From 78bbf3ac5f2b6dea7ef7755a5a4b1a9e932c8c40 Mon Sep 17 00:00:00 2001 From: Dmitri Svetlov <123213937+dmitrisvetlov@users.noreply.github.com> Date: Tue, 7 Nov 2023 15:04:54 -0700 Subject: [PATCH 01/14] Add nanoparticle-assembly example --- WorkSpace/Dmitri/Nov2SSITExample.m | 92 ++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 WorkSpace/Dmitri/Nov2SSITExample.m diff --git a/WorkSpace/Dmitri/Nov2SSITExample.m b/WorkSpace/Dmitri/Nov2SSITExample.m new file mode 100644 index 0000000..19d8778 --- /dev/null +++ b/WorkSpace/Dmitri/Nov2SSITExample.m @@ -0,0 +1,92 @@ +clear all +% addpath(genpath('../src')); +addpath('../CommandLine') + +Model = SSIT; +Model.species = {'E1'}; +Model.initialCondition = [1]; +Model.propensityFunctions = {}; +Model.propensityFunctions = {'kb'} +Model.stoichiometry = [1]; +Model.parameters = ({'kb',0.0}); +Model.tSpan = linspace(0,2,21); + +% Add species +for i=2:5 + Model = Model.addSpecies(['E',num2str(i)],0); +end + +Model = Model.addSpecies('G',0); + +% Add reactions +for i = 1:4 + Model = Model.addParameter({['alpha',num2str(i)],0.1;... + ['beta',num2str(i+1)],0.2;... + ['k',num2str(i)],10}); + + stoichForward = zeros(size(Model.species,1),1); + stoichForward([i,i+1])=[-1,1]; + Model = Model.addReaction(['alpha',num2str(i),'*E',num2str(i)],stoichForward); + + stoichBackward = zeros(size(Model.species,1),1); + stoichBackward([i,i+1])=[1,-1]; + Model = Model.addReaction(['beta',num2str(i+1),'*E',num2str(i+1)],stoichBackward); + + stoichProduction = zeros(size(Model.species,1),1); + stoichProduction(end)=1; + Model = Model.addReaction(['k',num2str(i),'*E',num2str(i)],stoichProduction); + +end +Model = Model.addParameter({'k5',0.1;... + 'deg',0.2}); + +stoichProduction = zeros(size(Model.species,1),1); +stoichProduction(end)=1; +Model = Model.addReaction('k5*E5',stoichProduction); + +stoichDegradtion = zeros(size(Model.species,1),1); +stoichDegradtion(end)=-1; +Model = Model.addReaction('deg*G',stoichDegradtion); + +Model.summarizeModel +%% Generate Equations for Propensities +Model = Model.formPropensitiesGeneral('EnzymeModel'); + +%% Solve the FSP +Model.fspOptions.verbose = true; +[fspSoln,Model.fspOptions.bounds] = Model.solve; + +%% Generate Fake Data +Model.ssaOptions.nSimsPerExpt = 10; +Model.ssaOptions.Nexp = 1; +Model.sampleDataFromFSP(fspSoln,'fakeData.csv') + +%% +Model = Model.loadData('fakeData.csv',{'G','exp1_s6'}); +Model.makeFitPlot + +%% +fitOptions = optimset('Display','iter','MaxIter',1000); +fitOptions.SIG = []; +for i=1:3 + fitPars = Model.maximizeLikelihood([],fitOptions); + Model.parameters(:,2) = num2cell(fitPars); +end + +%% +% run Metropolis Hastings +MHFitOptions.thin=1; +MHFitOptions.numberOfSamples=1000; +MHFitOptions.burnIn=0; +MHFitOptions.progress=true; +MHFitOptions.useFIMforMetHast =true; +MHFitOptions.CovFIMscale = 1.0; +MHFitOptions.numChains = 1; +MHFitOptions.saveFile = 'TMPMHChain.mat'; +Model.fittingOptions.modelVarsToFit = [1,2]; +[newPars,~,MHResults] = Model.maximizeLikelihood(... + [], MHFitOptions, 'MetropolisHastings'); +Model.parameters(Model.fittingOptions.modelVarsToFit,2) = num2cell(newPars); +delete('TMPMHChain.mat') + + From fd4d38fc1d91521c2b9ce8b7986d9acf20553351 Mon Sep 17 00:00:00 2001 From: Munsky Date: Tue, 7 Nov 2023 15:10:46 -0700 Subject: [PATCH 02/14] Update SSIT. --- WorkSpace/Dmitri/Nov2SSITExample.m | 8 +++++--- src/CommandLine/SSIT.m | 3 ++- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/WorkSpace/Dmitri/Nov2SSITExample.m b/WorkSpace/Dmitri/Nov2SSITExample.m index 19d8778..be86dcf 100644 --- a/WorkSpace/Dmitri/Nov2SSITExample.m +++ b/WorkSpace/Dmitri/Nov2SSITExample.m @@ -1,6 +1,5 @@ clear all -% addpath(genpath('../src')); -addpath('../CommandLine') +addpath(genpath('../../src')); Model = SSIT; Model.species = {'E1'}; @@ -89,4 +88,7 @@ Model.parameters(Model.fittingOptions.modelVarsToFit,2) = num2cell(newPars); delete('TMPMHChain.mat') - +%% FIM Calculation +Model.unobservedSpecies = {'x1',; + + diff --git a/src/CommandLine/SSIT.m b/src/CommandLine/SSIT.m index cbbde33..36c19e2 100644 --- a/src/CommandLine/SSIT.m +++ b/src/CommandLine/SSIT.m @@ -913,7 +913,8 @@ function sampleDataFromFSP(obj,fspSoln,saveFile) PP = double(fspSoln.fsp{it}.p.data); clear w w(:) = PP(:); w(w<0)=0; - [I1,I2,I3,I4,I5] = ind2sub(size(PP),randsample(length(w), nSims, true, w )); + % TODO - there has to be another way of doing this. + [I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,I11] = ind2sub(size(PP),randsample(length(w), nSims, true, w )); for iSp = 1:length(obj.species) eval(['Solution.trajs(iSp,it,:) = I',num2str(iSp),'-1;']); end From 8d1c753561495d4bec1fe4d16cc142c7fba837de Mon Sep 17 00:00:00 2001 From: Munsky Date: Fri, 10 Nov 2023 11:59:48 -0700 Subject: [PATCH 03/14] Fix issue for plotting MH results and FIM when only some parameters are free. --- src/CommandLine/SSIT.m | 1 + 1 file changed, 1 insertion(+) diff --git a/src/CommandLine/SSIT.m b/src/CommandLine/SSIT.m index 36c19e2..437992e 100644 --- a/src/CommandLine/SSIT.m +++ b/src/CommandLine/SSIT.m @@ -2294,6 +2294,7 @@ function plotMHResults(obj,mhResults,FIM,fimScale,mhPlotScale,scatterFig) else FIMi = FIM{i}; end + FIMi = FIMi(obj.fittingOptions.modelVarsToFit,obj.fittingOptions.modelVarsToFit); if strcmp(mhPlotScale,'log10') covFIM{i} = FIMi^(-1)/log(10)^2; else From e328dcb9cddb1ab1f8010eb59f0e40cb38ee23eb Mon Sep 17 00:00:00 2001 From: Munsky Date: Fri, 10 Nov 2023 12:00:35 -0700 Subject: [PATCH 04/14] Update a few models in the workspace folder. --- WorkSpace/Dmitri/Nov2SSITExample.m | 97 ++++++-- .../josh_IterativeBayesianExperimentDesign.m | 232 ++++++++++++++++++ tests/multiModelTests.m | 2 +- 3 files changed, 308 insertions(+), 23 deletions(-) create mode 100644 WorkSpace/Josh/josh_IterativeBayesianExperimentDesign.m diff --git a/WorkSpace/Dmitri/Nov2SSITExample.m b/WorkSpace/Dmitri/Nov2SSITExample.m index be86dcf..36d5c8a 100644 --- a/WorkSpace/Dmitri/Nov2SSITExample.m +++ b/WorkSpace/Dmitri/Nov2SSITExample.m @@ -2,34 +2,44 @@ addpath(genpath('../../src')); Model = SSIT; -Model.species = {'E1'}; -Model.initialCondition = [1]; +Model.species = {}; +Model.initialCondition = []; Model.propensityFunctions = {}; -Model.propensityFunctions = {'kb'} -Model.stoichiometry = [1]; -Model.parameters = ({'kb',0.0}); +Model.propensityFunctions = {}; +Model.stoichiometry = []; +Model.parameters = {}; Model.tSpan = linspace(0,2,21); % Add species -for i=2:5 - Model = Model.addSpecies(['E',num2str(i)],0); +for i=1:5 + if i==1 + Model = Model.addSpecies(['E',num2str(i)],1); + else + Model = Model.addSpecies(['E',num2str(i)],0); + end end Model = Model.addSpecies('G',0); -% Add reactions +Model.stoichiometry = []; + +Model = Model.addParameter({'alph',0.1;... + 'bet',0.2}); + % Add reactions for i = 1:4 - Model = Model.addParameter({['alpha',num2str(i)],0.1;... - ['beta',num2str(i+1)],0.2;... - ['k',num2str(i)],10}); + % Model = Model.addParameter({['alpha',num2str(i)],0.1;... + % ['beta',num2str(i+1)],0.2;... + % ['k',num2str(i)],10}); + Model = Model.addParameter({['k',num2str(i)],10}); stoichForward = zeros(size(Model.species,1),1); stoichForward([i,i+1])=[-1,1]; - Model = Model.addReaction(['alpha',num2str(i),'*E',num2str(i)],stoichForward); + % Model = Model.addReaction(['alpha',num2str(i),'*E',num2str(i)],stoichForward); + Model = Model.addReaction(['alph*E',num2str(i)],stoichForward); stoichBackward = zeros(size(Model.species,1),1); stoichBackward([i,i+1])=[1,-1]; - Model = Model.addReaction(['beta',num2str(i+1),'*E',num2str(i+1)],stoichBackward); + Model = Model.addReaction(['bet*E',num2str(i+1),'*',num2str(i)],stoichBackward); stoichProduction = zeros(size(Model.species,1),1); stoichProduction(end)=1; @@ -58,6 +68,7 @@ %% Generate Fake Data Model.ssaOptions.nSimsPerExpt = 10; Model.ssaOptions.Nexp = 1; +Model.tSpan = linspace(0,2,9); Model.sampleDataFromFSP(fspSoln,'fakeData.csv') %% @@ -65,12 +76,12 @@ Model.makeFitPlot %% -fitOptions = optimset('Display','iter','MaxIter',1000); -fitOptions.SIG = []; -for i=1:3 - fitPars = Model.maximizeLikelihood([],fitOptions); - Model.parameters(:,2) = num2cell(fitPars); -end +% fitOptions = optimset('Display','iter','MaxIter',1000); +% fitOptions.SIG = []; +% for i=1:3 +% fitPars = Model.maximizeLikelihood([],fitOptions); +% Model.parameters(:,2) = num2cell(fitPars); +% end %% % run Metropolis Hastings @@ -79,16 +90,58 @@ MHFitOptions.burnIn=0; MHFitOptions.progress=true; MHFitOptions.useFIMforMetHast =true; -MHFitOptions.CovFIMscale = 1.0; +MHFitOptions.suppressFSPExpansion = true; +MHFitOptions.CovFIMscale = 1; MHFitOptions.numChains = 1; MHFitOptions.saveFile = 'TMPMHChain.mat'; -Model.fittingOptions.modelVarsToFit = [1,2]; +Model.fittingOptions.modelVarsToFit = [1:8]; +delete('TMPMHChain.mat') [newPars,~,MHResults] = Model.maximizeLikelihood(... [], MHFitOptions, 'MetropolisHastings'); Model.parameters(Model.fittingOptions.modelVarsToFit,2) = num2cell(newPars); delete('TMPMHChain.mat') +%% +Model.plotMHResults(MHResults) + %% FIM Calculation -Model.unobservedSpecies = {'x1',; +%% Solve FSP Sensitivity +Model.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity +[sensSoln,bounds] = Model.solve(fspSoln.stateSpace); % Solve the sensitivity problem + +% Set model to ignore unobservable species. +Model.pdoOptions.unobservedSpecies = {'E1','E2','E3','E4','E5'}; +% Model.pdoOptions.unobservedSpecies = {'E1','E2','E3','E4'}; + +% Compute the FIM for each time point +fimResults = Model.computeFIM(sensSoln.sens); + +% Choose experiment design (number of measurements per time for each time). +cellCounts = 100*ones(size(Model.tSpan)); + +% Compute the total FIM: +[fimTotal,mleCovEstimate,fimMetrics] = ... + Model.evaluateExperiment(fimResults,cellCounts); + +% Compare to the MH results +Model.plotMHResults(MHResults,fimTotal) + +% Optimize to find the best experiment for same number of spot/cells +allowableCellNumber = 2100; +OptimumExperiment = Model.optimizeCellCounts(fimResults,allowableCellNumber,... + 'Determinant',[],[],[]); + +% Compute the total FIM: +fimTotalOptimized = Model.evaluateExperiment(fimResults,OptimumExperiment); + +% Compare to the MH results +Model.plotMHResults(MHResults,[fimTotal,fimTotalOptimized]) + +%% trouble shooting unidentifiable models +[Vecs,Vals] = eig(full(fimTotal{1})); +JUncertain = find(diag(Vals)<1e-9) +figure +bar(Vecs(:,JUncertain)) + diff --git a/WorkSpace/Josh/josh_IterativeBayesianExperimentDesign.m b/WorkSpace/Josh/josh_IterativeBayesianExperimentDesign.m new file mode 100644 index 0000000..10cc75d --- /dev/null +++ b/WorkSpace/Josh/josh_IterativeBayesianExperimentDesign.m @@ -0,0 +1,232 @@ +close all +clear all +addpath(genpath('../../src')); + +rng(1) +%% Define Model +% example = 'Poisson'; +% example = 'Toggle'; +example = 'DUSP1'; +switch example + case 'Poisson' + ModelTrue = SSIT; + ModelTrue.species = {'rna'}; + ModelTrue.initialCondition = 0; + ModelTrue.propensityFunctions = {'kr';'gr*rna'}; + ModelTrue.stoichiometry = [1,-1]; + ModelTrue.fspOptions.fspTol = 1e-5; + ModelTrue.parameters = ({'kr',10;'gr',0.3}); + ModelTrue = ModelTrue.formPropensitiesGeneral('Poiss',true); + dataToFit = {'rna','exp1_s1'}; + fitParameters = [1:2]; + nT = 21; + ModelTrue.tSpan = linspace(0,10,nT); + case 'Toggle' + ModelTrue = SSIT; + ModelTrue.parameters = {'kb',10;'ka',80;'M',20;'g',1}; + ModelTrue.species = {'lacI';'lambdaCI'}; + ModelTrue.stoichiometry = [1,-1,0, 0;... + 0, 0,1,-1]; + ModelTrue.propensityFunctions = {'kb+ka*M^3/(M^3+lambdaCI^3)';... + 'g*lacI';... + 'kb+ka*M^3/(M^3+lacI^3)';... + 'g*lambdaCI'}; + ModelTrue.initialCondition = [0;0]; + ModelTrue.customConstraintFuns = {'(x1-3).