlat+k5tk/jfITU1FREREbhz5w48PDxw6dIl3L59G1FRUTA0NHzX5kl8ACgdyrp166JRo0Y4cOAAZs2aBUdHx3dt2nuP6jwSFhaG+Ph4rF69Wu20qKxJS0tD5cqVUatWLXHz6e9E2sTFxWHjxo0YPnw4vv76a43ZJ/HfU1RUhMePHyMtLQ2ffPIJDAwMkJOTg6CgINy5cwck0bBhQ6xcuRK1atVS+2xOTg5mzZqF3bt348iRI2jYsOG7+RESEh8gkjP5D1BOXH/++Sf8/PzQvHlzjBgxQnQoV65cifnz56NSpUro0qULxo0bV2LnrCKhuhCYNGkSsrOzsXjxYmmXXqJMUTqUsbGxIImtW7fi22+/lUIIJcqNmzdvYsSIEUhJScHQoUPFU3KJf4/yZLe4uBiZmZmoW7duuX7/m0JuSwt5lUIKKy45OTmYOHEi7ty5g6SkJGhra6NFixbw9PREp06dEBQUhJMnTyIwMBDDhg2Djo6O2PaUjuS+ffuwefNmGBgYvOufIyHxQSE5k/8Q5eD1559/YsiQIWjRogWCg4PRsmVLKBQK9O/fHxkZGVi9ejUaNWr0rs39jyh/j0KhAADx35JDKVGWPHr0CBEREbhx4wb69OmDoUOHAqjYYawS/1vcunULI0aMQK1atdRyKKVNjX/Pu54z3mUOp8S/Jzs7G7169ULt2rXh4OCAL774Ag8fPsT69euRlpYGPz8/jBgxAv7+/rh16xYGDhyI/v37o1KlSpDJZJgzZw727t2LLVu2SI6khMQ7QPIY/gFKv7u4uBjW1tZYtWoVLl26hMWLFyM2Nhba2tpo3rw5srKy8OTJE/G9FRlV51FyJCU0RcOGDTF58mTo6+tjx44dWLp0KQCIu8sSEppGEAQsXLgQWVlZmDt3LmJjYwFAciTLgHc9ZygdSR0dHUycOBGdOnVCbGwsJk+eDJlMJjmSFZjs7Gz06NED9erVw+zZs+Ht7Y1OnTrBx8cHq1evhoODA1asWIFFixZh6dKl+Pbbb7FhwwZs3LgRWVlZWLBggeRISki8Y6STybeQm5uL48ePo379+jAyMoJCoUC1atXU3nP69GkEBgbC1NQUU6ZMQd26deHs7AxbW1tMnz79HVkuIVExSU1NxbRp03D37l107twZ48ePf9cmSXxg3Lx5E+PGjUNxcTGmTZuGFi1avGuTJMqId5HDKfHfk5OTg549e+Lrr79GRESE+JwUCgW0tLSgpaWFhw8fYv78+Th06BDmzp0LOzs7BAQE4PHjx6hevToePHiA6OhoKQdfQuIdIjmTb4AkfH19cebMGQDAl19+ierVq8PCwgJNmjSBiYkJGjZsiE8//RQJCQlwc3ODpaUlAgICcOHCBSxduhRr1qyBtbX1O/4lEhIVi0ePHmHMmDH47LPPsGjRIulkSKLcuX79OqZOnYoFCxa8F+kIEn+fd53DKfH3IInAwED89ttviIqKQufOnQGoh0wrQ9Dv3bsHf39/1K1bF1u3bkVmZiYGDx6M+/fvY9u2be9c0FBC4kNHcibfQkJCAsaMGYP09HQ0atQITZs2xe+//46cnBzo6OigSpUq+Oabb9CtWzdcuXIF+/fvR8+ePVG1alUcPnwY27ZtwxdffPGuf4aERIXj2bNnqF27NrS1taWcNYl3giTG8r+LlK7xfvCmWrCqc4Ly3zt27EBYWBi2bNmC5s2bQyaTQSaTSaqtEhIVAMmZ/A/cunUL48aNw6effopx48ZBEAQkJyfj3LlzSExMxPXr13H79m18/PHHePbsGQDA3d0dgYGBFVrJVUKiIiAt+iQkJCQ+XJSle2rWrIkxY8bAwsICwF9OpHKOiIuLg5eXF3788UfY2tq+Y6slJCRUkZzJv0FCQoJY+Hrs2LGwtLQUXysoKIBMJsOjR49w9epVPHjwAJ6envjmm2/eocUSEhISEhISEhWfNzmUqpuNypzJVatWSesrCYkKhuRM/k1UB7vRo0eLDuXrkuPSSYuEhISEhISExN/nTQ4lAGRmZiI8PByZmZlYvHgxatSo8Q4tlZCQeB3J6/mb6OnpYcGCBcjKysL8+fMRExMDAKhUqRJU/XHJkZSQkJCQkJCQ+PuorrHmzZsnlu7Jzs7GvHnz8Pvvv2PSpEmSIykhUQGRTib/Iaq7Z6oJ4xISEhISEhISEv89qmus4cOH4/Tp09i2bZtUR1JCogIjOZP/BVKdMgkJCQkJCQmJske5xkpKSoK2trZUR1JCooIjxWT+F+jp6WHmzJmoWrUqPv/883dtjoSEhISEhITE/wR6enqYNWsWmjVrhh07dkiOpIREBUc6mfwXSHXKJCQkJCQkJCTKHmmNJSHxfiA5kxISEhISEhISEhISEhL/GCnMVUJCQkJCQkJCQkJCQuIfIzmTEhISEhISEhISEhISEv8YyZmUkJCQkJCQkJCQkJCQ+MdIzqSEhISEhISEhISEhITEP0ZyJiUkJCQkJCQkJCQkJCT+MZIzKSEhISEhISEhISEhIfGPkZxJCQkJCQkJCQkJCQkJiX/M/wENEn47nnD+UwAAAABJRU5ErkJggg==\n"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "cur_fluxes = pd.Series(solution.velocities).abs().sort_values(ascending=False)\n",
+    "cur_fluxes = pd.Series(solution.velocities, index=metabolism.reaction_names).abs().sort_values(ascending=False)\n",
     "\n",
     "# plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
     "plt.figure(figsize=(15, 5))\n",
@@ -516,8 +569,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:16.618573Z",
-     "end_time": "2023-05-03T13:22:16.788059Z"
+     "end_time": "2023-06-26T19:06:25.457288Z",
+     "start_time": "2023-06-26T19:06:25.304614Z"
     }
    }
   },
@@ -531,12 +584,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 21,
    "id": "a59449e4-be36-49c8-94ed-35c14f5bb72d",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:16.787988Z",
-     "end_time": "2023-05-03T13:22:17.108193Z"
+     "end_time": "2023-06-26T19:06:25.817693Z",
+     "start_time": "2023-06-26T19:06:25.456944Z"
     }
    },
    "outputs": [
@@ -544,7 +597,7 @@
      "data": {
       "text/plain": "<Response [200]>"
      },
-     "execution_count": 90,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -557,45 +610,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'G6245', 'G7431', 'G7452'}"
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{\"G6245\", \"G7431\", \"G7452\"}"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:17.107510Z",
-     "end_time": "2023-05-03T13:22:17.110101Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 22,
    "id": "ab016fac-3d48-49fb-85c7-63826a8f5664",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:17.120431Z",
-     "end_time": "2023-05-03T13:22:17.122819Z"
+     "end_time": "2023-06-26T19:06:25.817958Z",
+     "start_time": "2023-06-26T19:06:25.817592Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "                                      Environment  \\\n0                  Hydrogen + fumarate, -O2, -Glc   \n1                                 Ribose C-source   \n2                              Thiamine salvaging   \n3                     Nitrate as reduction target   \n4  3-Phenylpropionate as carbon and energy source   \n5                   Molybdenum cofactor synthesis   \n6                         pre-Quenosine synthesis   \n\n                                               Genes  \\\n0  {EG11799, G7554, EG10468, EG10470, EG11801, EG...   \n1               {EG10814, EG10818, EG10815, EG10816}   \n2           {EG11572, EG11574, G6566, M007, EG11573}   \n3  {EG10640, EG10638, EG10639, EG10647, EG10648, ...   \n4                          {M011, G6206, M010, M012}   \n5  {EG11598, EG11666, EG10152, EG11597, EG11595, ...   \n6                              {G6245, G7452, G7431}   \n\n                              Add                        Remove  \\\n0  {HYDROGEN-MOLECULE[p], FUM[p]}  {OXYGEN-MOLECULE[p], GLC[p]}   \n1             {D-Ribopyranose[p]}                      {GLC[p]}   \n2                   {THIAMINE[p]}                            {}   \n3                    {NITRATE[p]}          {OXYGEN-MOLECULE[p]}   \n4         {3-PHENYLPROPIONATE[p]}                      {GLC[p]}   \n5                      {CPD-3[p]}                            {}   \n6                              {}                            {}   \n\n                              Demand Reactions  \n0                              False     [nan]  \n1                              False     [nan]  \n2                              False     [nan]  \n3                              False     [nan]  \n4                              False     [nan]  \n5                      {CPD-8123[c]}     [nan]  \n6  {7-AMINOMETHYL-7-DEAZAGUANINE[c]}     [nan]  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hydrogen + fumarate, -O2, -Glc</td>\n      <td>{EG11799, G7554, EG10468, EG10470, EG11801, EG...</td>\n      <td>{HYDROGEN-MOLECULE[p], FUM[p]}</td>\n      <td>{OXYGEN-MOLECULE[p], GLC[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ribose C-source</td>\n      <td>{EG10814, EG10818, EG10815, EG10816}</td>\n      <td>{D-Ribopyranose[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Thiamine salvaging</td>\n      <td>{EG11572, EG11574, G6566, M007, EG11573}</td>\n      <td>{THIAMINE[p]}</td>\n      <td>{}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Nitrate as reduction target</td>\n      <td>{EG10640, EG10638, EG10639, EG10647, EG10648, ...</td>\n      <td>{NITRATE[p]}</td>\n      <td>{OXYGEN-MOLECULE[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>{M011, G6206, M010, M012}</td>\n      <td>{3-PHENYLPROPIONATE[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>{EG11598, EG11666, EG10152, EG11597, EG11595, ...</td>\n      <td>{CPD-3[p]}</td>\n      <td>{}</td>\n      <td>{CPD-8123[c]}</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>{G6245, G7452, G7431}</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{7-AMINOMETHYL-7-DEAZAGUANINE[c]}</td>\n      <td>[nan]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "                                      Environment  \\\n0                  Hydrogen + fumarate, -O2, -Glc   \n1                                 Ribose C-source   \n2                              Thiamine salvaging   \n3                     Nitrate as reduction target   \n4  3-Phenylpropionate as carbon and energy source   \n5                   Molybdenum cofactor synthesis   \n6                         pre-Quenosine synthesis   \n\n                                               Genes  \\\n0  {EG11799, EG10470, EG10468, EG11801, EG11800, ...   \n1               {EG10816, EG10818, EG10815, EG10814}   \n2           {EG11574, EG11572, EG11573, M007, G6566}   \n3  {EG10638, EG10640, EG10648, EG10639, EG10644, ...   \n4                          {G6206, M011, M010, M012}   \n5  {EG11829, EG11666, EG10153, EG10152, EG11597, ...   \n6                              {G6245, G7431, G7452}   \n\n                              Add                        Remove  \\\n0  {HYDROGEN-MOLECULE[p], FUM[p]}  {OXYGEN-MOLECULE[p], GLC[p]}   \n1             {D-Ribopyranose[p]}                      {GLC[p]}   \n2                   {THIAMINE[p]}                            {}   \n3                    {NITRATE[p]}          {OXYGEN-MOLECULE[p]}   \n4         {3-PHENYLPROPIONATE[p]}                      {GLC[p]}   \n5                      {CPD-3[p]}                            {}   \n6                              {}                            {}   \n\n                              Demand Reactions  \n0                              False     [nan]  \n1                              False     [nan]  \n2                              False     [nan]  \n3                              False     [nan]  \n4                              False     [nan]  \n5                      {CPD-8123[c]}     [nan]  \n6  {7-AMINOMETHYL-7-DEAZAGUANINE[c]}     [nan]  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hydrogen + fumarate, -O2, -Glc</td>\n      <td>{EG11799, EG10470, EG10468, EG11801, EG11800, ...</td>\n      <td>{HYDROGEN-MOLECULE[p], FUM[p]}</td>\n      <td>{OXYGEN-MOLECULE[p], GLC[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ribose C-source</td>\n      <td>{EG10816, EG10818, EG10815, EG10814}</td>\n      <td>{D-Ribopyranose[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Thiamine salvaging</td>\n      <td>{EG11574, EG11572, EG11573, M007, G6566}</td>\n      <td>{THIAMINE[p]}</td>\n      <td>{}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Nitrate as reduction target</td>\n      <td>{EG10638, EG10640, EG10648, EG10639, EG10644, ...</td>\n      <td>{NITRATE[p]}</td>\n      <td>{OXYGEN-MOLECULE[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>{G6206, M011, M010, M012}</td>\n      <td>{3-PHENYLPROPIONATE[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>{EG11829, EG11666, EG10153, EG10152, EG11597, ...</td>\n      <td>{CPD-3[p]}</td>\n      <td>{}</td>\n      <td>{CPD-8123[c]}</td>\n      <td>[nan]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>{G6245, G7431, G7452}</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{7-AMINOMETHYL-7-DEAZAGUANINE[c]}</td>\n      <td>[nan]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 92,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -665,12 +694,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 23,
    "id": "0ecc835e-9a66-4cfe-838f-257cf37449b0",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:17.123952Z",
-     "end_time": "2023-05-03T13:22:18.814660Z"
+     "end_time": "2023-06-26T19:06:27.529252Z",
+     "start_time": "2023-06-26T19:06:25.817825Z"
     }
    },
    "outputs": [],
@@ -701,21 +730,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 24,
    "id": "4ec194b0-2aeb-43b9-85c6-108bebb4a0e9",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:18.821816Z",
-     "end_time": "2023-05-03T13:22:18.824076Z"
+     "end_time": "2023-06-26T19:06:27.538169Z",
+     "start_time": "2023-06-26T19:06:27.535842Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "                                      Environment  \\\n0                  Hydrogen + fumarate, -O2, -Glc   \n1                                 Ribose C-source   \n2                              Thiamine salvaging   \n3                     Nitrate as reduction target   \n4  3-Phenylpropionate as carbon and energy source   \n5                   Molybdenum cofactor synthesis   \n6                         pre-Quenosine synthesis   \n\n                                               Genes  \\\n0  {EG11799, G7554, EG10468, EG10470, EG11801, EG...   \n1               {EG10814, EG10818, EG10815, EG10816}   \n2           {EG11572, EG11574, G6566, M007, EG11573}   \n3  {EG10640, EG10638, EG10639, EG10647, EG10648, ...   \n4                          {M011, G6206, M010, M012}   \n5  {EG11598, EG11666, EG10152, EG11597, EG11595, ...   \n6                              {G6245, G7452, G7431}   \n\n                              Add                        Remove  \\\n0  {HYDROGEN-MOLECULE[p], FUM[p]}  {OXYGEN-MOLECULE[p], GLC[p]}   \n1             {D-Ribopyranose[p]}                      {GLC[p]}   \n2                   {THIAMINE[p]}                            {}   \n3                    {NITRATE[p]}          {OXYGEN-MOLECULE[p]}   \n4         {3-PHENYLPROPIONATE[p]}                      {GLC[p]}   \n5                      {CPD-3[p]}                            {}   \n6                              {}                            {}   \n\n                              Demand  \\\n0                              False   \n1                              False   \n2                              False   \n3                              False   \n4                              False   \n5                      {CPD-8123[c]}   \n6  {7-AMINOMETHYL-7-DEAZAGUANINE[c]}   \n\n                                           Reactions  \n0                  {RXN0-7399, RXN0-5256, RXN-16420}  \n1                          {ABC-28-RXN, RIBOKIN-RXN}  \n2  {THIKIN-RXN, RXN0-7298, ABC-32-RXN, THIAZOLSYN...  \n3                  {RXN-15119, RXN0-3501, RXN0-7124}  \n4  {TRANS-RXN0-457, 1.13.11.16-RXN, TRANS-RXN-61,...  \n5  {RXN-8342, RXN0-262, RXN0-6254, RXN-8340, RXN-...  \n6                  {RXN-12093, RXN0-5507, RXN0-4022}  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hydrogen + fumarate, -O2, -Glc</td>\n      <td>{EG11799, G7554, EG10468, EG10470, EG11801, EG...</td>\n      <td>{HYDROGEN-MOLECULE[p], FUM[p]}</td>\n      <td>{OXYGEN-MOLECULE[p], GLC[p]}</td>\n      <td>False</td>\n      <td>{RXN0-7399, RXN0-5256, RXN-16420}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ribose C-source</td>\n      <td>{EG10814, EG10818, EG10815, EG10816}</td>\n      <td>{D-Ribopyranose[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>{ABC-28-RXN, RIBOKIN-RXN}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Thiamine salvaging</td>\n      <td>{EG11572, EG11574, G6566, M007, EG11573}</td>\n      <td>{THIAMINE[p]}</td>\n      <td>{}</td>\n      <td>False</td>\n      <td>{THIKIN-RXN, RXN0-7298, ABC-32-RXN, THIAZOLSYN...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Nitrate as reduction target</td>\n      <td>{EG10640, EG10638, EG10639, EG10647, EG10648, ...