^2.*(x2-3).^2'}; + dataToFit = {'lacI','exp1_s1';'lambdaCI','exp1_s2'}; + fitParameters = [1:4]; + nT = 21; + ModelTrue.tSpan = linspace(0,10,nT); + + case 'DUSP1' + ModelTrue = SSIT; % Rename SSIT code to make changes with out changing the original code + ModelTrue.species = {'x1';'x2'}; % x1: number of on alleles, x2: mRNA + ModelTrue.initialCondition = [0;0]; % Set initial conditions + ModelTrue.propensityFunctions = {'kon*IGR*(2-x1)';'koff*x1';'kr*x1';'gr*x2'}; % Set the propensity functions of the 4 reactions + % Associated stoichiometry of the four reactions + stoich = [1,0; % Gene allele switching to on state + -1,0; % Gene allele switching to off state + 0,1; % mRNA production + 0,-1]; % mRNA degredation + ModelTrue.stoichiometry = transpose(stoich); + % Input expression for time varying signals + ModelTrue.inputExpressions = {'IGR','kcn0/knc+(t>=0)*kcn1/(r1-knc)*(exp(-knc*t)-exp(-r1*t))'}; + % Defining the values of each parameter used + ModelTrue.parameters = ({'koff',0.14;'kon',1;'kr',1;'gr',0.01;... + 'kcn0',0.01;'kcn1',0.1;'knc',1;'r1',0.01}); + ModelTrue.fspOptions.initApproxSS = true; + + dataToFit = {'x2','exp1_s2'}; + fitParameters = [1:4]; + timeDUSP1 = [0 10 20 30 40 50 60 75 90 120 150 180]; + nT = length(timeDUSP1); + ModelTrue.tSpan = timeDUSP1; + ModelTrue.fspOptions.bounds(3:4) = [2.1,100]; +end +ModelTrue.fspOptions.fspTol = 1e-6; +saveFileName = ['IterativeExperimentResults_',example]; + +%% Generate Model Propensity Functions and Solve True Model +ModelTrue = ModelTrue.formPropensitiesGeneral('TrueModel',true); +ModelTrue.fspOptions.verbose = true; +[ModelSolution,ModelTrue.fspOptions.bounds] = ModelTrue.solve; + +%% Verify that the true model and simulated data look correct. +dataFile = ['FakeExperiment_TMP.csv']; +nextExperiment = 100*ones(1,nT) +ModelTrue.ssaOptions.nSimsPerExpt = max(nextExperiment); +ModelTrue.ssaOptions.Nexp = 1; +ModelTrue.sampleDataFromFSP(ModelSolution,dataFile) +ModelTMP = ModelTrue.loadData(dataFile,dataToFit); +ModelTMP.makeFitPlot + +%% Set Rules for Experiment Designs and Iterations +% Possible Time points for Experiments. + +% Number of experiment Rounds +nExptRounds = 5; + +% MLE Fitting Options +maxFitIter = 1000; +nFitRounds = 3; + +% Metropolis Hastings Properties +nSamplesMH = 1000; % Number of MH Samples to run +nThinMH = 2; % Thin rate for MH sampling +nBurnMH = 100; % Number for MH burn in + +% Number of MH samples to use for FIM calculation +nFIMsamples = 10; + +% Total number of new cells allowed in each experiment +numCellsPerExperiment = 300; + +% Definition of initial experiment +nextExperiment = zeros(1,nT); +nextExperiment([1,round(nT/2),nT]) = round(numCellsPerExperiment/3); + +% FIM options +fimScale = 'log'; % Maximize fim for log parameters + +% Plotting options +mhPlotScale = 'log10'; % Show MH and FIM plots in log10 scale. + +%% +% Create Model for Estimate. +ModelGuess = ModelTrue; +ModelGuess.fspOptions.fspTol = inf; + +% Randomize the initial parameter set. +np = size(ModelGuess.parameters,1); +ModelGuess.parameters(:,2) = ... + num2cell([ModelGuess.parameters{:,2}]'.*(1+0.5*randn(np,1))); + +% Initialize cells for saved results +covMH = cell(1,nExptRounds); +covLogMH = cell(1,nExptRounds); +covLogFIM_Prediction = cell(1,nExptRounds); +covFIM_Prediction = cell(1,nExptRounds); +parametersFound = cell(1,nExptRounds); +exptDesigns = cell(1,nExptRounds); +FIMOptNextExptSaved = cell(1,nExptRounds); +MHResultsSaved = cell(1,nExptRounds); + +% Loop over subsequent experiments +allDataSoFar = []; +nTotalCells = zeros(1,nT); +for iExpt = 1:nExptRounds + % Generate "Fake" data + dataFile = ['FakeExperiment_',num2str(iExpt),'.csv']; + ModelTrue.ssaOptions.nSimsPerExpt = max(nextExperiment); + ModelTrue.ssaOptions.Nexp = 1; + ModelTrue.sampleDataFromFSP(ModelSolution,dataFile) + + % DownSelect fake data to specified number of cells at each time + X = importdata(dataFile); + for iT = 1:length(ModelTrue.tSpan) + if nextExperiment(iT)>0 + J = find(X.data(:,1)==ModelTrue.tSpan(iT),nextExperiment(iT)); + + % Add new data to previous data set + allDataSoFar = [allDataSoFar;X.data(J,:)]; + end + end + + % Save generated data to file. + A = table; + for j=1:length(X.colheaders) + A.(X.colheaders{j}) = allDataSoFar(:,j); + writetable(A,dataFile) + end + + nTotalCells = nTotalCells + nextExperiment + + % Add Data to Model + ModelGuess = ModelGuess.loadData(dataFile,dataToFit); + + % Fit Model to data. + fitOptions = optimset('Display','iter','MaxIter',maxFitIter); + for iFitIter=1:nFitRounds + fitPars = ModelGuess.maximizeLikelihood([],fitOptions); + if iExpt == 1 + ModelGuess.parameters(:,2) = num2cell(fitPars); + else + ModelGuess.parameters(fitParameters,2) = num2cell(fitPars); + end + end + + % Run Metropolis Hastings + MHFitOptions.thin=nThinMH; + MHFitOptions.numberOfSamples=nSamplesMH; + MHFitOptions.burnIn=nBurnMH; + MHFitOptions.progress=true; + MHFitOptions.useFIMforMetHast=true; + MHFitOptions.CovFIMscale = 0.6; + MHFitOptions.numChains = 1; + MHFitOptions.saveFile = 'TMPMHChain.mat'; + delete 'TMPMHChain.mat' + ModelGuess.fittingOptions.modelVarsToFit = fitParameters; + [newPars,~,MHResults] = ModelGuess.maximizeLikelihood(... + [], MHFitOptions, 