</td>\n      <td>{NITRATE[p]}</td>\n      <td>{OXYGEN-MOLECULE[p]}</td>\n      <td>False</td>\n      <td>{RXN-15119, RXN0-3501, RXN0-7124}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>{M011, G6206, M010, M012}</td>\n      <td>{3-PHENYLPROPIONATE[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>{TRANS-RXN0-457, 1.13.11.16-RXN, TRANS-RXN-61,...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>{EG11598, EG11666, EG10152, EG11597, EG11595, ...</td>\n      <td>{CPD-3[p]}</td>\n      <td>{}</td>\n      <td>{CPD-8123[c]}</td>\n      <td>{RXN-8342, RXN0-262, RXN0-6254, RXN-8340, RXN-...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>{G6245, G7452, G7431}</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{7-AMINOMETHYL-7-DEAZAGUANINE[c]}</td>\n      <td>{RXN-12093, RXN0-5507, RXN0-4022}</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "                                      Environment  \\\n0                  Hydrogen + fumarate, -O2, -Glc   \n1                                 Ribose C-source   \n2                              Thiamine salvaging   \n3                     Nitrate as reduction target   \n4  3-Phenylpropionate as carbon and energy source   \n5                   Molybdenum cofactor synthesis   \n6                         pre-Quenosine synthesis   \n\n                                               Genes  \\\n0  {EG11799, EG10470, EG10468, EG11801, EG11800, ...   \n1               {EG10816, EG10818, EG10815, EG10814}   \n2           {EG11574, EG11572, EG11573, M007, G6566}   \n3  {EG10638, EG10640, EG10648, EG10639, EG10644, ...   \n4                          {G6206, M011, M010, M012}   \n5  {EG11829, EG11666, EG10153, EG10152, EG11597, ...   \n6                              {G6245, G7431, G7452}   \n\n                              Add                        Remove  \\\n0  {HYDROGEN-MOLECULE[p], FUM[p]}  {OXYGEN-MOLECULE[p], GLC[p]}   \n1             {D-Ribopyranose[p]}                      {GLC[p]}   \n2                   {THIAMINE[p]}                            {}   \n3                    {NITRATE[p]}          {OXYGEN-MOLECULE[p]}   \n4         {3-PHENYLPROPIONATE[p]}                      {GLC[p]}   \n5                      {CPD-3[p]}                            {}   \n6                              {}                            {}   \n\n                              Demand  \\\n0                              False   \n1                              False   \n2                              False   \n3                              False   \n4                              False   \n5                      {CPD-8123[c]}   \n6  {7-AMINOMETHYL-7-DEAZAGUANINE[c]}   \n\n                                           Reactions  \n0                  {RXN0-7399, RXN0-5256, RXN-16420}  \n1                          {RIBOKIN-RXN, ABC-28-RXN}  \n2  {THIKIN-RXN, ABC-32-RXN, RXN0-7298, THIAZOLSYN...  \n3                  {RXN0-3501, RXN-15119, RXN0-7124}  \n4  {TRANS-RXN-61, MHPHYDROXY-RXN, RXN-10040, 1.13...  \n5  {RXN-11361, RXN-17809, RXN-8342, RXN-16457, RX...  \n6                  {RXN0-4022, RXN-12093, RXN0-5507}  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hydrogen + fumarate, -O2, -Glc</td>\n      <td>{EG11799, EG10470, EG10468, EG11801, EG11800, ...</td>\n      <td>{HYDROGEN-MOLECULE[p], FUM[p]}</td>\n      <td>{OXYGEN-MOLECULE[p], GLC[p]}</td>\n      <td>False</td>\n      <td>{RXN0-7399, RXN0-5256, RXN-16420}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ribose C-source</td>\n      <td>{EG10816, EG10818, EG10815, EG10814}</td>\n      <td>{D-Ribopyranose[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>{RIBOKIN-RXN, ABC-28-RXN}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Thiamine salvaging</td>\n      <td>{EG11574, EG11572, EG11573, M007, G6566}</td>\n      <td>{THIAMINE[p]}</td>\n      <td>{}</td>\n      <td>False</td>\n      <td>{THIKIN-RXN, ABC-32-RXN, RXN0-7298, THIAZOLSYN...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Nitrate as reduction target</td>\n      <td>{EG10638, EG10640, EG10648, EG10639, EG10644, ...</td>\n      <td>{NITRATE[p]}</td>\n      <td>{OXYGEN-MOLECULE[p]}</td>\n      <td>False</td>\n      <td>{RXN0-3501, RXN-15119, RXN0-7124}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>{G6206, M011, M010, M012}</td>\n      <td>{3-PHENYLPROPIONATE[p]}</td>\n      <td>{GLC[p]}</td>\n      <td>False</td>\n      <td>{TRANS-RXN-61, MHPHYDROXY-RXN, RXN-10040, 1.13...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>{EG11829, EG11666, EG10153, EG10152, EG11597, ...</td>\n      <td>{CPD-3[p]}</td>\n      <td>{}</td>\n      <td>{CPD-8123[c]}</td>\n      <td>{RXN-11361, RXN-17809, RXN-8342, RXN-16457, RX...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>{G6245, G7431, G7452}</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{7-AMINOMETHYL-7-DEAZAGUANINE[c]}</td>\n      <td>{RXN0-4022, RXN-12093, RXN0-5507}</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 94,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -734,12 +763,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 25,
    "id": "810fa6c8-b907-45b1-a1a7-d072f2295fe2",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:22:18.830617Z",
-     "end_time": "2023-05-03T13:22:54.413756Z"
+     "end_time": "2023-06-26T19:06:31.436967Z",
+     "start_time": "2023-06-26T19:06:27.546303Z"
     }
    },
    "outputs": [
@@ -763,16 +792,14 @@
     "for i in range(len(condition_df)):\n",
     "    condition_idx = i\n",
     "\n",
-    "    uptake = (fba[\"unconstrained_molecules\"][3]).copy()\n",
-    "    uptake_c = fba[\"constrained_molecules\"]\n",
+    "    uptake = metabolism.allowed_exchange_uptake\n",
     "\n",
-    "    homeostatic = dict(pd.DataFrame(fba[\"target_homeostatic_dmdt\"]).loc[4, :])\n",
+    "    homeostatic = dict(pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :])\n",
     "    new_demand = condition_df.at[condition_idx, \"Demand\"]\n",
     "    if new_demand:\n",
     "        for met in new_demand:\n",
     "            homeostatic[met] = 100\n",
     "\n",
-    "    uptake.extend(list(uptake_c.keys()))\n",
     "    uptake = set(uptake)\n",
     "\n",
     "    addition = condition_df.at[condition_idx, \"Add\"]\n",
@@ -781,22 +808,30 @@
     "    uptake = uptake | addition\n",
     "    uptake = uptake - removals\n",
     "\n",
-    "    model = NetworkFlowModel(reactions=stoichiometric_matrix_dict,\n",
-    "                         homeostatic_metabolites=homeostatic,\n",
-    "                         free_reactions=FREE_RXNS)\n",
+    "    model = NetworkFlowModel(\n",
+    "            stoich_arr=metabolism.stoichiometry,\n",
+    "            metabolites=metabolism.metabolite_names,\n",
+    "            reactions=metabolism.reaction_names,\n",
+    "            homeostatic_metabolites=list(dict(homeostatic).keys()),\n",
+    "            kinetic_reactions=list(dict(kinetic).keys()),\n",
+    "            free_reactions=FREE_RXNS)\n",
     "\n",
     "    model.set_up_exchanges(exchanges=metabolism.exchange_molecules, uptakes=uptake)\n",
     "\n",
-    "    solution: FlowResult = model.solve(homeostatic_targets=homeostatic,\n",
-    "                                   maintenance_target=maintenance,\n",
-    "                                   kinetic_targets=dict(kinetic),\n",
-    "                                   reaction_catalyst_counts=sim_catalysts.to_dict()[4],\n",
-    "                                   objective_weights={'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001},\n",
-    "                                   upper_flux_bound=100000000)\n",
+    "\n",
+    "    objective_weights = {'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001}\n",
+    "    solution: FlowResult = model.solve(\n",
+    "                homeostatic_targets=np.array(list(dict(homeostatic).values())),\n",
+    "                maintenance_target=maintenance,\n",
+    "                kinetic_targets=np.array(list(dict(kinetic).values())),\n",
+    "                binary_kinetic_idx=[],\n",
+    "                objective_weights=objective_weights,\n",
+    "                upper_flux_bound=100000000)\n",
     "\n",
     "    # Check flux\n",
-    "    fluxes = pd.DataFrame({model.rxns[i]: [solution.velocities[model.rxns[i]], stoichiometric_matrix_dict[model.rxns[i]]] for i in range(len(model.rxns))}).T\n",
-    "    fluxes.columns = [\"Flux\", \"Stoichiometry\"]\n",
+    "    fluxes = pd.Series(solution.velocities, index=metabolism.reaction_names)\n",
+    "    # fluxes = pd.DataFrame({model.rxns[i]: [solution.velocities[model.rxns[i]]] for i in range(len(model.rxns))}).T\n",
+    "    fluxes.columns = [\"Flux\"]\n",
     "\n",
     "    relevant_rxns = condition_df.at[condition_idx, \"Reactions\"]\n",
     "    relevant_rxn_idx = []\n",
@@ -804,9 +839,9 @@
     "    for rxn in relevant_rxns:\n",
     "        relevant_rxn_idx.extend(list(np.where(fluxes.index.str.contains(rxn))[0] ))\n",
     "\n",
-    "    relevant_fluxes = fluxes.iloc[relevant_rxn_idx, :]\n",
+    "    relevant_fluxes = fluxes.iloc[relevant_rxn_idx]\n",
     "    \n",
-    "    relevant_nonzero_fluxes = relevant_fluxes[relevant_fluxes[\"Flux\"] != 0].loc[:, \"Flux\"]\n",
+    "    relevant_nonzero_fluxes = relevant_fluxes[relevant_fluxes != 0]\n",
     "    \n",
     "    for j, k in enumerate(relevant_nonzero_fluxes.index):\n",
     "        results_df = results_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
@@ -818,21 +853,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 26,
    "id": "3feb60e7-d881-4b21-afc4-e501301874bc",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:38:34.731609Z",
-     "end_time": "2023-05-03T13:38:34.737887Z"
+     "end_time": "2023-06-26T19:06:31.444941Z",
+     "start_time": "2023-06-26T19:06:31.442657Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "                                      Environment              Reaction  \\\n0                  Hydrogen + fumarate, -O2, -Glc  RXN0-5256-CPD-9728/P   \n0                                 Ribose C-source  ABC-28-RXN-ATP/D-Rib   \n0                                 Ribose C-source  RIBOKIN-RXN-CPD0-110   \n0                                 Ribose C-source  RIBOKIN-RXN-CPD0-110   \n0                              Thiamine salvaging            THIKIN-RXN   \n0                              Thiamine salvaging            ABC-32-RXN   \n0                     Nitrate as reduction target  RXN0-3501-NITRATE/RE   \n0                     Nitrate as reduction target  RXN0-7124-NITRATE/CP   \n0  3-Phenylpropionate as carbon and energy source        1.13.11.16-RXN   \n0  3-Phenylpropionate as carbon and energy source        MHPCHYDROL-RXN   \n0                   Molybdenum cofactor synthesis              RXN-8342   \n0                   Molybdenum cofactor synthesis              RXN-8340   \n0                   Molybdenum cofactor synthesis             RXN-11361   \n0                   Molybdenum cofactor synthesis             RXN-17809   \n0                   Molybdenum cofactor synthesis            ABC-19-RXN   \n0                   Molybdenum cofactor synthesis              RXN-8348   \n0                         pre-Quenosine synthesis             RXN-12093   \n0                         pre-Quenosine synthesis             RXN0-5507   \n0                         pre-Quenosine synthesis   RXN0-4022 (reverse)   \n\n           Flux  log(Flux)  \n0  2.367649e+07   7.374317  \n0  2.029540e+06   6.307398  \n0  1.264890e+06   6.102053  \n0  7.646495e+05   5.883462  \n0  4.900000e+01   1.690196  \n0  4.900000e+01   1.690196  \n0  1.405236e+06   6.147749  \n0  2.350110e+05   5.371088  \n0  1.284798e+06   6.108835  \n0  1.284798e+06   6.108835  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  2.000000e+02   2.301030  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Reaction</th>\n      <th>Flux</th>\n      <th>log(Flux)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hydrogen + fumarate, -O2, -Glc</td>\n      <td>RXN0-5256-CPD-9728/P</td>\n      <td>2.367649e+07</td>\n      <td>7.374317</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Ribose C-source</td>\n      <td>ABC-28-RXN-ATP/D-Rib</td>\n      <td>2.029540e+06</td>\n      <td>6.307398</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Ribose C-source</td>\n      <td>RIBOKIN-RXN-CPD0-110</td>\n      <td>1.264890e+06</td>\n      <td>6.102053</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Ribose C-source</td>\n      <td>RIBOKIN-RXN-CPD0-110</td>\n      <td>7.646495e+05</td>\n      <td>5.883462</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Thiamine salvaging</td>\n      <td>THIKIN-RXN</td>\n      <td>4.900000e+01</td>\n      <td>1.690196</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Thiamine salvaging</td>\n      <td>ABC-32-RXN</td>\n      <td>4.900000e+01</td>\n      <td>1.690196</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Nitrate as reduction target</td>\n      <td>RXN0-3501-NITRATE/RE</td>\n      <td>1.405236e+06</td>\n      <td>6.147749</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Nitrate as reduction target</td>\n      <td>RXN0-7124-NITRATE/CP</td>\n      <td>2.350110e+05</td>\n      <td>5.371088</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>1.13.11.16-RXN</td>\n      <td>1.284798e+06</td>\n      <td>6.108835</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>MHPCHYDROL-RXN</td>\n      <td>1.284798e+06</td>\n      <td>6.108835</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-8342</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-8340</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-11361</td>\n      <td>2.000000e+02</td>\n      <td>2.301030</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-17809</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>ABC-19-RXN</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-8348</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>RXN-12093</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>RXN0-5507</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>RXN0-4022 (reverse)</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "                                      Environment              Reaction  \\\n0                  Hydrogen + fumarate, -O2, -Glc  RXN0-5256-CPD-9728/P   \n0                                 Ribose C-source  RIBOKIN-RXN-CPD0-110   \n0                                 Ribose C-source  RIBOKIN-RXN-CPD0-110   \n0                                 Ribose C-source  ABC-28-RXN-ATP/D-Rib   \n0                              Thiamine salvaging            THIKIN-RXN   \n0                              Thiamine salvaging            ABC-32-RXN   \n0                     Nitrate as reduction target  RXN0-3501-NITRATE/RE   \n0                     Nitrate as reduction target  RXN0-7124-NITRATE/CP   \n0  3-Phenylpropionate as carbon and energy source        1.13.11.16-RXN   \n0  3-Phenylpropionate as carbon and energy source        MHPCHYDROL-RXN   \n0                   Molybdenum cofactor synthesis             RXN-11361   \n0                   