'MetropolisHastings'); + ModelGuess.parameters(ModelGuess.fittingOptions.modelVarsToFit,2) = num2cell(newPars); + delete('TMPMHChain.mat') + + % Compute FIM for subsampling of MH results. + J = floor(linspace(nSamplesMH/2,nSamplesMH,nFIMsamples)); + MHSamplesForFIM = exp(MHResults.mhSamples(J,:)); + ModelGuess.tSpan = ModelTrue.tSpan; + fimResults = ModelGuess.computeFIM([],fimScale,MHSamplesForFIM); + + % Find optimal NEXT experiment design given parameter sets + nextExperiment = ModelGuess.optimizeCellCounts(fimResults,numCellsPerExperiment,'Determinant',[],nTotalCells); + FIMOptNextExpt = ModelGuess.totalFim(fimResults,nextExperiment+nTotalCells); + + % Compute and Save Covariance from MH from current stage. + parametersFound{iExpt} = newPars; + FIMOptNextExptSaved{iExpt} = FIMOptNextExpt; + covLogMH{iExpt} = cov(MHResults.mhSamples); + covMH{iExpt} = cov(exp(MHResults.mhSamples)); + MHResultsSaved{iExpt} = MHResults; + + % Save Predicted FIM for next stage. + for jFIM=1:nFIMsamples + covFIM_Prediction{iExpt}{jFIM} = inv(FIMOptNextExpt{jFIM}); + end + + exptDesigns{iExpt} = nextExperiment; + + f = figure; + f.Name = ['Current MH Results and Next FIM Prediction (Round ',num2str(iExpt),')']; + ModelGuess.plotMHResults(MHResults,FIMOptNextExpt,fimScale,mhPlotScale,f) + if iExpt>1 + f = figure; + f.Name = ['Current MH Results and Previous FIM Prediction (Round ',num2str(iExpt),')']; + ModelGuess.plotMHResults(MHResults,[FIMOptNextExptSaved{iExpt-1}],fimScale,mhPlotScale,f) + end + + save(saveFileName,'parametersFound','FIMOptNextExptSaved','covMH',... + 'covLogMH','exptDesigns','') + +end + + diff --git a/tests/multiModelTests.m b/tests/multiModelTests.m index 8488856..7811d74 100644 --- a/tests/multiModelTests.m +++ b/tests/multiModelTests.m @@ -12,7 +12,7 @@ methods (TestClassSetup) % Shared setup for the entire test class function createTestModel1(tc) - addpath('../CommandLine') + addpath(genpath('../src')); %% Test Case 1 - a simple Poisson model tc.Poiss = SSIT; tc.Poiss.species = {'rna'}; From 26201278aefeeb6661f27068e6ab4f4d6e160d17 Mon Sep 17 00:00:00 2001 From: Dmitri Svetlov <123213937+dmitrisvetlov@users.noreply.github.com> Date: Mon, 13 Nov 2023 12:48:24 -0700 Subject: [PATCH 05/14] Correct some typos --- WorkSpace/Dmitri/Nov2SSITExample.m | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/WorkSpace/Dmitri/Nov2SSITExample.m b/WorkSpace/Dmitri/Nov2SSITExample.m index 36d5c8a..e2d1314 100644 --- a/WorkSpace/Dmitri/Nov2SSITExample.m +++ b/WorkSpace/Dmitri/Nov2SSITExample.m @@ -53,9 +53,9 @@ stoichProduction(end)=1; Model = Model.addReaction('k5*E5',stoichProduction); -stoichDegradtion = zeros(size(Model.species,1),1); -stoichDegradtion(end)=-1; -Model = Model.addReaction('deg*G',stoichDegradtion); +stoichDegradation = zeros(size(Model.species,1),1); +stoichDegradation(end)=-1; +Model = Model.addReaction('deg*G',stoichDegradation); Model.summarizeModel %% Generate Equations for Propensities @@ -65,14 +65,14 @@ Model.fspOptions.verbose = true; [fspSoln,Model.fspOptions.bounds] = Model.solve; -%% Generate Fake Data +%% Generate In Silico Data Model.ssaOptions.nSimsPerExpt = 10; Model.ssaOptions.Nexp = 1; Model.tSpan = linspace(0,2,9); -Model.sampleDataFromFSP(fspSoln,'fakeData.csv') +Model.sampleDataFromFSP(fspSoln,'InSilicoData.csv') %% -Model = Model.loadData('fakeData.csv',{'G','exp1_s6'}); +Model = Model.loadData('InSilicoData.csv',{'G','exp1_s6'}); Model.makeFitPlot %% From b752e162a2cd9dee1fae1b9f8ae511307ad71443 Mon Sep 17 00:00:00 2001 From: Dmitri Svetlov <123213937+dmitrisvetlov@users.noreply.github.com> Date: Mon, 13 Nov 2023 12:49:04 -0700 Subject: [PATCH 06/14] Add scripts for Grad Show examples --- WorkSpace/Dmitri/FIV_SSITExample_GradShow.m | 137 ++++++++++++++++++ WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m | 140 +++++++++++++++++++ 2 files changed, 277 insertions(+) create mode 100644 WorkSpace/Dmitri/FIV_SSITExample_GradShow.m create mode 100644 WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m diff --git a/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m b/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m new file mode 100644 index 0000000..84b35b9 --- /dev/null +++ b/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m @@ -0,0 +1,137 @@ +clear all +close all +addpath(genpath('../../src')); + +Model = SSIT; +Model.species = {}; +Model.initialCondition = []; +Model.propensityFunctions = {}; +Model.stoichiometry = []; +Model.parameters = {}; +Model.tSpan = linspace(0,2,21); + +% Add species + +Model = Model.addSpecies('S',100); +Model = Model.addSpecies('Iregr',0); +Model = Model.addSpecies('Iprog',0); +Model = Model.addSpecies('R',0); + +Model.stoichiometry = []; + +% Add parameters + +Model = Model.addParameter({'progTransmit',0.25}); % given effective contact +Model = Model.addParameter({'regrTransmitMultiplier',0.1}); % given effective contact +Model = Model.addParameter({'contact',0.25}); +Model = Model.addParameter({'progDeath',1/18}); +Model = Model.addParameter({'regrRecovery',1/18}); +Model = Model.addParameter({'respawn',1/6}); + +P = 0.25; % Proportion randomly assigned to progressive and regressive states + +% Add reactions + +% [S][Iregr][Iprog][R] + +% Regressive infection +stoichRegrInfect = [0,-3,1,2]; +Model = Model.addReaction('contact*progTransmit*regrTransmitMultiplier*Iregr',stoichRegrInfect); + +% Progressive infection +stoichProgInfect = [0,1,-3,2]; +Model = Model.addReaction('contact*progTransmit*Iprog',stoichProgInfect); + +% Recovery from regressive infection +stoichRegrRecovery = [0,-1,0,1]; +Model = Model.addReaction('regrRecovery*Iregr',stoichRegrRecovery); + +% Death from progressive infection +stoichProgDeath = [0,0,-1,0]; +Model = Model.addReaction('progDeath*Iprog',stoichProgDeath); + +% Respawn +stoichRespawn = [1,0,0,0]; +Model = Model.addReaction('respawn',stoichRespawn); + +Model.summarizeModel +%% Generate Equations for Propensities +%Model = Model.formPropensitiesGeneral('EnzymeModel'); + +%% Solve the FSP +Model.fspOptions.verbose = true; +[fspSoln,Model.fspOptions.bounds] = Model.solve; + +%% Generate In Silico Data +Model.ssaOptions.nSimsPerExpt = 10; +Model.ssaOptions.Nexp = 1; +Model.tSpan = linspace(0,2,9); +Model.sampleDataFromFSP(fspSoln,'InSilicoData_FIV.csv') + +%% +Model = Model.loadData('InSilicoData_FIV.csv',{'S','exp1_s4'}); +Model.makeFitPlot + +%% +% fitOptions = optimset('Display','iter','MaxIter',1000); +% fitOptions.SIG = []; +% for i=1:3 +% fitPars = Model.maximizeLikelihood([],fitOptions); +% Model.parameters(:,2) = num2cell(fitPars); +% end + +%% +% run Metropolis Hastings +MHFitOptions.thin=1; +MHFitOptions.numberOfSamples=1000; +MHFitOptions.burnIn=0; +MHFitOptions.progress=true; +MHFitOptions.useFIMforMetHast =true; +MHFitOptions.suppressFSPExpansion = true; +MHFitOptions.CovFIMscale = 1; +MHFitOptions.numChains = 1; +MHFitOptions.saveFile = 'TMPMHChain.mat'; +Model.fittingOptions.modelVarsToFit = [1:5]; +delete('TMPMHChain.mat') +[newPars,~,MHResults] = Model.maximizeLikelihood(... + [], MHFitOptions, 'MetropolisHastings'); +Model.parameters(Model.fittingOptions.modelVarsToFit,2) = num2cell(newPars); +delete('TMPMHChain.mat') + +%% +Model.plotMHResults(MHResults) + +%% FIM Calculation +%% Solve FSP Sensitivity +Model.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity +[sensSoln,bounds] = Model.solve(fspSoln.stateSpace); % Solve the sensitivity problem + +% Compute the FIM for each time point +fimResults = Model.computeFIM(sensSoln.sens); + +% Choose experiment design (number of measurements per time for each time). +cellCounts = 10*ones(size(Model.tSpan)); + +% Compute the total FIM: +[fimTotal,mleCovEstimate,fimMetrics] = ... + Model.evaluateExperiment(fimResults,cellCounts); + +% Compare to the MH results +Model.plotMHResults(MHResults,fimTotal) + +% Optimize to find the best experiment for same number of spot/cells +allowableCellNumber = 500; +OptimumExperiment = Model.optimizeCellCounts(fimResults,allowableCellNumber,... + 'Determinant',[],[],[]); + +% Compute the total FIM: +fimTotalOptimized = Model.evaluateExperiment(fimResults,OptimumExperiment); + +% Compare to the MH results +Model.plotMHResults(MHResults,[fimTotal,fimTotalOptimized]) + +%% trouble shooting unidentifiable models +[Vecs,Vals] = eig(full(fimTotal{1})); +JUncertain = find(diag(Vals)<1e-9) +figure +bar(Vecs(:,JUncertain)) \ No newline at end of file diff --git a/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m b/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m new file mode 100644 index 0000000..505f728 --- /dev/null +++ b/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m @@ -0,0 +1,140 @@ +clear all +close all +addpath(genpath('../../src')); + +Model = SSIT; +Model.species = {}; +Model.initialCondition = []; +Model.propensityFunctions = {}; +Model.propensityFunctions = {}; +Model.stoichiometry = []; +Model.parameters = {}; +Model.tSpan = linspace(0,2,21); + +% Add species +for i=1:3 + if i==1 + Model = Model.addSpecies(['E',num2str(i)],1); + else + Model = Model.addSpecies(['E',num2str(i)],0); + end +end + +Model = Model.addSpecies('NP',0); + +Model.stoichiometry = []; + +Model = Model.addParameter({'vAlpha',0.1;... + 'vBeta',0.2}); + % Add reactions +for i = 1:2 + stoichForward = zeros(size(Model.species,1),1); + stoichForward([i,i+1])=[-1,1]; + Model = Model.addReaction(['vAlpha*E',num2str(i)],stoichForward); + + stoichBackward = zeros(size(Model.species,1),1); + stoichBackward([i,i+1])=[1,-1]; + Model = Model.addReaction(['vBeta*E',num2str(i+1),'*',num2str(i)],stoichBackward); + + if i>1 + Model = Model.addParameter({['k',num2str(i)],1}); + stoichProduction = zeros(size(Model.species,1),1); + stoichProduction(end)=1; + Model = Model.addReaction(['k',num2str(i),'*E',num2str(i)],stoichProduction); + end + +end +Model = Model.addParameter({'k3',5;'deg',0.2}); + +stoichProduction = zeros(size(Model.species,1),1); +stoichProduction(end)=1; +Model = Model.addReaction('k3*E3',stoichProduction); + +stoichDegradation = zeros(size(Model.species,1),1); +stoichDegradation(end)=-1; +Model = Model.addReaction('deg*NP',stoichDegradation); + +Model.summarizeModel +%% Generate Equations for Propensities +Model = Model.formPropensitiesGeneral('EnzymeModel'); + +%% Solve the FSP +Model.fspOptions.verbose = true; +[fspSoln,Model.fspOptions.bounds] = Model.solve; + +%% Generate In Silico Data +Model.ssaOptions.nSimsPerExpt = 10; +Model.ssaOptions.Nexp = 1; +Model.tSpan = linspace(0,2,9); +Model.sampleDataFromFSP(fspSoln,'InSilicoData.csv') + +%% +Model = Model.loadData('InSilicoData.csv',{'NP','exp1_s4'}); +Model.makeFitPlot + +%% +% fitOptions = optimset('Display','iter','MaxIter',1000); +% fitOptions.SIG = []; +% for i=1:3 +% fitPars = Model.maximizeLikelihood([],fitOptions); +% Model.parameters(:,2) = num2cell(fitPars); +% end + +%% +% run Metropolis Hastings +MHFitOptions.thin=1; +MHFitOptions.numberOfSamples=1000; +MHFitOptions.burnIn=0; +MHFitOptions.progress=true; +MHFitOptions.useFIMforMetHast =true; +MHFitOptions.suppressFSPExpansion = true; +MHFitOptions.CovFIMscale = 1; +MHFitOptions.numChains = 1; +MHFitOptions.saveFile = 'TMPMHChain.mat'; +Model.fittingOptions.modelVarsToFit = [1:5]; +delete('TMPMHChain.mat') +[newPars,~,MHResults] = Model.maximizeLikelihood(... + [], MHFitOptions, 'MetropolisHastings'); +Model.parameters(Model.fittingOptions.modelVarsToFit,2) = num2cell(newPars); +delete('TMPMHChain.mat') + +%% +Model.plotMHResults(MHResults) + +%% FIM Calculation +%% Solve FSP Sensitivity +Model.