Molybdenum cofactor synthesis             RXN-17809   \n0                   Molybdenum cofactor synthesis              RXN-8342   \n0                   Molybdenum cofactor synthesis              RXN-8340   \n0                   Molybdenum cofactor synthesis              RXN-8348   \n0                   Molybdenum cofactor synthesis            ABC-19-RXN   \n0                         pre-Quenosine synthesis   RXN0-4022 (reverse)   \n0                         pre-Quenosine synthesis             RXN-12093   \n0                         pre-Quenosine synthesis             RXN0-5507   \n\n           Flux  log(Flux)  \n0  2.073151e+07   7.316631  \n0  8.324587e+05   5.920363  \n0  5.199004e+05   5.715920  \n0  1.352359e+06   6.131092  \n0  2.900000e+01   1.462398  \n0  2.900000e+01   1.462398  \n0  1.300256e+06   6.114029  \n0  1.756030e+05   5.244532  \n0  7.993023e+05   5.902711  \n0  7.993023e+05   5.902711  \n0  2.000000e+02   2.301030  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  \n0  1.000000e+02   2.000000  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Reaction</th>\n      <th>Flux</th>\n      <th>log(Flux)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Hydrogen + fumarate, -O2, -Glc</td>\n      <td>RXN0-5256-CPD-9728/P</td>\n      <td>2.073151e+07</td>\n      <td>7.316631</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Ribose C-source</td>\n      <td>RIBOKIN-RXN-CPD0-110</td>\n      <td>8.324587e+05</td>\n      <td>5.920363</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Ribose C-source</td>\n      <td>RIBOKIN-RXN-CPD0-110</td>\n      <td>5.199004e+05</td>\n      <td>5.715920</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Ribose C-source</td>\n      <td>ABC-28-RXN-ATP/D-Rib</td>\n      <td>1.352359e+06</td>\n      <td>6.131092</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Thiamine salvaging</td>\n      <td>THIKIN-RXN</td>\n      <td>2.900000e+01</td>\n      <td>1.462398</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Thiamine salvaging</td>\n      <td>ABC-32-RXN</td>\n      <td>2.900000e+01</td>\n      <td>1.462398</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Nitrate as reduction target</td>\n      <td>RXN0-3501-NITRATE/RE</td>\n      <td>1.300256e+06</td>\n      <td>6.114029</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Nitrate as reduction target</td>\n      <td>RXN0-7124-NITRATE/CP</td>\n      <td>1.756030e+05</td>\n      <td>5.244532</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>1.13.11.16-RXN</td>\n      <td>7.993023e+05</td>\n      <td>5.902711</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>3-Phenylpropionate as carbon and energy source</td>\n      <td>MHPCHYDROL-RXN</td>\n      <td>7.993023e+05</td>\n      <td>5.902711</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-11361</td>\n      <td>2.000000e+02</td>\n      <td>2.301030</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-17809</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-8342</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-8340</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>RXN-8348</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Molybdenum cofactor synthesis</td>\n      <td>ABC-19-RXN</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>RXN0-4022 (reverse)</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>RXN-12093</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>pre-Quenosine synthesis</td>\n      <td>RXN0-5507</td>\n      <td>1.000000e+02</td>\n      <td>2.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 108,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -845,12 +880,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 27,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 1688.28x720 with 1 Axes>",
-      "image/png": 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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -864,211 +899,9 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-03T13:40:35.617460Z",
-     "end_time": "2023-05-03T13:40:36.234754Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc585496-f8aa-4a33-95c6-080673fdacee",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Check kinetics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "outputs": [],
-   "source": [
-    "uptake = (fba[\"unconstrained_molecules\"][3]).copy()\n",
-    "uptake_c = fba[\"constrained_molecules\"]\n",
-    "\n",
-    "homeostatic = dict(pd.DataFrame(fba[\"target_homeostatic_dmdt\"]).loc[4, :])\n",
-    "exchanges = pd.DataFrame(fba[\"estimated_exchange_dmdt\"]).loc[4, :]\n",
-    "maintenance = pd.DataFrame(fba[\"maintenance_target\"]).at[4, 'maintenance_reaction']\n",
-    "kinetic = dict(pd.DataFrame(fba[\"target_kinetic_fluxes\"]).loc[4, :])\n",
-    "sim_fluxes = dict(pd.DataFrame(fba[\"estimated_fluxes\"]).loc[4, :])\n",
-    "\n",
-    "uptake.extend(list(uptake_c.keys()))\n",
-    "uptake = set(uptake)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-28T10:37:03.150602Z",
-     "end_time": "2023-04-28T10:37:03.236435Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "outputs": [],
-   "source": [
-    "model = NetworkFlowModel(reactions=stoichiometric_matrix_dict,\n",
-    "                         homeostatic_metabolites=metabolism.homeostatic_objective,\n",
-    "                         free_reactions=FREE_RXNS)\n",
-    "\n",
-    "model.set_up_exchanges(exchanges=metabolism.exchange_molecules, uptakes=metabolism.allowed_exchange_uptake)\n",
-    "\n",
-    "solution: FlowResult = model.solve(homeostatic_targets=homeostatic,\n",
-    "                                   maintenance_target=maintenance,\n",
-    "                                   kinetic_targets=kinetic,\n",
-    "                                   reaction_catalyst_counts=sim_catalysts.to_dict()[4],\n",
-    "                                   objective_weights={'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.00001},\n",
-    "                                   upper_flux_bound=100000000)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-28T10:02:20.823261Z",
-     "end_time": "2023-04-28T10:02:26.443574Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'1-ACYLGLYCEROL-3-P-ACYLTRANSFER-RXN': -0.0,\n '1.1.1.127-RXN': -0.0,\n '1.1.1.127-RXN (reverse)': -0.0,\n '1.1.1.215-RXN (reverse)': -0.0,\n '1.1.1.251-RXN': -0.0,\n '1.1.1.251-RXN (reverse)': -0.0,\n '1.1.1.271-RXN (reverse)': -0.0,\n '1.1.1.274-RXN (reverse)': -0.0,\n '1.1.1.283-RXN (reverse)': -0.0,\n '1.1.1.39-RXN': -0.0,\n '1.1.1.83-RXN': -0.0,\n '1.11.1.15-RXN': -0.0,\n '1.13.11.16-RXN': 0.0,\n '1.3.1.9-RXN (reverse)': -0.0,\n '1.5.1.20-RXN-5-METHYL-THF/NAD//METHYLENE-THF/NADH/PROTON.44.': -0.0,\n '1.5.1.20-RXN-5-METHYL-THF/NAD//METHYLENE-THF/NADH/PROTON.44. (reverse)': 15408.0,\n '1.5.1.20-RXN-CPD-1302/NAD//CPD-12996/NADH/PROTON.36.': -0.0,\n '1.5.1.20-RXN-CPD-1302/NAD//CPD-12996/NADH/PROTON.36. (reverse)': 3621.999999999991,\n '1.7.2.2-RXN-A-REDUCED-TORY-PROTEIN/NITRITE/PROTON//AMMONIUM/an-oxidized-TorY-protein/WATER.79.': -0.0,\n '1.7.2.2-RXN-Cytochromes-C-Reduced/NITRITE/PROTON//AMMONIUM/Cytochromes-C-Oxidized/WATER.76.': 0.0,\n '1.7.2.2-RXN-Cytochromes-C550-Red/NITRITE/PROTON//AMMONIUM/Cytochromes-C550-Ox/WATER.72.': 0.0,\n '1.7.2.2-RXN-Cytochromes-C552-Red/NITRITE/PROTON//AMMONIUM/Cytochromes-C552-Ox/WATER.72.': 0.0,\n '1.7.2.2-RXN-Cytochromes-C554-Red/NITRITE/PROTON//AMMONIUM/Cytochromes-C554-Ox/WATER.72.': 0.0,\n '1.7.2.2-RXN-Cytochromes-CL-Red/NITRITE/PROTON//AMMONIUM/Cytochromes-CL-Ox/WATER.68.': -0.0,\n '1.7.2.2-RXN-Cytochromes-c3-Reduced/NITRITE/PROTON//AMMONIUM/Cytochromes-c3-Oxidized/WATER.78.': 0.0,\n '1.7.2.2-RXN-Reduced-NapC-proteins/NITRITE/PROTON//AMMONIUM/Oxidized-NapC-proteins/WATER.76.': -0.0,\n '1.7.2.2-RXN-Reduced-cytochromes-c2/NITRITE/PROTON//AMMONIUM/Oxidized-cytochromes-c2/WATER.78.': -0.0,\n '1.7.2.2-RXN-Reduced-cytochromes-c553/NITRITE/PROTON//AMMONIUM/Oxidized-cytochromes-c553/WATER.82.': 0.0,\n '1.7.2.2-RXN-a-reduced-NrfB-protein/NITRITE/PROTON//AMMONIUM/an-oxidized-NrfB-protein/WATER.79.': -0.0,\n '1.7.2.2-RXN-a-reduced-TorC-protein/NITRITE/PROTON//AMMONIUM/an-oxidized-TorC-protein/WATER.79.': -0.0,\n '1.7.2.3-RXN (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Cytochromes-C550-Ox/WATER//TRIMETHYLAMINE-N-O/Cytochromes-C550-Red/PROTON.89. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Cytochromes-C552-Ox/WATER//TRIMETHYLAMINE-N-O/Cytochromes-C552-Red/PROTON.89. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Cytochromes-C554-Ox/WATER//TRIMETHYLAMINE-N-O/Cytochromes-C554-Red/PROTON.89. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Cytochromes-CL-Ox/WATER//TRIMETHYLAMINE-N-O/Cytochromes-CL-Red/PROTON.85. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Cytochromes-c3-Oxidized/WATER//TRIMETHYLAMINE-N-O/Cytochromes-c3-Reduced/PROTON.95. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Oxidized-NapC-proteins/WATER//TRIMETHYLAMINE-N-O/Reduced-NapC-proteins/PROTON.93. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Oxidized-cytochromes-c2/WATER//TRIMETHYLAMINE-N-O/Reduced-cytochromes-c2/PROTON.95. (reverse)': -0.0,\n '1.7.2.3-RXN-TRIMETHYLAMINE/Oxidized-cytochromes-c553/WATER//TRIMETHYLAMINE-N-O/Reduced-cytochromes-c553/PROTON.99. 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'3.2.2.10-RXN-CMP/WATER//RIBOSE-5P/CYTOSINE.30.': 4.999999999999999,\n '3.2.2.10-RXN-UMP/WATER//CPD-15318/URACIL.28.': -0.0,\n '3.2.2.10-RXN-UMP/WATER//CPD-15895/URACIL.28.': -0.0,\n '3.2.2.10-RXN-UMP/WATER//CPD-16551/URACIL.28.': -0.0,\n '3.2.2.10-RXN-UMP/WATER//RIBOSE-5P/URACIL.28.': -0.0,\n '3.2.2.23-RXN': 0.0,\n '3.4.11.1-RXN': -0.0,\n '3.4.11.18-RXN': -0.0,\n '3.4.11.2-RXN': -0.0,\n '3.4.11.4-RXN': -0.0,\n '3.4.11.9-RXN': -0.0,\n '3.4.13.18-RXN-D-ALA-D-ALA/WATER//L-ALPHA-ALANINE.35.': -0.0,\n '3.4.13.18-RXN-GLYCYLGLYCINE/WATER//GLY.25.': -0.0,\n '3.4.13.21-RXN': -0.0,\n '3.4.13.22-RXN': -0.0,\n '3.4.13.9-RXN-CPD0-2649/WATER//PRO/MET.25.': -0.0,\n '3.4.15.5-RXN': -0.0,\n '3.4.17.8-RXN': -0.0,\n '3.4.19.12-RXN': -0.0,\n '3.4.21.102-RXN-General-Protein-Substrates/WATER//Peptides-holder/PROTON.57.': -0.0,\n '3.4.21.107-RXN-General-Protein-Substrates/WATER//Peptides-holder/PROTON.57.': -0.0,\n '3.4.21.53-RXN-General-Protein-Substrates/WATER//Peptides-holder/PROTON.57.': -0.0,\n '3.4.21.92-RXN-General-Protein-Substrates/ATP/WATER//Peptides-holder/ADP/Pi/PROTON.68.': -0.0,\n '3.4.24.55-RXN-General-Protein-Substrates/WATER//Peptides-holder/PROTON.57.': -0.0,\n '3.5.1.88-RXN': -0.0,\n '3.5.2.17-RXN': -0.0,\n '3.6.1.41-RXN': -0.0,\n '3.6.3.23-RXN-ACETYLMURAMYL-ALANYL-ISOGLUTAMINE/ATP/WATER//ACETYLMURAMYL-ALANYL-ISOGLUTAMINE/ADP/Pi/PROTON.93.': -0.0,\n '3.6.3.23-RXN-ALA-GLY/ATP/WATER//ALA-GLY/ADP/Pi/PROTON.41.': -0.0,\n '3.6.3.23-RXN-CARNOSINE/ATP/WATER//CARNOSINE/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-10814/ATP/WATER//CPD-10814/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13390/ATP/WATER//CPD-13390/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13393/ATP/WATER//CPD-13393/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13394/ATP/WATER//CPD-13394/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13395/ATP/WATER//CPD-13395/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13397/ATP/WATER//CPD-13397/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13398/ATP/WATER//CPD-13398/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13401/ATP/WATER//CPD-13401/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13403/ATP/WATER//CPD-13403/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13404/ATP/WATER//CPD-13404/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-13406/ATP/WATER//CPD-13406/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-15384/ATP/WATER//CPD-15384/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-20340/ATP/WATER//CPD-20340/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-20746/ATP/WATER//CPD-20746/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-20750/ATP/WATER//CPD-20750/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD-3569/ATP/WATER//CPD-3569/ADP/Pi/PROTON.43.': -0.0,\n '3.6.3.23-RXN-CPD0-1082/ATP/WATER//CPD0-1082/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1085/ATP/WATER//CPD0-1085/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1445/ATP/WATER//CPD0-1445/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1620/ATP/WATER//CPD0-1620/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1906/ATP/WATER//CPD0-1906/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1907/ATP/WATER//CPD0-1907/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1908/ATP/WATER//CPD0-1908/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1909/ATP/WATER//CPD0-1909/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1910/ATP/WATER//CPD0-1910/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1911/ATP/WATER//CPD0-1911/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1913/ATP/WATER//CPD0-1913/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1915/ATP/WATER//CPD0-1915/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1944/ATP/WATER//CPD0-1944/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-1965/ATP/WATER//CPD0-1965/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2056/ATP/WATER//CPD0-2056/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2073/ATP/WATER//CPD0-2073/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2190/ATP/WATER//CPD0-2190/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2555/ATP/WATER//CPD0-2555/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2649/ATP/WATER//CPD0-2649/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2650/ATP/WATER//CPD0-2650/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CPD0-2651/ATP/WATER//CPD0-2651/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.23-RXN-CYS-GLY/ATP/WATER//CYS-GLY/ADP/Pi/PROTON.41.': 0.0,\n '3.6.3.23-RXN-D-ALA-D-ALA/ATP/WATER//D-ALA-D-ALA/ADP/Pi/PROTON.49.': -0.0,\n '3.6.3.23-RXN-GLYCYLGLYCINE/ATP/WATER//GLYCYLGLYCINE/ADP/Pi/PROTON.53.': -0.0,\n '3.6.3.23-RXN-L-ALA-GAMMA-D-GLU-DAP/ATP/WATER//L-ALA-GAMMA-D-GLU-DAP/ADP/Pi/PROTON.69.': -0.0,\n '3.6.3.23-RXN-L-GAMMA-GLUTAMYLCYSTEINE/ATP/WATER//L-GAMMA-GLUTAMYLCYSTEINE/ADP/Pi/PROTON.75.': -0.0,\n '3.6.3.39-RXN': -0.0,\n '3.6.3.39-RXN-CPD-15238/ATP/WATER//CPD-15238/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-15242/ATP/WATER//CPD-15242/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-21352/ATP/WATER//CPD-21352/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-21354/ATP/WATER//CPD-21354/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-21356/ATP/WATER//CPD-21356/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-21357/ATP/WATER//CPD-21357/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-21359/ATP/WATER//CPD-21359/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD-22536/ATP/WATER//CPD-22536/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD0-1151/ATP/WATER//CPD0-1151/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD0-2249/ATP/WATER//CPD0-2249/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD0-2294/ATP/WATER//CPD0-2294/ADP/Pi/PROTON.45.': -0.0,\n '3.6.3.39-RXN-CPD0-938/ATP/WATER//CPD0-938/ADP/Pi/PROTON.43.': -0.0,\n '3.6.3.39-RXN-CPD0-939/ATP/WATER//CPD0-939/ADP/Pi/PROTON.43.': -0.0,\n '3.6.3.39-RXN-KDO2-LIPID-A/ATP/WATER//KDO2-LIPID-A/ADP/Pi/PROTON.51.': -0.0,\n '3.6.3.39-RXN-KDO2-LIPID-IVA/ATP/WATER//KDO2-LIPID-IVA/ADP/Pi/PROTON.55.': -0.0,\n '3.6.3.53-RXN': -0.0,\n '325-BISPHOSPHATE-NUCLEOTIDASE-RXN': 22692.99999999998,\n '325-BISPHOSPHATE-NUCLEOTIDASE-RXN__EG10043-MONOMER': 10556.0,\n '3PGAREARR-RXN': 0.0,\n '3PGAREARR-RXN (reverse)': 1591252.4000000097,\n '4-HYDROXYBUTYRATE-DEHYDROGENASE-RXN (reverse)': -0.0,\n '4-NITROPHENYLPHOSPHATASE-RXN': -0.0,\n '4-NITROPHENYLPHOSPHATASE-RXN__ALKAPHOSPHA-CPLX': -0.0,\n '4.1.2.28-RXN': -0.0,\n '4.1.2.28-RXN (reverse)': -0.0,\n '4.2.1.58-RXN': -0.0,\n '4.2.1.59-RXN': -0.0,\n '4.2.1.61-RXN': -0.0,\n '4.2.1.99-RXN': -0.0,\n '4.2.1.99-RXN (reverse)': 0.0,\n '4.2.99.18-RXN': -0.0,\n '4.3.1.15-RXN': -0.0,\n '4.3.1.17-RXN': -0.0,\n '4OHBENZOATE-OCTAPRENYLTRANSFER-RXN': -0.0,\n '5-FORMYL-THF-CYCLO-LIGASE-RXN-5-FORMYL-THF/ATP//5-10-METHENYL-THF/ADP/Pi.43.': -0.0,\n '5-NUCLEOTID-RXN[CCO-CYTOSOL]-XANTHOSINE-5-PHOSPHATE/WATER//XANTHOSINE/Pi.57.': -0.0,\n '5-NUCLEOTID-RXN[CCO-CYTOSOL]-XANTHOSINE-5-PHOSPHATE/WATER//XANTHOSINE/Pi.57.__EG11817-MONOMER': -0.0,\n '5-NUCLEOTID-RXN[CCO-PERI-BAC]-XANTHOSINE-5-PHOSPHATE/WATER//XANTHOSINE/Pi.58.': -0.0,\n '5-OXOPROLINASE-ATP-HYDROLYSING-RXN': 0.0,\n '5.1.3.20-RXN': 7692.0,\n '5.3.1.17-RXN': -0.0,\n '5.3.1.17-RXN (reverse)': -0.0,\n '5.3.3.14-RXN': -0.0,\n '5.3.3.14-RXN (reverse)': -0.0,\n '5.4.2.10-RXN': 0.0,\n '5.4.2.10-RXN (reverse)': -0.0,\n '5.4.2.10-RXN-CPD-13469//GLUCOSAMINE-1P.26.': 16347.999999999998,\n '5.4.2.10-RXN-CPD-13469//GLUCOSAMINE-1P.26. (reverse)': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//ALPHA-GLC-6-P/ALPHA-GLUCOSE.45.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//ALPHA-GLC-6-P/GLC.35.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//ALPHA-GLC-6-P/Glucopyranose.45.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//D-glucopyranose-6-phosphate/ALPHA-GLUCOSE.59.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//D-glucopyranose-6-phosphate/GLC.49.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//GLC-6-P/ALPHA-GLUCOSE.39.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//GLC-6-P/GLC.29.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-15978/WATER//GLC-6-P/Glucopyranose.39.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//ALPHA-GLC-6-P/ALPHA-GLUCOSE.43.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//ALPHA-GLC-6-P/GLC.33.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//ALPHA-GLC-6-P/Glucopyranose.43.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//D-glucopyranose-6-phosphate/ALPHA-GLUCOSE.57.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//D-glucopyranose-6-phosphate/GLC.47.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//D-glucopyranose-6-phosphate/Glucopyranose.57.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//GLC-6-P/ALPHA-GLUCOSE.37.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//GLC-6-P/GLC.27.': -0.0,\n '6-PHOSPHO-BETA-GLUCOSIDASE-RXN-CPD-507/WATER//GLC-6-P/Glucopyranose.37.': -0.0,\n '6PFRUCTPHOS-RXN__6PFK-1-CPX': 400720.08333334816,\n '6PFRUCTPHOS-RXN__6PFK-2-CPX': 33153.0,\n '6PGLUCONOLACT-RXN': 1149154.2333333257,\n '7-ALPHA-HYDROXYSTEROID-DEH-RXN': -0.0,\n 'ABC-10-RXN': -0.0,\n 'ABC-11-RXN-ATP/CPD0-2114/WATER//ADP/CPD0-2114/Pi/PROTON.45.': -0.0,\n 'ABC-11-RXN-ATP/CPD0-2241/WATER//ADP/CPD0-2241/Pi/PROTON.45.': -0.0,\n 'ABC-11-RXN-ATP/CPD0-621/WATER//ADP/CPD0-621/Pi/PROTON.43.': -0.0,\n 'ABC-12-RXN': -0.0,\n 'ABC-13-RXN': -0.0,\n 'ABC-14-RXN': -0.0,\n 'ABC-15-RXN': -0.0,\n 'ABC-16-RXN-MALTOSE/ATP/WATER//MALTOSE/ADP/Pi/PROTON.41.': -0.0,\n 'ABC-18-RXN': 0.0,\n 'ABC-18-RXN-ATP/ALPHA-D-GALACTOSE/WATER//ADP/ALPHA-D-GALACTOSE/Pi/PROTON.61.': -0.0,\n 'ABC-18-RXN-ATP/ALPHA-D-GALACTOSE/WATER//ADP/D-galactopyranose/Pi/PROTON.61.': -0.0,\n 'ABC-18-RXN-ATP/ALPHA-D-GALACTOSE/WATER//ADP/GALACTOSE/Pi/PROTON.53.': -0.0,\n 'ABC-18-RXN-ATP/D-galactopyranose/WATER//ADP/ALPHA-D-GALACTOSE/Pi/PROTON.61.': -0.0,\n 'ABC-18-RXN-ATP/D-galactopyranose/WATER//ADP/D-galactopyranose/Pi/PROTON.61.': 0.0,\n 'ABC-18-RXN-ATP/D-galactopyranose/WATER//ADP/GALACTOSE/Pi/PROTON.53.': -0.0,\n 'ABC-18-RXN-ATP/GALACTOSE/WATER//ADP/ALPHA-D-GALACTOSE/Pi/PROTON.53.': -0.0,\n 'ABC-18-RXN-ATP/GALACTOSE/WATER//ADP/D-galactopyranose/Pi/PROTON.53.': -0.0,\n 'ABC-18-RXN-ATP/GALACTOSE/WATER//ADP/GALACTOSE/Pi/PROTON.45.': -0.0,\n 'ABC-19-RXN': -0.0,\n 'ABC-2-RXN': 0.0,\n 'ABC-2-RXN-ATP/ARABINOSE/WATER//ADP/ARABINOSE/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/ARABINOSE/WATER//ADP/CPD-12045/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/ARABINOSE/WATER//ADP/CPD-12046/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/ARABINOSE/WATER//ADP/CPD-15699/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/ARABINOSE/WATER//ADP/L-ARABINOSE/Pi/PROTON.47.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12045/WATER//ADP/ARABINOSE/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12045/WATER//ADP/CPD-12045/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12045/WATER//ADP/CPD-12046/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12045/WATER//ADP/CPD-15699/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12045/WATER//ADP/L-ARABINOSE/Pi/PROTON.47.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12046/WATER//ADP/ARABINOSE/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12046/WATER//ADP/CPD-12045/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12046/WATER//ADP/CPD-12046/Pi/PROTON.45.': -0.0,\n 'ABC-2-RXN-ATP/CPD-12046/WATER//ADP/CPD-15699/Pi/PROTON.45.': 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(reverse)': 0.0,\n 'ENOYL-COA-DELTA-ISOM-RXN-POLYMER-INST-CIS-DELTA3-ENOYL-COA-C2-H4//CPD0-2121.51.': 0.0,\n 'ENOYL-COA-DELTA-ISOM-RXN-POLYMER-INST-CIS-DELTA3-ENOYL-COA-C2-H4//CPD0-2121.51. (reverse)': -0.0,\n 'ENOYL-COA-HYDRAT-RXN-CPD0-2171//CPD-15568/WATER.27. (reverse)': -0.0,\n 'ENOYL-COA-HYDRAT-RXN-CPD0-2253//CPD-10262/WATER.27. (reverse)': -0.0,\n 'ENOYL-COA-HYDRAT-RXN-POLYMER-INST-L-3-HYDROXYACYL-COA-C12-H24//CPD0-2117/WATER.58. (reverse)': -0.0,\n 'ENOYL-COA-HYDRAT-RXN-POLYMER-INST-L-3-HYDROXYACYL-COA-C16-H32//POLYMER-INST-TRANS-D2-ENOYL-COA-C16-H32/WATER.88. (reverse)': 0.0,\n 'ENTDB-RXN': -0.0,\n 'ENTF-RXN': -0.0,\n 'ENTG-RXN': -0.0,\n 'ENTMULTI-RXN': 53.99999999999999,\n 'ENVZ-RXN': -0.0,\n 'ERYTH4PDEHYDROG-RXN': 48.99999999999999,\n 'ERYTHRON4PDEHYDROG-RXN': 48.99999999999999,\n 'ETHAMLY-RXN': 0.0,\n 'EVGA-RXN': -0.0,\n 'F16ALDOLASE-RXN (reverse)': -0.0,\n 'F16ALDOLASE-RXN__FRUCBISALD-CLASSI': -0.0,\n 'F16ALDOLASE-RXN__FRUCBISALD-CLASSII': 430519.0833333481,\n 'F16BDEPHOS-RXN[CCO-CYTOSOL]-FRUCTOSE-16-DIPHOSPHATE/WATER//FRUCTOSE-6P/Pi.59.__CPLX0-303': -0.0,\n 'F16BDEPHOS-RXN[CCO-CYTOSOL]-FRUCTOSE-16-DIPHOSPHATE/WATER//FRUCTOSE-6P/Pi.59.__CPLX0-7776': -0.0,\n 'F16BDEPHOS-RXN[CCO-CYTOSOL]-FRUCTOSE-16-DIPHOSPHATE/WATER//FRUCTOSE-6P/Pi.59.__EG11239-MONOMER': -0.0,\n 'F16BDEPHOS-RXN[CCO-CYTOSOL]-FRUCTOSE-16-DIPHOSPHATE/WATER//FRUCTOSE-6P/Pi.59.__F16B-CPLX': -0.0,\n 'F16BDEPHOS-RXN[CCO-PERI-BAC]-FRUCTOSE-16-DIPHOSPHATE/WATER//FRUCTOSE-6P/Pi.60.': -0.0,\n 'FAD-OX-RXN': -0.0,\n 'FAD-RED-RXN-Ox-FAD-Flavoproteins/NADH/PROTON//Red-FADH2-Flavoproteins/NAD.62.': -0.0,\n 'FAD-RED-RXN-Ox-FAD-Flavoproteins/NADPH/PROTON//Red-FADH2-Flavoproteins/NADP.64.': -0.0,\n 'FADSYN-RXN': 91.0,\n 'FCLEPIM-RXN': -0.0,\n 'FCLREDUCT-RXN (reverse)': -0.0,\n 'FERREDOXIN--NAD+-REDUCTASE-RXN (reverse)': 10.999999999999998,\n 'FGAMSYN-RXN': 43851.00000000398,\n 'FHLMULTI-RXN': -0.0,\n 'FLAVONADPREDUCT-RXN (reverse)': -0.0,\n 'FMN-OX-RXN': -0.0,\n 'FMN-RED-RXN-Ox-FMN-Flavoproteins/NADH/PROTON//Red-FMNH2-Flavoproteins/NAD.62.': -0.0,\n 'FMN-RED-RXN-Ox-FMN-Flavoproteins/NADPH/PROTON//Red-FMNH2-Flavoproteins/NADP.64.': -0.0,\n 'FORMATEDEHYDROG-RXN-FORMATE/CPD-9728/PROTON//CARBON-DIOXIDE/REDUCED-MENAQUINONE/PROTON.67.': -0.0,\n 'FORMATEDEHYDROG-RXN-FORMATE/CPD-9728/PROTON//CARBON-DIOXIDE/REDUCED-MENAQUINONE/PROTON.67.__FORMATEDEHYDROGN-CPLX': -0.0,\n 'FORMYLTHFDEFORMYL-RXN-10-FORMYL-THF/WATER//THF/FORMATE/PROTON.40.': -0.0,\n 'FPPSYN-RXN': 4083.0000000000005,\n 'FRUCTOKINASE-RXN': -0.0,\n 'FUC4NACTRANS-RXN': 0.0,\n 'FUCISOM-RXN': -0.0,\n 'FUCISOM-RXN (reverse)': -0.0,\n 'FUCPALDOL-RXN': -0.0,\n 'FUCULOKIN-RXN': -0.0,\n 'FUMHYDR-RXN': -0.0,\n ...}"
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solution.velocities"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-28T10:02:26.457570Z",
-     "end_time": "2023-04-28T10:02:26.460895Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "                                              Reaction    Target  Estimated\n0                                         1.1.1.39-RXN  123358.0       -0.0\n1                                         1.1.1.83-RXN   10129.0       -0.0\n2                                       1.13.11.16-RXN     133.0        0.0\n3    1.5.1.20-RXN-5-METHYL-THF/NAD//METHYLENE-THF/N...   15408.0    15408.0\n4    2.1.1.79-RXN-S-ADENOSYLMETHIONINE/CPD-18361//C...    3129.0       -0.0\n..                                                 ...       ...        ...\n425  UNDECAPRENYL-DIPHOSPHATASE-RXN[CCO-CYTOSOL]-UN...     574.0      574.0\n426                                     URA-PHOSPH-RXN    7558.0       -0.0\n427                                   UROGENIIISYN-RXN   62030.0      107.0\n428                            URPHOS-RXN__URPHOS-CPLX  542101.0       -0.0\n429                                      XYLULOKIN-RXN   11537.0        0.0\n\n[430 rows x 3 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Reaction</th>\n      <th>Target</th>\n      <th>Estimated</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.1.1.39-RXN</td>\n      <td>123358.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.1.1.83-RXN</td>\n      <td>10129.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.13.11.16-RXN</td>\n      <td>133.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.5.1.20-RXN-5-METHYL-THF/NAD//METHYLENE-THF/N...</td>\n      <td>15408.0</td>\n      <td>15408.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.1.1.79-RXN-S-ADENOSYLMETHIONINE/CPD-18361//C...</td>\n      <td>3129.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>425</th>\n      <td>UNDECAPRENYL-DIPHOSPHATASE-RXN[CCO-CYTOSOL]-UN...</td>\n      <td>574.0</td>\n      <td>574.0</td>\n    </tr>\n    <tr>\n      <th>426</th>\n      <td>URA-PHOSPH-RXN</td>\n      <td>7558.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>427</th>\n      <td>UROGENIIISYN-RXN</td>\n      <td>62030.0</td>\n      <td>107.0</td>\n    </tr>\n    <tr>\n      <th>428</th>\n      <td>URPHOS-RXN__URPHOS-CPLX</td>\n      <td>542101.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>429</th>\n      <td>XYLULOKIN-RXN</td>\n      <td>11537.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>430 rows × 3 columns</p>\n</div>"
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comparison = {key: [kinetic[key], solution.velocities[key]] for key in kinetic.keys()}\n",
-    "comparison = pd.DataFrame(comparison).T.reset_index()\n",
-    "comparison.columns = [\"Reaction\", \"Target\", \"Estimated\"]\n",
-    "# comparison = comparison.melt(id_vars=\"Reaction\", value_vars=[\"Target\", \"Estimated\"], var_name=\"Type\", value_name=\"Flux\")\n",
-    "comparison"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-28T10:02:26.469053Z",
-     "end_time": "2023-04-28T10:02:26.494442Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.scatterplot(x=\"Target\", y=\"Estimated\", data=comparison)\n",
-    "plt.yscale('log')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-28T10:02:26.477709Z",
-     "end_time": "2023-04-28T10:02:26.576911Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Evaluate actual sim fluxes"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "dict_keys(['estimated_fluxes', 'estimated_homeostatic_dmdt', 'target_homeostatic_dmdt', 'target_kinetic_fluxes', 'estimated_exchange_dmdt', 'estimated_intermediate_dmdt', 'maintenance_target', 'reaction_catalyst_counts', 'time_per_step', 'unconstrained_molecules', 'constrained_molecules'])"
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fba.keys()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "                                              Reaction    Target  Estimated\n0                                         1.1.1.39-RXN  123358.0       -0.0\n1                                         1.1.1.83-RXN   10129.0       -0.0\n2                                       1.13.11.16-RXN     133.0        0.0\n3    1.5.1.20-RXN-5-METHYL-THF/NAD//METHYLENE-THF/N...   15408.0    15408.0\n4    2.1.1.79-RXN-S-ADENOSYLMETHIONINE/CPD-18361//C...    3129.0       -0.0\n..                                                 ...       ...        ...\n425  UNDECAPRENYL-DIPHOSPHATASE-RXN[CCO-CYTOSOL]-UN...     574.0      574.0\n426                                     URA-PHOSPH-RXN    7558.0       -0.0\n427                                   UROGENIIISYN-RXN   62030.0      107.0\n428                            URPHOS-RXN__URPHOS-CPLX  542101.0       -0.0\n429                                      XYLULOKIN-RXN   11537.0        0.0\n\n[430 rows x 3 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Reaction</th>\n      <th>Target</th>\n      <th>Estimated</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.1.1.39-RXN</td>\n      <td>123358.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.1.1.83-RXN</td>\n      <td>10129.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.13.11.16-RXN</td>\n      <td>133.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.5.1.20-RXN-5-METHYL-THF/NAD//METHYLENE-THF/N...</td>\n      <td>15408.0</td>\n      <td>15408.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.1.1.79-RXN-S-ADENOSYLMETHIONINE/CPD-18361//C...</td>\n      <td>3129.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>425</th>\n      <td>UNDECAPRENYL-DIPHOSPHATASE-RXN[CCO-CYTOSOL]-UN...</td>\n      <td>574.0</td>\n      <td>574.0</td>\n    </tr>\n    <tr>\n      <th>426</th>\n      <td>URA-PHOSPH-RXN</td>\n      <td>7558.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>427</th>\n      <td>UROGENIIISYN-RXN</td>\n      <td>62030.0</td>\n      <td>107.0</td>\n    </tr>\n    <tr>\n      <th>428</th>\n      <td>URPHOS-RXN__URPHOS-CPLX</td>\n      <td>542101.0</td>\n      <td>-0.0</td>\n    </tr>\n    <tr>\n      <th>429</th>\n      <td>XYLULOKIN-RXN</td>\n      <td>11537.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>430 rows × 3 columns</p>\n</div>"
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