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity +[sensSoln,bounds] = Model.solve(fspSoln.stateSpace); % Solve the sensitivity problem + +% Set model to ignore unobservable species. +Model.pdoOptions.unobservedSpecies = {'E1','E2','E3'}; + +% Compute the FIM for each time point +fimResults = Model.computeFIM(sensSoln.sens); + +% Choose experiment design (number of measurements per time for each time). +cellCounts = 100*ones(size(Model.tSpan)); + +% Compute the total FIM: +[fimTotal,mleCovEstimate,fimMetrics] = ... + Model.evaluateExperiment(fimResults,cellCounts); + +% Compare to the MH results +Model.plotMHResults(MHResults,fimTotal) + +% Optimize to find the best experiment for same number of spot/cells +allowableCellNumber = 2100; +OptimumExperiment = Model.optimizeCellCounts(fimResults,allowableCellNumber,... + 'Determinant',[],[],[]); + +% Compute the total FIM: +fimTotalOptimized = Model.evaluateExperiment(fimResults,OptimumExperiment); + +% Compare to the MH results +Model.plotMHResults(MHResults,[fimTotal,fimTotalOptimized]) + +%% trouble shooting unidentifiable models +[Vecs,Vals] = eig(full(fimTotal{1})); +JUncertain = find(diag(Vals)<1e-9) +figure +bar(Vecs(:,JUncertain)) \ No newline at end of file From 0055cccf5e5f98e0cb0e24288722710094386353 Mon Sep 17 00:00:00 2001 From: Munsky Date: Mon, 13 Nov 2023 16:00:36 -0700 Subject: [PATCH 07/14] Update makeSeparatePlotOfData.m --- src/gui/data_loading/makeSeparatePlotOfData.m | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/gui/data_loading/makeSeparatePlotOfData.m b/src/gui/data_loading/makeSeparatePlotOfData.m index b155c11..de2a0b0 100644 --- a/src/gui/data_loading/makeSeparatePlotOfData.m +++ b/src/gui/data_loading/makeSeparatePlotOfData.m @@ -78,12 +78,12 @@ function makeSeparatePlotOfData(app,smoothWindow,fignums,usePanels) L{end+1} = [le,'-data']; xm = max(xm,length(H1)); - if ~isempty(soMod) - M = squeeze(app.DataLoadingAndFittingTabOutputs.fitResults.current(it,:,:,:,:,:,:,:,:,:,:,:,:)); - H1 = squeeze(sum(M,soMod)); - else + % if ~isempty(soMod) + % M = squeeze(app.DataLoadingAndFittingTabOutputs.fitResults.current(it,:,:,:,:,:,:,:,:,:,:,:,:)); + % H1 = squeeze(sum(M,soMod)); + % else H1 = squeeze(app.DataLoadingAndFittingTabOutputs.fitResults.current(it,:,:,:,:,:,:,:,:,:,:,:,:)); - end + % end H1(end+1)=0; if DistType stairs(FNHists,[0:length(H1)-1],smoothBins(H1,smoothWindow),'linewidth',3); From 9a8a99839b745ab72e4f0cc9cc64b2c634c4ee58 Mon Sep 17 00:00:00 2001 From: Dmitri Svetlov <123213937+dmitrisvetlov@users.noreply.github.com> Date: Mon, 13 Nov 2023 17:37:26 -0700 Subject: [PATCH 08/14] Continue work on Grad Show examples --- WorkSpace/Dmitri/FIV_SSITExample_GradShow.m | 119 +++++++++++++------ WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m | 46 ++++--- 2 files changed, 111 insertions(+), 54 deletions(-) diff --git a/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m b/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m index 84b35b9..8f52146 100644 --- a/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m +++ b/WorkSpace/Dmitri/FIV_SSITExample_GradShow.m @@ -3,19 +3,23 @@ addpath(genpath('../../src')); Model = SSIT; + +% Model.species = {'S','RegrI','ProgI','R'}; +% Model.initialCondition = [180;5;5;10]; Model.species = {}; Model.initialCondition = []; + Model.propensityFunctions = {}; Model.stoichiometry = []; Model.parameters = {}; -Model.tSpan = linspace(0,2,21); +Model.tSpan = linspace(0,20,4); % Add species -Model = Model.addSpecies('S',100); -Model = Model.addSpecies('Iregr',0); -Model = Model.addSpecies('Iprog',0); -Model = Model.addSpecies('R',0); +Model = Model.addSpecies('S',20); +Model = Model.addSpecies('RegrI',4); +Model = Model.addSpecies('ProgI',4); +Model = Model.addSpecies('R',8); Model.stoichiometry = []; @@ -27,63 +31,93 @@ Model = Model.addParameter({'progDeath',1/18}); Model = Model.addParameter({'regrRecovery',1/18}); Model = Model.addParameter({'respawn',1/6}); +% Model = Model.addParameter({'infectionArrival',1/10}); P = 0.25; % Proportion randomly assigned to progressive and regressive states % Add reactions -% [S][Iregr][Iprog][R] - -% Regressive infection -stoichRegrInfect = [0,-3,1,2]; -Model = Model.addReaction('contact*progTransmit*regrTransmitMultiplier*Iregr',stoichRegrInfect); - -% Progressive infection -stoichProgInfect = [0,1,-3,2]; -Model = Model.addReaction('contact*progTransmit*Iprog',stoichProgInfect); +% [S][RegrI][ProgI][R] +% +% % Regressive infection due to arrival from outside +% stoichRegrInfectOutside = [-1;1;0;0]; +% Model = Model.addReaction('infectionArrival*S/4',stoichRegrInfectOutside); +% +% % Progressive infection due to arrival from outside +% stoichProgInfectOutside = [-1;0;1;0]; +% Model = Model.addReaction('infectionArrival*S/4',stoichProgInfectOutside); +% +% % Abortive infection due to arrival from outside +% stoichAborInfectOutside = [-1;0;0;1]; +% Model = Model.addReaction('infectionArrival*S/2',stoichAborInfectOutside); + +% Regressive infection from regressive +stoichRegrInfectRegr = [-1;1;0;0]; +Model = Model.addReaction('contact*progTransmit*regrTransmitMultiplier*S*RegrI/4',stoichRegrInfectRegr); + +% Progressive infection from regressive +stoichProgInfectRegr = [-1;0;1;0]; +Model = Model.addReaction('contact*progTransmit*regrTransmitMultiplier*S*RegrI/4',stoichProgInfectRegr); + +% Abortive infection