+     "end_time": "2023-06-26T19:06:32.082647Z",
+     "start_time": "2023-06-26T19:06:31.451785Z"
     }
-   ],
-   "source": [
-    "sim_comparison = {key: [kinetic[key], solution.velocities[key]] for key in kinetic.keys()}\n",
-    "sim_comparison = pd.DataFrame(sim_comparison).T.reset_index()\n",
-    "sim_comparison.columns = [\"Reaction\", \"Target\", \"Estimated\"]\n",
-    "# comparison = comparison.melt(id_vars=\"Reaction\", value_vars=[\"Target\", \"Estimated\"], var_name=\"Type\", value_name=\"Flux\")\n",
-    "sim_comparison"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
    }
   }
  ],
diff --git a/notebooks/new_environments/Fe_S_pathway.ipynb b/notebooks/new_environments/Fe_S_pathway.ipynb
new file mode 100644
index 000000000..33efaf528
--- /dev/null
+++ b/notebooks/new_environments/Fe_S_pathway.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "id": "8022bfb4-c69b-4cb7-a24c-fec017cc3661",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:45.597821Z",
+     "start_time": "2023-07-18T18:19:45.527026Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import requests\n",
+    "import xmltodict\n",
+    "import cvxpy as cp\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]\n",
+    "\n",
+    "sns.set(style='darkgrid', palette='viridis', context='talk')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a143cbbb-3570-4548-bc24-f499c070a880",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Import simulation output\n",
+    "\n",
+    "Before running this, I ran a sim in ecoli/experiments/metabolism_redux_sim.py with -n 2 to generate a simulation output. This is necessary to replicate the simulation environment, where we e.g. have dynamic demand for amino acids and nucleotides, and varying cell volumes.\n",
+    "\n",
+    "You don't have to do this for now. We'll use a single time step from the simulation output to test the network flow model on its own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-06-26'\n",
+    "experiment = 'fba-redux'\n",
+    "entry = f'{experiment}_{time}_{date}'\n",
+    "folder = f'out/fbagd/{entry}/'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:45.688383Z",
+     "start_time": "2023-07-18T18:19:45.533623Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "outputs": [],
+   "source": [
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "# output = np.load(r\"out/geneRxnVerifData/output_glc.npy\", allow_pickle=True, encoding='ASCII').tolist()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:47.449179Z",
+     "start_time": "2023-07-18T18:19:45.541372Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "outputs": [],
+   "source": [
+    "# this is the model itself - we'll pull e.g. stoichiometry from here.\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:51.465654Z",
+     "start_time": "2023-07-18T18:19:47.464138Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "outputs": [],
+   "source": [
+    "homeostatic = pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :]\n",
+    "maintenance = pd.DataFrame(fba[\"maintenance_target\"][1:], columns=['maintenance_reaction']).iat[24, 0]\n",
+    "kinetic = pd.DataFrame(fba[\"target_kinetic_fluxes\"], columns=metabolism.kinetic_constraint_reactions).loc[24, :]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:51.511073Z",
+     "start_time": "2023-07-18T18:19:51.477125Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28db669f-93f3-4f5c-aea0-e23b6a811660",
+   "metadata": {},
+   "source": [
+    "## Connect to EcoCyc to go from Genes -> Reactions\n",
+    "\n",
+    "We use the EcoCyc API to automatically figure out what are made possible by the genes we are adding. While reactions are always possible, enzymes make them faster to the point that it is useful to the cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "a59449e4-be36-49c8-94ed-35c14f5bb72d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:51.780525Z",
+     "start_time": "2023-07-18T18:19:51.502956Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "outputs": [],
+   "source": [
+    "added_gene_to_reaction = {\"EG10941\": [\"putative-L-selenocysteine-synthesis\"],\n",
+    "                          \"G7325\": [\"cysteine-desulfurase-FeS-cluster-assembly\"],\n",
+    "                          \"EG12132\": [\"iron-transport-FeS-cluster-assembly\"],\n",
+    "                          \"G7324\": [\"complex-formation-FeS-cluster-assembly\"],\n",
+    "                          \"EG12130\": [\"2Fe2S-cluster-transport-FeS-cluster-assembly\"],\n",
+    "                          \"EG12332\": [\"4Fe4S-cluster-transport-FeS-cluster-assembly\"],\n",
+    "                          \"EG11328\": [\"cysteine-desulfurase-IscS-formation-FeS-cluster-assembly\", \"2Fe2S-conversion\"]\n",
+    "                          # Missing HscB with HscA complex\n",
+    "                          }"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:51.787727Z",
+     "start_time": "2023-07-18T18:19:51.784199Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "ab016fac-3d48-49fb-85c7-63826a8f5664",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:51.858355Z",
+     "start_time": "2023-07-18T18:19:51.797431Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "             Environment                                              Genes  \\\n0  Testing Fe-S Clusters  {EG12132, G7325, EG12130, EG11328, EG12332, G7...   \n\n                                                 Add Remove  \\\n0  {CYS[c], reduced-ferredoxin[c], S-SULFANYL-cys...     {}   \n\n                                         Demand Reactions Substrates  \n0  {oxidized-ferredoxin[c], CPD-6[c], CPD-7[c]}     [nan]      [nan]  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n      <th>Substrates</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>{EG12132, G7325, EG12130, EG11328, EG12332, G7...</td>\n      <td>{CYS[c], reduced-ferredoxin[c], S-SULFANYL-cys...</td>\n      <td>{}</td>\n      <td>{oxidized-ferredoxin[c], CPD-6[c], CPD-7[c]}</td>\n      <td>[nan]</td>\n      <td>[nan]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition_df = pd.read_excel(\"notebooks/new_environments/temp.xlsx\")\n",
+    "condition_df[\"Genes\"] = condition_df[\"Genes\"].apply(lambda x: set(x.split(\", \") if x == x else set()))\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Demand\"] = condition_df[\"Demand\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Reactions\"] = [[np.nan]] * len(condition_df)\n",
+    "condition_df[\"Substrates\"] = [[np.nan]] * len(condition_df)\n",
+    "\n",
+    "condition_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "outputs": [],
+   "source": [
+    "length = len(condition_df.index)\n",
+    "\n",
+    "for i in range(length):\n",
+    "    genes = condition_df.loc[i, \"Genes\"]\n",
+    "    rxn_set = set()\n",
+    "    added_rxn_set = set()\n",
+    "\n",
+    "    for gene in genes:\n",
+    "        function = \"reactions-of-gene\"\n",
+    "        name = gene\n",
+    "        req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{name}&detail=none&fmt=json\"\n",
+    "\n",
+    "        r = s.get(req_str)\n",
+    "        if gene in added_gene_to_reaction.keys():\n",
+    "            for elem in added_gene_to_reaction[gene]:\n",
+    "                added_rxn_set.add(elem)\n",
+    "        else:\n",
+    "            o = xmltodict.parse(r.content)['ptools-xml']['Reaction']\n",
+    "            if type(o) is dict:\n",
+    "                o = [o]\n",
+    "\n",
+    "            for rxn in o:\n",
+    "                if type(rxn) is not str: # no rxns\n",
+    "                    rxn_set.add(rxn['@frameid'])\n",
+    "                else:\n",
+    "                    print(gene, rxn)\n",
+    "\n",
+    "    overall_list = []\n",
+    "    for reaction in rxn_set:\n",
+    "        compound_set = set()\n",
+    "        function = \"substrates-of-reaction\"\n",
+    "        name = reaction\n",
+    "        req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{name}&detail=none&fmt=json\"\n",
+    "\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Compound']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for compound in o:\n",
+    "            if type(compound) is not str:\n",
+    "                compound_set.add(compound['@frameid'])\n",
+    "            else:\n",
+    "                print(reaction, compound)\n",
+    "\n",
+    "        overall_list.append(compound_set)\n",
+    "\n",
+    "    condition_df.at[i, \"Reactions\"] = rxn_set | added_rxn_set\n",
+    "    # condition_df.at[i, \"Reactions\"].update(added_rxn_set)\n",
+    "    condition_df.at[i, \"Substrates\"] = overall_list\n",
+    "    # condition_df.loc[i + length] = [condition_df.at[i, \"Environment\"] + \" CONTROL\", set(), set(), set(), set(), condition_df.at[i, \"Reactions\"], []]\n",
+    "    # This is to create the control"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:52.249175Z",
+     "start_time": "2023-07-18T18:19:51.848628Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "             Environment                                              Genes  \\\n0  Testing Fe-S Clusters  {EG12132, G7325, EG12130, EG11328, EG12332, G7...   \n\n                                                 Add Remove  \\\n0  {CYS[c], reduced-ferredoxin[c], S-SULFANYL-cys...     {}   \n\n                                         Demand  \\\n0  {oxidized-ferredoxin[c], CPD-6[c], CPD-7[c]}   \n\n                                           Reactions Substrates  \n0  {complex-formation-FeS-cluster-assembly, iron-...         []  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n      <th>Substrates</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>{EG12132, G7325, EG12130, EG11328, EG12332, G7...</td>\n      <td>{CYS[c], reduced-ferredoxin[c], S-SULFANYL-cys...</td>\n      <td>{}</td>\n      <td>{oxidized-ferredoxin[c], CPD-6[c], CPD-7[c]}</td>\n      <td>{complex-formation-FeS-cluster-assembly, iron-...</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:19:52.271602Z",
+     "start_time": "2023-07-18T18:19:52.264527Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Quick and dirty but workhorse loop (after init)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished Testing Fe-S Clusters\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(columns=[\"Environment\", \"Reaction\", \"Flux\"])\n",
+    "zero_df = pd.DataFrame()\n",
+    "all_flux = []\n",
+    "\n",
+    "reactions_add = {\"putative-L-selenocysteine-synthesis\": {'SEPO3[c]': -1, 'SER[c]': -1, 'Pi[c]': 1, 'L-SELENOCYSTEINE[c]': 1},\n",
+    "                 \"cysteine-desulfurase-FeS-cluster-assembly\": {'CYS[c]': -1, 'cysteine-desulfurase[c]': -1, 'L-ALPHA-ALANINE[c]': 1, 'S-SULFANYL-IscS[c]': 1},\n",
+    "                 \"iron-transport-FeS-cluster-assembly\": {'FE+2[c]': -1, 'DISORDERED-FdS-IscU[c]': 1},\n",
+    "                 \"complex-formation-FeS-cluster-assembly\": {'DISORDERED-FdS-IscU[c]': -1, 'S-SULFANYL-IscS[c]': -1, 'S-SULFANYL-DISORDERED-FdS-IscU[c]': 1},\n",
+    "                 \"2Fe2S-cluster-transport-FeS-cluster-assembly\": {'S-SULFANYL-DISORDERED-FdS-IscU[c]': -1, 'ATP[c]': -1, 'ADP[c]': 1, 'CPD-6[c]': 1},\n",
+    "                 \"2Fe2S-conversion\": {'S-SULFANYL-DISORDERED-FdS-IscU[c]': -1, '4Fe4S-S-SULFANYL-DISORDERED-FdS-IscU[c]': 1},\n",
+    "                 \"4Fe4S-cluster-transport-FeS-cluster-assembly\": {'4Fe4S-S-SULFANYL-DISORDERED-FdS-IscU[c]': -1, 'ATP[c]': -1, 'ADP[c]': 1, 'CPD-7[c]': 1},\n",
+    "                 \"cysteine-desulfurase-IscS-formation-FeS-cluster-assembly\": {'S-SULFANYL-cysteine-desulfurase[c]': -1, 'ATP[c]': -1, 'reduced-ferredoxin[c]': -1, 'PROTON[c]': -1, 'AMP[c]': 1, 'oxidized-ferredoxin[c]': 1, 'cysteine-desulfurase[c]': 1\n",
+    "                 } # TRNA-S-TRANSFERASE-RXN, missing the tRNA cytosines!\n",
+    "                 # Insert other added reactions here\n",
+    "                 }\n",
+    "\n",
+    "metabolites_add = ['S-SULFANYL-IscS[c]', 'DISORDERED-FdS-IscU[c]', 'S-SULFANYL-DISORDERED-FdS-IscU[c]', 'CPD-6[c]', 'cysteine-desulfurase[c]', 'S-SULFANYL-cysteine-desulfurase[c]', 'reduced-ferredoxin[c]', 'oxidized-ferredoxin[c]', '4Fe4S-S-SULFANYL-DISORDERED-FdS-IscU[c]', 'CPD-7[c]']\n",
+    "\n",
+    "catalyst_add = {\"putative-L-selenocysteine-synthesis\": 'CPLX0-1141[c]',\n",
+    "                \"cysteine-desulfurase-FeS-cluster-assembly\": 'G7325-MONOMER[c]',\n",
+    "                \"iron-transport-FeS-cluster-assembly\": 'EG12132-MONOMER[c]',\n",
+    "                \"complex-formation-FeS-cluster-assembly\": 'G7324-DIMER[c]',\n",
+    "                \"2Fe2S-conversion\": 'EG11328-MONOMER[c]',\n",
+    "                \"2Fe2S-cluster-transport-FeS-cluster-assembly\": 'HSC-Complex[c]',\n",
+    "                \"4Fe4S-cluster-transport-FeS-cluster-assembly\": 'EG12332-MONOMER[c]',\n",
+    "                'cysteine-desulfurase-IscS-formation-FeS-cluster-assembly': 'EG11328-MONOMER[c]'}\n",
+    "\n",
+    "for j in range(len(condition_df)):\n",
+    "    metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "    reaction_catalysts = metabolism.parameters['reaction_catalysts']\n",
+    "    metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "    catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}\n",
+    "\n",
+    "    new_stoich = metabolism.stoichiometry.copy()\n",
+    "    new_metabolites = metabolism.metabolite_names.copy()\n",
+    "    new_reaction_names = metabolism.reaction_names.copy()\n",
+    "\n",
+    "    condition_idx = j\n",
+    "\n",
+    "    uptake = metabolism.allowed_exchange_uptake\n",
+    "\n",
+    "    homeostatic = dict(pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :])\n",
+    "    new_demand = condition_df.at[condition_idx, \"Demand\"]\n",
+    "    if new_demand != set():\n",
+    "        for met in new_demand:\n",
+    "            homeostatic[met] = 0\n",
+    "            flat_demand_add = 100\n",
+    "            if met not in metabolites_add:\n",
+    "                function = \"reactions-of-compound\"\n",
+    "                name = met[:-3]\n",
+    "                req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{name}&detail=none&fmt=json\"\n",
+    "\n",
+    "                r = s.get(req_str)\n",
+    "                o = xmltodict.parse(r.content)['ptools-xml']['Reaction']\n",
+    "                if type(o) is dict:\n",
+    "                    o = [o]\n",
+    "                for rxn in o:\n",
+    "                    if type(rxn) is not str: # no rxns\n",
+    "                        function = \"substrates-of-reaction\"\n",
+    "                        req_str = f\"https://websvc.biocyc.org/getxml?id=ECOLI:{rxn['@frameid']}&detail=low\"\n",
+    "                        r = s.get(req_str)\n",
+    "                        left = xmltodict.parse(r.content)['ptools-xml']['Reaction']['left']\n",
+    "                        right = xmltodict.parse(r.content)['ptools-xml']['Reaction']['right']\n",
+    "                        if type(left) is dict:\n",
+    "                            left = [left]\n",
+    "                        if type(right) is dict:\n",
+    "                            right = [right]\n",
+    "                        for compound in left:\n",
+    "                            if type(compound) is not str:\n",
+    "                                try:\n",
+    "                                    if compound['Compound']['@frameid'] == name:\n",
+    "                                        for demand_compound in right:\n",
+    "                                            try:\n",
+    "                                                if type(demand_compound) is not str and demand_compound['Compound']['@frameid'] in homeostatic:\n",
+    "                                                    homeostatic[met] += homeostatic[demand_compound['Compound']['@frameid']]\n",
+    "                                            except KeyError:\n",
+    "                                                pass\n",
+    "                                except KeyError:\n",
+    "                                    pass\n",
+    "\n",
+    "            homeostatic[met] += flat_demand_add\n",
+    "            # Maybe look at what the demanded metabolite goes into and use that quantity + a flat value\n",
+    "\n",
+    "    uptake = set(uptake)\n",
+    "    exchange = set(metabolism.exchange_molecules)\n",
+    "\n",
+    "    addition = condition_df.at[condition_idx, \"Add\"]\n",
+    "    removals = condition_df.at[condition_idx, \"Remove\"]\n",
+    "\n",
+    "    uptake = uptake | addition\n",
+    "    uptake = uptake - removals\n",
+    "\n",
+    "    exchange = exchange | uptake\n",
+    "\n",
+    "    for reaction in condition_df.at[condition_idx, \"Reactions\"]:\n",
+    "        if reaction in catalyst_add:\n",
+    "            catalyst_idx[catalyst_add[reaction]] = len(catalyst_idx)\n",
+    "\n",
+    "        if reaction in reactions_add:\n",
+    "            for metabolite in reactions_add[reaction]:\n",
+    "                if metabolite in metabolites_add and metabolite not in metabolites_idx:\n",
+    "                    new_row = np.zeros([1, np.shape(new_stoich)[1]])\n",
+    "                    new_stoich = np.append(new_stoich, new_row, axis=0)\n",
+    "                    new_metabolites.append(metabolite)\n",
+    "                    metabolites_idx[metabolite] = len(metabolites_idx)\n",
+    "\n",
+    "            new_reaction_names.append(reaction)\n",
+    "            new_col = np.zeros([len(new_stoich), 1])\n",
+    "            new_stoich = np.append(new_stoich, new_col, axis=1)\n",
+    "\n",
+    "            for species, coefficient in reactions_add[reaction].items():\n",
+    "                i = metabolites_idx[species]\n",
+    "                new_stoich[i, np.shape(new_stoich)[1] - 1] = coefficient\n",
+    "\n",
+    "            # now adds an entry for every reaction, list can be empty\n",
+    "            reaction_catalysts[reaction] = [catalyst_add[reaction]]\n",
+    "            # Taken from reconstruction/ecoli.../complexation reactions to find the complex that the gene forms\n",
+    "            enzyme_idx = [catalyst_idx[catalyst] for catalyst in reaction_catalysts.get(reaction, [])]\n",
+    "            metabolism.catalyzed_rxn_enzymes_idx.append(enzyme_idx)\n",
+    "\n",
+    "    model = NetworkFlowModel(\n",
+    "            stoich_arr=new_stoich,\n",
+    "            metabolites=new_metabolites,\n",
+    "            reactions=new_reaction_names,\n",
+    "            homeostatic_metabolites=list(dict(homeostatic).keys()),\n",
+    "            kinetic_reactions=list(dict(kinetic).keys()))\n",
+    "\n",
+    "    model.set_up_exchanges(exchanges=exchange, uptakes=uptake)\n",
+    "\n",
+    "    objective_weights = {'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001}\n",
+    "    solution: FlowResult = model.solve(\n",
+    "            homeostatic_targets=np.array(list(dict(homeostatic).values())),\n",
+    "            maintenance_target=maintenance,\n",
+    "            kinetic_targets=np.array(list(dict(kinetic).values())),\n",
+    "            binary_kinetic_idx=[],\n",
+    "            objective_weights=objective_weights,\n",
+    "            upper_flux_bound=100000000)\n",
+    "\n",
+    "    # Check flux\n",
+    "    fluxes = pd.Series(solution.velocities, index=new_reaction_names)\n",
+    "    # fluxes = pd.DataFrame({model.rxns[i]: [solution.velocities[model.rxns[i]]] for i in range(len(model.rxns))}).T\n",
+    "    fluxes.columns = [\"Flux\"]\n",
+    "    all_flux.append(fluxes)\n",
+    "\n",
+    "    if condition_df.at[condition_idx, \"Environment\"][-7:] == \"CONTROL\":\n",
+    "        control_df = pd.DataFrame()\n",
+    "        flux_threshold = 100\n",
+    "        for j, k in enumerate(fluxes.index):\n",
+    "            if abs(all_flux[condition_idx - length][k] - fluxes[k]) / abs(fluxes[k] + 0.01) >= flux_threshold:\n",
+    "                control_df = control_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
+    "                          fluxes.index[j],\n",
+    "                         (all_flux[condition_idx - length][k] - fluxes[k]) / (fluxes[k] + 0.01) ]], columns=[\"Environment\", \"Reaction\", \"(Experiment - Control)/(Control + 0.01) Flux\"]))\n",
+    "        if len(control_df) > 0:\n",
+    "            control_df = control_df.sort_values(by=[\"(Experiment - Control)/(Control + 0.01) Flux\"], ascending=False)\n",
+    "        control_df.to_csv(rf'notebooks/new_environments/figures/control_differences/{condition_df.at[condition_idx, \"Environment\"]}.csv')\n",
+    "    else:\n",
+    "        relevant_rxns = condition_df.at[condition_idx, \"Reactions\"]\n",
+    "        relevant_rxn_idx = []\n",
+    "\n",
+    "        for rxn in relevant_rxns:\n",
+    "            relevant_rxn_idx.extend(list(np.where(fluxes.index.str.contains(rxn))[0] ))\n",
+    "\n",
+    "        relevant_fluxes = fluxes.iloc[relevant_rxn_idx]\n",
+    "\n",
+    "        relevant_nonzero_fluxes = relevant_fluxes[relevant_fluxes != 0]\n",
+    "        zero_fluxes = relevant_fluxes[relevant_fluxes == 0]\n",
+    "\n",
+    "        for j, k in enumerate(relevant_nonzero_fluxes.index):\n",
+    "            results_df = results_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
+    "                          relevant_nonzero_fluxes.index[j],\n",
+    "                          relevant_nonzero_fluxes[k]]], columns=[\"Environment\", \"Reaction\", \"Flux\"]))\n",
+    "\n",
+    "        for j, k in enumerate(zero_fluxes.index):\n",
+    "            zero_df = zero_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
+    "                          zero_fluxes.index[j],\n",
+    "                          zero_fluxes[k]]], columns=[\"Environment\", \"Reaction\", \"Flux\"]))\n",
+    "\n",
+    "    print(f\"\"\"Finished {condition_df.at[condition_idx, \"Environment\"]}\"\"\")\n",
+    "\n",
+    "    index = metabolites_idx[\"S-SULFANYL-DISORDERED-FdS-IscU[c]\"]\n",
+    "    np.savetxt('notebooks/new_environments/metabolite', new_stoich[:,np.shape(new_stoich)[1] - 4])\n",
+    "zero_df.to_csv(r'notebooks/new_environments/figures/zero_flux.csv')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:20:00.582174Z",
+     "start_time": "2023-07-18T18:19:52.269293Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "             Environment                                           Reaction  \\\n0  Testing Fe-S Clusters             complex-formation-FeS-cluster-assembly   \n0  Testing Fe-S Clusters                iron-transport-FeS-cluster-assembly   \n0  Testing Fe-S Clusters          cysteine-desulfurase-FeS-cluster-assembly   \n0  Testing Fe-S Clusters  cysteine-desulfurase-IscS-formation-FeS-cluste...   \n0  Testing Fe-S Clusters       2Fe2S-cluster-transport-FeS-cluster-assembly   \n0  Testing Fe-S Clusters                                   2Fe2S-conversion   \n0  Testing Fe-S Clusters       4Fe4S-cluster-transport-FeS-cluster-assembly   \n\n    Flux  log(Flux)  \n0  200.0    2.30103  \n0  200.0    2.30103  \n0  200.0    2.30103  \n0  200.0    2.30103  \n0  100.0    2.00000  \n0  100.0    2.00000  \n0  100.0    2.00000  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Reaction</th>\n      <th>Flux</th>\n      <th>log(Flux)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>complex-formation-FeS-cluster-assembly</td>\n      <td>200.0</td>\n      <td>2.30103</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>iron-transport-FeS-cluster-assembly</td>\n      <td>200.0</td>\n      <td>2.30103</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>cysteine-desulfurase-FeS-cluster-assembly</td>\n      <td>200.0</td>\n      <td>2.30103</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>cysteine-desulfurase-IscS-formation-FeS-cluste...</td>\n      <td>200.0</td>\n      <td>2.30103</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>2Fe2S-cluster-transport-FeS-cluster-assembly</td>\n      <td>100.0</td>\n      <td>2.00000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>2Fe2S-conversion</td>\n      <td>100.0</td>\n      <td>2.00000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Testing Fe-S Clusters</td>\n      <td>4Fe4S-cluster-transport-FeS-cluster-assembly</td>\n      <td>100.0</td>\n      <td>2.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 262,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# results_df[\"Reaction\"] = results_df['Reaction'].str.slice(0,20)\n",
+    "results_df[\"log(Flux)\"] = np.log10(results_df[\"Flux\"])\n",
+    "results_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:20:00.582764Z",
+     "start_time": "2023-07-18T18:20:00.573306Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "outputs": [],
+   "source": [
+    "# for each environment, plot the fluxes of the reactions that are relevant to that environment.\n",
+    "# g = sns.catplot(y=\"Environment\",hue=\"Reaction\", x=\"log(Flux)\", data=results_df, kind=\"bar\", aspect=2, height=10, sharey=False)\n",
+    "# g.savefig(\"notebooks/new_environments/figures/rxns_light_up.pdf\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:20:00.588073Z",
+     "start_time": "2023-07-18T18:20:00.580528Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "outputs": [],
+   "source": [
+    "import textwrap\n",
+    "\n",
+    "def wrap_x_labels(ax, width, break_long_words=False):\n",
+    "    labels = []\n",
+    "    for label in ax.get_xticklabels():\n",
+    "        text = label.get_text()\n",
+    "        labels.append(textwrap.fill(text, width=width,\n",
+    "                      break_long_words=break_long_words))\n",
+    "    ax.set_xticklabels(labels, rotation=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:20:00.600312Z",
+     "start_time": "2023-07-18T18:20:00.589596Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 300x600 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", rc={'figure.dpi': 300}, font_scale=0.5)\n",
+    "fig, ax = plt.subplots(figsize=(len(results_df[\"Environment\"].unique()), 2))\n",
+    "ax.set_title(\"Non-zero Fluxes\")\n",
+    "ax = sns.barplot(data=results_df, x=\"Environment\", y=\"log(Flux)\", ax=ax, hue=\"Reaction\")\n",
+    "ax.legend(bbox_to_anchor=(1, 1), title='Reactions', loc='upper left', ncol = 1)\n",
+    "wrap_x_labels(ax, 10)\n",
+    "fig = ax.get_figure()\n",
+    "plt.show(fig)\n",
+    "fig.savefig(\"notebooks/new_environments/figures/vert_bar_rxns.png\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-18T18:20:01.512004Z",
+     "start_time": "2023-07-18T18:20:00.600977Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/new_environments/new_genes.xlsx b/notebooks/new_environments/new_genes.xlsx
new file mode 100644
index 000000000..258433331
Binary files /dev/null and b/notebooks/new_environments/new_genes.xlsx differ
diff --git a/notebooks/new_environments/test_environments_new.ipynb b/notebooks/new_environments/test_environments_new.ipynb
new file mode 100644
index 000000000..792a4e74a
--- /dev/null
+++ b/notebooks/new_environments/test_environments_new.ipynb
@@ -0,0 +1,914 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8022bfb4-c69b-4cb7-a24c-fec017cc3661",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:27:41.384047Z",
+     "start_time": "2023-07-10T22:27:05.112853Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import requests\n",
+    "import xmltodict\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]\n",
+    "\n",
+    "sns.set(style='darkgrid', palette='viridis', context='talk')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a143cbbb-3570-4548-bc24-f499c070a880",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Import simulation output\n",
+    "\n",
+    "Before running this, I ran a sim in ecoli/experiments/metabolism_redux_sim.py with -n 2 to generate a simulation output. This is necessary to replicate the simulation environment, where we e.g. have dynamic demand for amino acids and nucleotides, and varying cell volumes.\n",
+    "\n",
+    "You don't have to do this for now. We'll use a single time step from the simulation output to test the network flow model on its own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "time = '50'\n",
+    "date = '2023-06-26'\n",
+    "experiment = 'fba-redux'\n",
+    "entry = f'{experiment}_{time}_{date}'\n",
+    "folder = f'out/fbagd/{entry}/'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:27:41.504575Z",
+     "start_time": "2023-07-10T22:27:41.186466Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "output = np.load(folder + 'output.npy',allow_pickle='TRUE').item()\n",
+    "# output = np.load(r\"out/geneRxnVerifData/output_glc.npy\", allow_pickle=True, encoding='ASCII').tolist()\n",
+    "output = output['agents']['0']\n",
+    "fba = output['listeners']['fba_results']\n",
+    "mass = output['listeners']['mass']\n",
+    "bulk = pd.DataFrame(output['bulk'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:27:59.820242Z",
+     "start_time": "2023-07-10T22:27:41.411204Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "# this is the model itself - we'll pull e.g. stoichiometry from here.\n",
+    "f = open(folder + 'agent_steps.pkl', 'rb')\n",
+    "agent = dill.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "metabolism = agent['ecoli-metabolism-redux']\n",
+    "stoichiometry = metabolism.stoichiometry"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:30.846738Z",