from regressive +stoichAborInfectRegr = [-1;0;0;1]; +Model = Model.addReaction('contact*progTransmit*regrTransmitMultiplier*S*RegrI/2',stoichAborInfectRegr); + +% Regressive infection from progressive +stoichRegrInfectProg = [-1;1;0;0]; +Model = Model.addReaction('contact*progTransmit*S*ProgI/4',stoichRegrInfectProg); + +% Progressive infection from progressive +stoichProgInfectProg = [-1;0;1;0]; +Model = Model.addReaction('contact*progTransmit*S*ProgI/4',stoichProgInfectProg); + +% Abortive infection from progressive +stoichAborInfectProg = [-1;0;0;1]; +Model = Model.addReaction('contact*progTransmit*S*ProgI/2',stoichAborInfectProg); % Recovery from regressive infection -stoichRegrRecovery = [0,-1,0,1]; -Model = Model.addReaction('regrRecovery*Iregr',stoichRegrRecovery); +stoichRegrRecovery = [0;-1;0;1]; +Model = Model.addReaction('regrRecovery*RegrI',stoichRegrRecovery); % Death from progressive infection -stoichProgDeath = [0,0,-1,0]; -Model = Model.addReaction('progDeath*Iprog',stoichProgDeath); +stoichProgDeath = [0;0;-1;0]; +Model = Model.addReaction('progDeath*ProgI',stoichProgDeath); % Respawn -stoichRespawn = [1,0,0,0]; +stoichRespawn = [1;0;0;0]; Model = Model.addReaction('respawn',stoichRespawn); Model.summarizeModel %% Generate Equations for Propensities -%Model = Model.formPropensitiesGeneral('EnzymeModel'); +Model = Model.formPropensitiesGeneral('DiseaseSpreadModel'); %% Solve the FSP + +Model.tSpan = linspace(0,20,4); Model.fspOptions.verbose = true; [fspSoln,Model.fspOptions.bounds] = Model.solve; %% Generate In Silico Data -Model.ssaOptions.nSimsPerExpt = 10; +Model.ssaOptions.nSimsPerExpt = 50; Model.ssaOptions.Nexp = 1; -Model.tSpan = linspace(0,2,9); -Model.sampleDataFromFSP(fspSoln,'InSilicoData_FIV.csv') +Model.sampleDataFromFSP(fspSoln,'InSilicoData.csv') %% -Model = Model.loadData('InSilicoData_FIV.csv',{'S','exp1_s4'}); +Model = Model.loadData('InSilicoData.csv',{'R','exp1_s4'}); Model.makeFitPlot %% -% fitOptions = optimset('Display','iter','MaxIter',1000); -% fitOptions.SIG = []; -% for i=1:3 -% fitPars = Model.maximizeLikelihood([],fitOptions); -% Model.parameters(:,2) = num2cell(fitPars); -% end +fitOptions = optimset('Display','iter','MaxIter',1000); +fitOptions.SIG = []; +for i=1:3 + fitPars = Model.maximizeLikelihood([],fitOptions); + Model.parameters(:,2) = num2cell(fitPars); +end %% % run Metropolis Hastings MHFitOptions.thin=1; -MHFitOptions.numberOfSamples=1000; +MHFitOptions.numberOfSamples=2000; MHFitOptions.burnIn=0; MHFitOptions.progress=true; MHFitOptions.useFIMforMetHast =true; @@ -106,11 +140,13 @@ Model.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity [sensSoln,bounds] = Model.solve(fspSoln.stateSpace); % Solve the sensitivity problem +% All species are observable + % Compute the FIM for each time point fimResults = Model.computeFIM(sensSoln.sens); % Choose experiment design (number of measurements per time for each time). -cellCounts = 10*ones(size(Model.tSpan)); +cellCounts = Model.dataSet.nCells; % Compute the total FIM: [fimTotal,mleCovEstimate,fimMetrics] = ... @@ -119,10 +155,10 @@ % Compare to the MH results Model.plotMHResults(MHResults,fimTotal) -% Optimize to find the best experiment for same number of spot/cells -allowableCellNumber = 500; +%% Optimize to find the best experiment for same number of spot/cells +allowableCellNumber = sum(Model.dataSet.nCells); OptimumExperiment = Model.optimizeCellCounts(fimResults,allowableCellNumber,... - 'Determinant',[],[],[]); + 'Determinant',[],[],[]) % Compute the total FIM: fimTotalOptimized = Model.evaluateExperiment(fimResults,OptimumExperiment); @@ -134,4 +170,15 @@ [Vecs,Vals] = eig(full(fimTotal{1})); JUncertain = find(diag(Vals)<1e-9) figure -bar(Vecs(:,JUncertain)) \ No newline at end of file +bar(Vecs(:,JUncertain)) + +%% +figure +subplot(1,2,1) +pie(Model.dataSet.nCells) +subplot(1,2,2) +pie(OptimumExperiment) + +%% +det(fimTotalOptimized{1}^-1) +det(fimTotal{1}^-1) \ No newline at end of file diff --git a/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m b/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m index 505f728..374dd18 100644 --- a/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m +++ b/WorkSpace/Dmitri/SeNP_SSITExample_GradShow.m @@ -6,10 +6,8 @@ Model.species = {}; Model.initialCondition = []; Model.propensityFunctions = {}; -Model.propensityFunctions = {}; Model.stoichiometry = []; Model.parameters = {}; -Model.tSpan = linspace(0,2,21); % Add species for i=1:3 @@ -24,8 +22,8 @@ Model.stoichiometry = []; -Model = Model.addParameter({'vAlpha',0.1;... - 'vBeta',0.2}); +Model = Model.addParameter({'vAlpha',0.2;... + 'vBeta',0.1}); % Add reactions for i = 1:2 stoichForward = zeros(size(Model.species,1),1); @@ -59,13 +57,14 @@ Model = Model.formPropensitiesGeneral('EnzymeModel'); %% Solve the FSP + +Model.tSpan = linspace(0,20,4); Model.fspOptions.verbose = true; [fspSoln,Model.fspOptions.bounds] = Model.solve; %% Generate In Silico Data -Model.ssaOptions.nSimsPerExpt = 10; +Model.ssaOptions.nSimsPerExpt = 50; Model.ssaOptions.Nexp = 1; -Model.tSpan = linspace(0,2,9); Model.sampleDataFromFSP(fspSoln,'InSilicoData.csv') %% @@ -73,17 +72,17 @@ Model.makeFitPlot %% -% fitOptions = optimset('Display','iter','MaxIter',1000); -% fitOptions.SIG = []; -% for i=1:3 -% fitPars = Model.maximizeLikelihood([],fitOptions); -% Model.parameters(:,2) = num2cell(fitPars); -% end +fitOptions = optimset('Display','iter','MaxIter',1000); +fitOptions.SIG = []; +for i=1:3 + fitPars = Model.maximizeLikelihood([],fitOptions); + Model.parameters(:,2) = num2cell(fitPars); +end %% % run Metropolis Hastings