+     "start_time": "2023-07-10T22:27:58.305589Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Check molecule exchanges from simulation - sanity check."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1080x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sim_exchanges = pd.DataFrame(fba[\"estimated_exchange_dmdt\"])\n",
+    "# sim_exchanges = pd.DataFrame(sim_exchanges.loc[24, :].abs().sort_values(ascending=False))\n",
+    "#\n",
+    "# # plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
+    "# plt.figure(figsize=(15, 5))\n",
+    "# plt.plot(sim_exchanges.head(20), 'o')\n",
+    "#\n",
+    "# # shorten all xtick names to 10 characters\n",
+    "# plt.xticks([i for i in range(20)], [i[:20] for i in sim_exchanges.index[:20]], rotation=45, ha= 'right')\n",
+    "# # make xticks aligned to right left of bar\n",
+    "# plt.yscale('log')\n",
+    "# plt.ylabel('Flux counts')\n",
+    "# plt.title('Top 10 Fluxes')\n",
+    "# plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:35.313499Z",
+     "start_time": "2023-07-10T22:28:30.863688Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Check reaction fluxes from simulation - sanity check."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1080x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sim_fluxes = pd.DataFrame(fba[\"estimated_fluxes\"], columns = metabolism.reaction_names)\n",
+    "# sim_fluxes = pd.DataFrame(sim_fluxes.loc[24, :].abs().sort_values(ascending=False))\n",
+    "#\n",
+    "#\n",
+    "# # plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
+    "# plt.figure(figsize=(15, 5))\n",
+    "# plt.plot(sim_fluxes.head(20), 'o')\n",
+    "#\n",
+    "# # shorten all xtick names to 10 characters\n",
+    "# plt.xticks([i for i in range(20)], [i[:20] for i in sim_fluxes.index[:20]], rotation=45, ha= 'right')\n",
+    "# # make xticks aligned to right left of bar\n",
+    "#\n",
+    "#\n",
+    "# plt.ylabel('Flux counts')\n",
+    "# # log scale\n",
+    "# plt.yscale('log')\n",
+    "# plt.title('Top 10 Fluxes')\n",
+    "# plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:37.967609Z",
+     "start_time": "2023-07-10T22:28:35.306915Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                                          4\nRXN0-1741-MALTOSE//MALTOSE.17.                      24833.0\nRXN0-0-CPD0-1123//N-ACETYLNEURAMINATE.31.           24807.0\nRXN-15315                                           24807.0\nRXN0-0-CPD0-1122//CPD0-1122.21.                     24807.0\nRXN0-0-CPD0-1122//N-ACETYLNEURAMINATE.31.           24807.0\n...                                                     ...\nBETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTO...      0.0\nBETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTO...      0.0\nBETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/A...      0.0\nBETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/G...      0.0\nRXN0-963                                                0.0\n\n[7504 rows x 1 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>RXN0-1741-MALTOSE//MALTOSE.17.</th>\n      <td>24833.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-0-CPD0-1123//N-ACETYLNEURAMINATE.31.</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>RXN-15315</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-0-CPD0-1122//CPD0-1122.21.</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-0-CPD0-1122//N-ACETYLNEURAMINATE.31.</th>\n      <td>24807.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTOSE/GLC.35.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-Alpha-lactose/WATER//GALACTOSE/Glucopyranose.45.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/ALPHA-GLUCOSE.41.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>BETAGALACTOSID-RXN-CPD-15972/WATER//GALACTOSE/GLC.31.</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>RXN0-963</th>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>7504 rows × 1 columns</p>\n</div>"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sim_catalysts = pd.DataFrame(fba[\"reaction_catalyst_counts\"], columns=metabolism.reaction_names)\n",
+    "# sim_catalysts = pd.DataFrame(sim_catalysts.loc[4, :].abs().sort_values(ascending=False))\n",
+    "# sim_catalysts"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:39.821251Z",
+     "start_time": "2023-07-10T22:28:37.962368Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Test changing nutrient composition"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'AMMONIUM[c]',\n 'CA+2[p]',\n 'CARBON-DIOXIDE[p]',\n 'CL-[p]',\n 'CO+2[p]',\n 'FE+2[p]',\n 'GLC[p]',\n 'K+[p]',\n 'L-SELENOCYSTEINE[c]',\n 'MG+2[p]',\n 'MN+2[p]',\n 'NA+[p]',\n 'NI+2[p]',\n 'OXYGEN-MOLECULE[p]',\n 'Pi[p]',\n 'SULFATE[p]',\n 'WATER[p]',\n 'ZN+2[p]'}"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# uptake = metabolism.allowed_exchange_uptake\n",
+    "# uptake = set(uptake)\n",
+    "#\n",
+    "# # addition = set([\"CPD0-1074[c]\"])\n",
+    "# addition = set([\n",
+    "#         # \"FRU[p]\",\n",
+    "#         # \"NITRATE[p]\"\n",
+    "#         # 'THIAMINE[p]',\n",
+    "#         # 'HYDROGEN-MOLECULE[p]',\n",
+    "#         # 'FUM[p]'\n",
+    "# ])\n",
+    "# removals = set([\n",
+    "#                   # \"GLC[p]\",\n",
+    "#                #      # 'AMMONIUM[c]',\n",
+    "#                #      # 'WATER[p]'\n",
+    "#                #       # 'Pi[p]',\n",
+    "#                #       # 'SULFATE[p]'\n",
+    "#                # 'OXYGEN-MOLECULE[p]'\n",
+    "# ])\n",
+    "#\n",
+    "#\n",
+    "# uptake = uptake | addition\n",
+    "# uptake = uptake - removals\n",
+    "# uptake"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:39.974652Z",
+     "start_time": "2023-07-10T22:28:39.796551Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Use NetworkFlowModel class to run model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "homeostatic = pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :]\n",
+    "maintenance = pd.DataFrame(fba[\"maintenance_target\"][1:], columns=['maintenance_reaction']).iat[24, 0]\n",
+    "kinetic = pd.DataFrame(fba[\"target_kinetic_fluxes\"], columns=metabolism.kinetic_constraint_reactions).loc[24, :]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:40.342788Z",
+     "start_time": "2023-07-10T22:28:39.825406Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "model = NetworkFlowModel(\n",
+    "            stoich_arr=metabolism.stoichiometry,\n",
+    "            metabolites=metabolism.metabolite_names,\n",
+    "            reactions=metabolism.reaction_names,\n",
+    "            homeostatic_metabolites=list(dict(homeostatic).keys()),\n",
+    "            kinetic_reactions=list(dict(kinetic).keys()),\n",
+    "            free_reactions=FREE_RXNS)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:45.137167Z",
+     "start_time": "2023-07-10T22:28:39.829153Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "model.set_up_exchanges(exchanges=metabolism.exchange_molecules, uptakes=metabolism.allowed_exchange_uptake)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:45.216756Z",
+     "start_time": "2023-07-10T22:28:45.144887Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [],
+   "source": [
+    "objective_weights = {'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001}\n",
+    "solution: FlowResult = model.solve(\n",
+    "            homeostatic_targets=np.array(list(dict(homeostatic).values())),\n",
+    "            maintenance_target=maintenance,\n",
+    "            kinetic_targets=np.array(list(dict(kinetic).values())),\n",
+    "            binary_kinetic_idx=[],\n",
+    "            objective_weights=objective_weights,\n",
+    "            upper_flux_bound=100000000)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:47.371218Z",
+     "start_time": "2023-07-10T22:28:45.189137Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "5182    8.249992e+06\n639     2.490931e+06\n4382    2.221422e+06\n3617    1.074952e+06\n446     9.069960e+05\n            ...     \n1775    0.000000e+00\n1774    0.000000e+00\n1773    0.000000e+00\n1772    0.000000e+00\n5313    0.000000e+00\nLength: 5314, dtype: float64"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(solution.exchanges).abs().sort_values(ascending=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:47.467987Z",
+     "start_time": "2023-07-10T22:28:47.398107Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 2160x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cur_exchanges = pd.Series(solution.exchanges, index=metabolism.metabolite_names).abs().sort_values(ascending=False)\n",
+    "nonzero = len(cur_exchanges[cur_exchanges > 0])\n",
+    "\n",
+    "# plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
+    "plt.figure(figsize=(30, 5))\n",
+    "plt.plot(cur_exchanges.head(nonzero), 'o')\n",
+    "\n",
+    "# shorten all xtick names to 10 characters\n",
+    "plt.xticks([i for i in range(nonzero)], [i[:20] for i in cur_exchanges.index[:nonzero]], rotation=45, ha= 'right')\n",
+    "# make xticks aligned to right left of bar\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Flux counts')\n",
+    "plt.title('Top 10 Fluxes')\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:50.137991Z",
+     "start_time": "2023-07-10T22:28:47.448653Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1080x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cur_fluxes = pd.Series(solution.velocities, index=metabolism.reaction_names).abs().sort_values(ascending=False)\n",
+    "\n",
+    "# plot the top 10 fluxes from sim_fluxes as a bar plot.\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "plt.plot(cur_fluxes.head(20), 'o')\n",
+    "\n",
+    "# shorten all xtick names to 10 characters\n",
+    "plt.xticks([i for i in range(20)], [i[:20] for i in cur_fluxes.index[:20]], rotation=45, ha= 'right')\n",
+    "# make xticks aligned to right left of bar\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Flux counts')\n",
+    "plt.title('Top 10 Fluxes')\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:51.809110Z",
+     "start_time": "2023-07-10T22:28:50.089286Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28db669f-93f3-4f5c-aea0-e23b6a811660",
+   "metadata": {},
+   "source": [
+    "## Connect to EcoCyc to go from Genes -> Reactions\n",
+    "\n",
+    "We use the EcoCyc API to automatically figure out what are made possible by the genes we are adding. While reactions are always possible, enzymes make them faster to the point that it is useful to the cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a59449e4-be36-49c8-94ed-35c14f5bb72d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:52.496434Z",
+     "start_time": "2023-07-10T22:28:51.805642Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Response [200]>"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = requests.Session() # create session\n",
+    "# Post login credentials to session:\n",
+    "s.post('https://websvc.biocyc.org/credentials/login/', data={'email':'cellulararchitect@protonmail.com', 'password':'Cellman0451'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [],
+   "source": [
+    "added_gene_to_reaction = {\"EG10941\": \"putative-L-selenocysteine-synthesis\"\n",
+    "                          # Associate added reactions with genes here\n",
+    "                          }"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:52.499965Z",
+     "start_time": "2023-07-10T22:28:52.352426Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ab016fac-3d48-49fb-85c7-63826a8f5664",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:53.330198Z",
+     "start_time": "2023-07-10T22:28:52.376226Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                Environment      Genes                 Add Remove  \\\n0  Testing L-Selenocysteine  {EG10941}  {SER[c], SEPO3[c]}     {}   \n\n                  Demand Reactions Substrates  \n0  {L-SELENOCYSTEINE[c]}     [nan]      [nan]  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n      <th>Substrates</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Testing L-Selenocysteine</td>\n      <td>{EG10941}</td>\n      <td>{SER[c], SEPO3[c]}</td>\n      <td>{}</td>\n      <td>{L-SELENOCYSTEINE[c]}</td>\n      <td>[nan]</td>\n      <td>[nan]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition_df = pd.read_excel(\"notebooks/new_environments/temp.xlsx\")\n",
+    "condition_df[\"Genes\"] = condition_df[\"Genes\"].apply(lambda x: set(x.split(\", \") if x == x else set()))\n",
+    "condition_df[\"Add\"] = condition_df[\"Add\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Remove\"] = condition_df[\"Remove\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Demand\"] = condition_df[\"Demand\"].apply(lambda x: set(str(x).split(\", \") if x == x else set()))\n",
+    "condition_df[\"Reactions\"] = [[np.nan]] * len(condition_df)\n",
+    "condition_df[\"Substrates\"] = [[np.nan]] * len(condition_df)\n",
+    "\n",
+    "condition_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [],
+   "source": [
+    "length = len(condition_df.index)\n",
+    "\n",
+    "for i in range(length):\n",
+    "    genes = condition_df.loc[i, \"Genes\"]\n",
+    "    rxn_set = set()\n",
+    "    added_rxn_set = set()\n",
+    "\n",
+    "    for gene in genes:\n",
+    "        function = \"reactions-of-gene\"\n",
+    "        name = gene\n",
+    "        req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{name}&detail=none&fmt=json\"\n",
+    "\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Reaction']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for rxn in o:\n",
+    "            if type(rxn) is not str: # no rxns\n",
+    "                rxn_set.add(rxn['@frameid'])\n",
+    "            else:\n",
+    "                print(gene, rxn)\n",
+    "\n",
+    "        if gene in added_gene_to_reaction.keys():\n",
+    "            added_rxn_set.add(added_gene_to_reaction[gene])\n",
+    "\n",
+    "    condition_df.at[i, \"Reactions\"] = rxn_set\n",
+    "\n",
+    "    overall_list = []\n",
+    "    for reaction in rxn_set:\n",
+    "        compound_set = set()\n",
+    "        function = \"substrates-of-reaction\"\n",
+    "        name = reaction\n",
+    "        req_str = f\"https://websvc.biocyc.org/apixml?fn={function}&id=ECOLI:{name}&detail=none&fmt=json\"\n",
+    "\n",
+    "        r = s.get(req_str)\n",
+    "        o = xmltodict.parse(r.content)['ptools-xml']['Compound']\n",
+    "        if type(o) is dict:\n",
+    "            o = [o]\n",
+    "\n",
+    "        for compound in o:\n",
+    "            if type(compound) is not str:\n",
+    "                compound_set.add(compound['@frameid'])\n",
+    "            else:\n",
+    "                print(reaction, compound)\n",
+    "\n",
+    "        overall_list.append(compound_set)\n",
+    "\n",
+    "    condition_df.at[i, \"Reactions\"].update(added_rxn_set)\n",
+    "    condition_df.at[i, \"Substrates\"] = overall_list\n",
+    "    condition_df.loc[i + length] = [condition_df.at[i, \"Environment\"] + \" CONTROL\", set(), set(), set(), set(), condition_df.at[i, \"Reactions\"], []]\n",
+    "    # This is to create the control"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:53.776477Z",
+     "start_time": "2023-07-10T22:28:53.362916Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4ec194b0-2aeb-43b9-85c6-108bebb4a0e9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:28:53.778343Z",
+     "start_time": "2023-07-10T22:28:53.686832Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                        Environment      Genes                 Add Remove  \\\n0          Testing L-Selenocysteine  {EG10941}  {SER[c], SEPO3[c]}     {}   \n1  Testing L-Selenocysteine CONTROL         {}                  {}     {}   \n\n                  Demand                                          Reactions  \\\n0  {L-SELENOCYSTEINE[c]}  {2.9.1.1-RXN, putative-L-selenocysteine-synthe...   \n1                     {}  {2.9.1.1-RXN, putative-L-selenocysteine-synthe...   \n\n      Substrates  \n0  [{Pi, SEPO3}]  \n1             []  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Genes</th>\n      <th>Add</th>\n      <th>Remove</th>\n      <th>Demand</th>\n      <th>Reactions</th>\n      <th>Substrates</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Testing L-Selenocysteine</td>\n      <td>{EG10941}</td>\n      <td>{SER[c], SEPO3[c]}</td>\n      <td>{}</td>\n      <td>{L-SELENOCYSTEINE[c]}</td>\n      <td>{2.9.1.1-RXN, putative-L-selenocysteine-synthe...</td>\n      <td>[{Pi, SEPO3}]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Testing L-Selenocysteine CONTROL</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{}</td>\n      <td>{2.9.1.1-RXN, putative-L-selenocysteine-synthe...</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bed4cb3d-8c43-44a6-9677-2769b81ade20",
+   "metadata": {},
+   "source": [
+    "## Quick and dirty loop (after init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished Testing L-Selenocysteine\n",
+      "Finished Testing L-Selenocysteine CONTROL\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(columns=[\"Environment\", \"Reaction\", \"Flux\"])\n",
+    "zero_df = pd.DataFrame()\n",
+    "all_flux = []\n",
+    "\n",
+    "metabolites_idx = {species: i for i, species in enumerate(metabolism.metabolite_names)}\n",
+    "reaction_catalysts = metabolism.parameters['reaction_catalysts']\n",
+    "metabolism.catalyst_ids = metabolism.parameters['catalyst_ids']\n",
+    "catalyst_idx = {catalyst: i for i, catalyst in enumerate(metabolism.catalyst_ids)}\n",
+    "\n",
+    "reactions_add = {\"putative-L-selenocysteine-synthesis\": {'SEPO3[c]': -1, 'SER[c]': -1, 'Pi[c]': 1, 'L-SELENOCYSTEINE[c]': 1}\n",
+    "                 # Insert other added reactions here\n",
+    "                 }\n",
+    "\n",
+    "for col_idx, (reaction, stoich) in enumerate(reactions_add.items()):\n",
+    "        new_col = np.zeros([len(metabolism.stoichiometry),1])\n",
+    "        metabolism.stoichiometry = np.append(metabolism.stoichiometry, new_col, axis=1)\n",
+    "\n",
+    "        for species, coefficient in stoich.items():\n",
+    "            i = metabolites_idx[species]\n",
+    "            metabolism.stoichiometry[i, np.shape(metabolism.stoichiometry)[1] - 1] = coefficient\n",
+    "\n",
+    "        # now adds an entry for every reaction, list can be empty\n",
+    "        reaction_catalysts[\"putative-L-selenocysteine-synthesis\"] = ['CPLX0-1141[c]']\n",
+    "        # Taken from reconstruction/ecoli.../complexation reactions to find the complex that the gene forms\n",
+    "        enzyme_idx = [catalyst_idx[catalyst] for catalyst in reaction_catalysts.get(reaction, [])]\n",
+    "        metabolism.catalyzed_rxn_enzymes_idx.append(enzyme_idx)\n",
+    "\n",
+    "for i in range(len(condition_df)):\n",
+    "    condition_idx = i\n",
+    "\n",
+    "    uptake = metabolism.allowed_exchange_uptake\n",
+    "\n",
+    "    homeostatic = dict(pd.DataFrame(fba[\"target_homeostatic_dmdt\"], columns=metabolism.homeostatic_metabolites).loc[24, :])\n",
+    "    new_demand = condition_df.at[condition_idx, \"Demand\"]\n",
+    "    if new_demand != set():\n",
+    "        for met in new_demand:\n",
+    "            homeostatic[met] = 100\n",
+    "\n",
+    "    uptake = set(uptake)\n",
+    "\n",
+    "    addition = condition_df.at[condition_idx, \"Add\"]\n",
+    "    removals = condition_df.at[condition_idx, \"Remove\"]\n",
+    "\n",
+    "    uptake = uptake | addition\n",
+    "    uptake = uptake - removals\n",
+    "\n",
+    "    for gene in condition_df.at[i, \"Genes\"]:\n",
+    "        if gene in added_gene_to_reaction:\n",
+    "            metabolism.reaction_names.append(added_gene_to_reaction[gene])\n",
+    "\n",
+    "    model = NetworkFlowModel(\n",
+    "            stoich_arr=metabolism.stoichiometry,\n",
+    "            metabolites=metabolism.metabolite_names,\n",
+    "            reactions=metabolism.reaction_names,\n",
+    "            homeostatic_metabolites=list(dict(homeostatic).keys()),\n",
+    "            kinetic_reactions=list(dict(kinetic).keys()),\n",
+    "            free_reactions=FREE_RXNS)\n",
+    "    model.set_up_exchanges(exchanges=metabolism.exchange_molecules, uptakes=uptake)\n",
+    "\n",
+    "    objective_weights = {'secretion': 0.01, 'efficiency': 0.0001, 'kinetics': 0.000001}\n",
+    "    solution: FlowResult = model.solve(\n",
+    "                homeostatic_targets=np.array(list(dict(homeostatic).values())),\n",
+    "                maintenance_target=maintenance,\n",
+    "                kinetic_targets=np.array(list(dict(kinetic).values())),\n",
+    "                binary_kinetic_idx=[],\n",
+    "                objective_weights=objective_weights,\n",
+    "                upper_flux_bound=100000000)\n",
+    "\n",
+    "    # Check flux\n",
+    "    fluxes = pd.Series(solution.velocities, index=metabolism.reaction_names)\n",
+    "    # fluxes = pd.DataFrame({model.rxns[i]: [solution.velocities[model.rxns[i]]] for i in range(len(model.rxns))}).T\n",
+    "    fluxes.columns = [\"Flux\"]\n",
+    "    all_flux.append(fluxes)\n",
+    "\n",
+    "    if condition_df.at[condition_idx, \"Environment\"][-7:] == \"CONTROL\":\n",
+    "        control_df = pd.DataFrame()\n",
+    "        flux_threshold = 100\n",
+    "        for j, k in enumerate(fluxes.index):\n",
+    "            if abs(all_flux[condition_idx - length][k] - fluxes[k]) / abs(fluxes[k] + 0.01) >= flux_threshold:\n",
+    "                control_df = control_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
+    "                          fluxes.index[j],\n",
+    "                         (all_flux[condition_idx - length][k] - fluxes[k]) / (fluxes[k] + 0.01) ]], columns=[\"Environment\", \"Reaction\", \"(Experiment - Control)/(Control + 0.01) Flux\"]))\n",
+    "        if len(control_df) > 0:\n",
+    "            control_df = control_df.sort_values(by=[\"(Experiment - Control)/(Control + 0.01) Flux\"], ascending=False)\n",
+    "        control_df.to_csv(rf'notebooks/new_environments/figures/control_differences/{condition_df.at[condition_idx, \"Environment\"]}.csv')\n",
+    "    else:\n",
+    "        relevant_rxns = condition_df.at[condition_idx, \"Reactions\"]\n",
+    "        relevant_rxn_idx = []\n",
+    "\n",
+    "        for rxn in relevant_rxns:\n",
+    "            relevant_rxn_idx.extend(list(np.where(fluxes.index.str.contains(rxn))[0] ))\n",
+    "\n",
+    "        relevant_fluxes = fluxes.iloc[relevant_rxn_idx]\n",
+    "\n",
+    "        relevant_nonzero_fluxes = relevant_fluxes[relevant_fluxes != 0]\n",
+    "        zero_fluxes = relevant_fluxes[relevant_fluxes == 0]\n",
+    "\n",
+    "        for j, k in enumerate(relevant_nonzero_fluxes.index):\n",
+    "            results_df = results_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
+    "                          relevant_nonzero_fluxes.index[j],\n",
+    "                          relevant_nonzero_fluxes[k]]], columns=[\"Environment\", \"Reaction\", \"Flux\"]))\n",
+    "\n",
+    "        for j, k in enumerate(zero_fluxes.index):\n",
+    "            zero_df = zero_df.append(pd.DataFrame([[condition_df.at[condition_idx, \"Environment\"],\n",
+    "                          zero_fluxes.index[j],\n",
+    "                          zero_fluxes[k]]], columns=[\"Environment\", \"Reaction\", \"Flux\"]))\n",
+    "\n",
+    "    print(f\"\"\"Finished {condition_df.at[condition_idx, \"Environment\"]}\"\"\")\n",
+    "zero_df.to_csv(r'notebooks/new_environments/figures/zero_flux.csv')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:29:21.975330Z",
+     "start_time": "2023-07-10T22:28:53.740958Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "3feb60e7-d881-4b21-afc4-e501301874bc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-11T22:13:00.470763Z",
+     "start_time": "2023-07-11T22:13:00.445723Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "                Environment                             Reaction   Flux  \\\n0  Testing L-Selenocysteine  putative-L-selenocysteine-synthesis  100.0   \n\n   log(Flux)  \n0        2.0  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Environment</th>\n      <th>Reaction</th>\n      <th>Flux</th>\n      <th>log(Flux)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Testing L-Selenocysteine</td>\n      <td>putative-L-selenocysteine-synthesis</td>\n      <td>100.0</td>\n      <td>2.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# results_df[\"Reaction\"] = results_df['Reaction'].str.slice(0,20)\n",
+    "results_df[\"log(Flux)\"] = np.log10(results_df[\"Flux\"])\n",
+    "results_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [],
+   "source": [
+    "# for each environment, plot the fluxes of the reactions that are relevant to that environment.\n",
+    "# g = sns.catplot(y=\"Environment\",hue=\"Reaction\", x=\"log(Flux)\", data=results_df, kind=\"bar\", aspect=2, height=10, sharey=False)\n",
+    "# g.savefig(\"notebooks/new_environments/figures/rxns_light_up.pdf\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:29:22.162005Z",
+     "start_time": "2023-07-10T22:29:21.927816Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [],
+   "source": [
+    "import textwrap\n",
+    "\n",
+    "def wrap_x_labels(ax, width, break_long_words=False):\n",
+    "    labels = []\n",
+    "    for label in ax.get_xticklabels():\n",
+    "        text = label.get_text()\n",
+    "        labels.append(textwrap.fill(text, width=width,\n",
+    "                      break_long_words=break_long_words))\n",
+    "    ax.set_xticklabels(labels, rotation=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:29:22.162977Z",
+     "start_time": "2023-07-10T22:29:21.948237Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 300x600 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", rc={'figure.dpi': 300}, font_scale=0.5)\n",
+    "fig, ax = plt.subplots(figsize=(len(results_df[\"Environment\"].unique()), 2))\n",
+    "ax.set_title(\"Non-zero Fluxes\")\n",
+    "ax = sns.barplot(data=results_df, x=\"Environment\", y=\"log(Flux)\", ax=ax, hue=\"Reaction\")\n",
+    "ax.legend(bbox_to_anchor=(1, 1), title='Reactions', loc='upper left', ncol = 1)\n",
+    "wrap_x_labels(ax, 10)\n",
+    "fig = ax.get_figure()\n",
+    "plt.show(fig)\n",
+    "fig.savefig(\"notebooks/new_environments/figures/vert_bar_rxns.png\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-10T22:29:24.621883Z",
+     "start_time": "2023-07-10T22:29:21.980416Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/new_environments/testing_pathways.ipynb b/notebooks/new_environments/testing_pathways.ipynb
new file mode 100644
index 000000000..a5205e366
--- /dev/null
+++ b/notebooks/new_environments/testing_pathways.ipynb
@@ -0,0 +1,115 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.chdir(os.path.expanduser('~/Documents/vivarium-ecoli'))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dill\n",
+    "import requests\n",
+    "import xmltodict\n",
+    "import cvxpy as cp\n",
+    "from ecoli.processes.metabolism_redux import NetworkFlowModel, FlowResult, MetabolismRedux\n",
+    "\n",
+    "FREE_RXNS = [\"TRANS-RXN-145\", \"TRANS-RXN0-545\", \"TRANS-RXN0-474\"]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-14T18:33:13.001144Z",
+     "start_time": "2023-07-14T18:33:12.991515Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2023-07-14T18:33:13.047304Z",
+     "start_time": "2023-07-14T18:33:13.035902Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 1. 1. 1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "    s_matrix = np.array([[-1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
+    "                         [0, 0, 0, -1, 1, 0, 0, 0, 0],\n",
+    "                         [0, 0, -1, 0, -1, 1, 0, 0, 0],\n",
+    "                         [0, 0, 0, 0, 0, -1, -1, 1, 1]]).T\n",
+    "\n",
+    "    metabolites = [\"CYS[c]\", \"L-ALPHA-ALANINE[c]\", \"S-SULFANYL-IscS[c]\", \"FE+2[c]\", \"DISORDERED-FdS-IscU[c]\", \"S-SULFANYL-DISORDERED-FdS-IscU[c]\", \"ATP[c]\", \"ADP[c]\", \"CPD-6[c]\"]\n",
+    "    reactions = [\"cysteine-desulfurase-FeS-cluster-assembly\", \"iron-transport-FeS-cluster-assembly\", \"complex-formation-FeS-cluster-assembly\", \"cluster-transport-FeS-cluster-assembly\"]\n",
+    "    homeostatic_metabolites = {\"CPD-6[c]\": 1} # Demand has to be added for everything in the pathway\n",
+    "\n",
+    "    exchanges = {\"CYS[c]\", \"FE+2[c]\", \"ATP[c]\", \"ADP[c]\", \"L-ALPHA-ALANINE[c]\"}\n",
+    "    uptakes = {\"CYS[c]\", \"FE+2[c]\", \"ATP[c]\"}\n",
+    "\n",
+    "    model = NetworkFlowModel(stoich_arr=s_matrix, reactions=reactions,\n",
+    "                             metabolites=metabolites, homeostatic_metabolites=list(homeostatic_metabolites.keys()),\n",
+    "                             kinetic_reactions=None)\n",
+    "\n",
+    "    model.set_up_exchanges(exchanges=exchanges, uptakes=uptakes)\n",
+    "\n",
+    "    solution: FlowResult = model.solve(homeostatic_targets=list(homeostatic_metabolites.values()),\n",
+    "                                       objective_weights={'secretion': 0.01, 'efficiency': 0.0001},\n",
+    "                                       # 0.01, 0.0001\n",
+    "                                       upper_flux_bound=100, solver=cp.GLOP)\n",
+    "\n",
+    "    print(solution.velocities)\n",
+    "    assert np.isclose(solution.velocities, np.array([1, 1, 1, 1])).all() == True, \"Network flow toy model did not converge to correct solution.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-07-14T18:33:13.052859Z",
+     "start_time": "2023-07-14T18:33:13.048695Z"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}