MHFitOptions.thin=1; -MHFitOptions.numberOfSamples=1000; +MHFitOptions.numberOfSamples=2000; MHFitOptions.burnIn=0; MHFitOptions.progress=true; MHFitOptions.useFIMforMetHast =true; @@ -113,7 +112,7 @@ fimResults = Model.computeFIM(sensSoln.sens); % Choose experiment design (number of measurements per time for each time). -cellCounts = 100*ones(size(Model.tSpan)); +cellCounts = Model.dataSet.nCells; % Compute the total FIM: [fimTotal,mleCovEstimate,fimMetrics] = ... @@ -122,10 +121,10 @@ % Compare to the MH results Model.plotMHResults(MHResults,fimTotal) -% Optimize to find the best experiment for same number of spot/cells -allowableCellNumber = 2100; +%% Optimize to find the best experiment for same number of spot/cells +allowableCellNumber = sum(Model.dataSet.nCells); OptimumExperiment = Model.optimizeCellCounts(fimResults,allowableCellNumber,... - 'Determinant',[],[],[]); + 'Determinant',[],[],[]) % Compute the total FIM: fimTotalOptimized = Model.evaluateExperiment(fimResults,OptimumExperiment); @@ -137,4 +136,15 @@ [Vecs,Vals] = eig(full(fimTotal{1})); JUncertain = find(diag(Vals)<1e-9) figure -bar(Vecs(:,JUncertain)) \ No newline at end of file +bar(Vecs(:,JUncertain)) + +%% +figure +subplot(1,2,1) +pie(Model.dataSet.nCells) +subplot(1,2,2) +pie(OptimumExperiment) + +%% +det(fimTotalOptimized{1}^-1) +det(fimTotal{1}^-1) From 775b6d4307c38de5eb2daaa1f9a569336cfdbadf Mon Sep 17 00:00:00 2001 From: Munsky Date: Tue, 14 Nov 2023 10:28:27 -0700 Subject: [PATCH 09/14] Update propensity generation to use parfor loop. --- src/+ssit/@Propensity/Propensity.m | 90 +++++++++++++++++++----------- 1 file changed, 56 insertions(+), 34 deletions(-) diff --git a/src/+ssit/@Propensity/Propensity.m b/src/+ssit/@Propensity/Propensity.m index abe0bce..56d18dc 100644 --- a/src/+ssit/@Propensity/Propensity.m +++ b/src/+ssit/@Propensity/Propensity.m @@ -159,7 +159,7 @@ species upstreamODEs = {}; logicTerms = {}; - prefixName = []; + prefixName = 'default'; computeSens = false; end @@ -231,9 +231,23 @@ expr_x_vec_sens = sym('wx',[n_reactions,n_pars]); end - anyLogical = false; - + anyLogical = zeros(1,n_reactions,'logical'); + + t = sym('t','real'); for iRxn = 1:n_reactions + prop_vars = symvar(symbolicExpression{iRxn}); + syms(prop_vars,'real'); + end + if ~isempty(upstreamODEs) + syms(upstreamODEs,'positive') + end + oneSym = str2sym('1'); + + % change to parfor? + parfor iRxn = 1:n_reactions + prop_vars = symvar(symbolicExpression{iRxn}); + hybridFactor =[]; + prefixNameLocal = [prefixName,'_',num2str(iRxn)]; obj{iRxn} = ssit.Propensity(stoichMatrix(jStochastic,iRxn), iRxn); obj{iRxn}.ODEstoichVector = stoichMatrix(jODE,iRxn); @@ -242,12 +256,11 @@ warning(['Reaction ',num2str(iRxn),' changes both ODE and stochastic species. Removing effect on upstream species.']) obj{iRxn}.ODEstoichVector = 0*obj{iRxn}.ODEstoichVector; end - prop_vars = symvar(symbolicExpression{iRxn}); + % prop_vars = symvar(symbolicExpression{iRxn}); - syms(prop_vars,'real'); - syms t real - expr_tx = symbolicExpression{iRxn}; + % syms t real + expr_tx = symbolicExpression{iRxn}; % Convert to callable function Jx = zeros(1,length(prop_vars),'logical'); % List of variables corrresponding to species Jt = zeros(1,length(prop_vars),'logical'); % List of variables corrresponding to time @@ -257,20 +270,21 @@ Jt(j) = max(strcmp(string(prop_vars(j)),['t',upstreamODEs])); end end + if sum(Jx)==0 % No x dependance - factors = [str2sym('1'),symbolicExpression{iRxn}]; + factors = [oneSym,symbolicExpression{iRxn}]; elseif sum(Jt)==0 % No t dependence - factors = [symbolicExpression{iRxn},str2sym('1')]; + factors = [symbolicExpression{iRxn},oneSym]; else % Determine if the symbolicExpression is isFactorizable factors = factor(symbolicExpression{iRxn}, prop_vars(Jx)); factors = [prod(factors(2:end)),factors(1)]; - if ~isempty(upstreamODEs) - syms(upstreamODEs,'positive') - end + % if ~isempty(upstreamODEs) + % syms(upstreamODEs,'positive') + % end if (ismember(t, symvar(factors(1))))||(~isempty(upstreamODEs)&&max(ismember(upstreamODEs,symvar(factors(1))))) obj{iRxn}.isFactorizable = false; @@ -303,7 +317,7 @@ if (~isempty(string(symvar(expr_x)))&&max(contains(string(symvar(expr_x)),'logT')))||... (~isempty(string(symvar(expr_t)))&&max(contains(string(symvar(expr_t)),'logX'))) obj{iRxn}.isFactorizable = false; - anyLogical = true; + anyLogical(iRxn) = true; end if (obj{iRxn}.isFactorizable) @@ -321,27 +335,29 @@ if ~isempty(logicTerms{iRxn})&&(isfield(logicTerms{iRxn},'logT')||isfield(logicTerms{iRxn},'logE')) obj{iRxn}.anonymousT = true; hybridFactor = sym2propfun(expr_t, true, false, nonXTpars(:,1), speciesStoch, varODEs, logicTerms(iRxn)); - anyLogical = true; + anyLogical(iRxn) = true; % elseif sum(contains(string(symvar(expr_t)),'logX')) % obj{iRxn}.anonymousT = true; % obj{iRxn}.isFactorizable = false; % hybridFactor = sym2propfun(expr_t, true, false, nonXTpars(:,1), speciesStoch, varODEs, logicTerms(iRxn)); - % anyLogical = true; + % anyLogical(iRxn) = true; else obj{iRxn}.anonymousT = false; % hybridFactor = sym2mFun(expr_t, true, false, nonXTpars(:,1), speciesStoch, varODEs); end + if ~isempty(logicTerms{iRxn})&&(isfield(logicTerms{iRxn},'logX')||isfield(logicTerms{iRxn},'logE')) obj{iRxn}.anonymousX = true; - anyLogical = true; + anyLogical(iRxn) = true; % elseif sum(contains(string(symvar(expr_x)),'logT')) % obj{iRxn}.isFactorizable = false; % obj{iRxn}.anonymousX = true; - % anyLogical = true; + % anyLogical(iRxn) = true; else obj{iRxn}.anonymousX = false; end + % Compute the time-varying factor. if ~isempty(logicTerms{iRxn})&&(isfield(logicTerms{iRxn},'logT')||isfield(logicTerms{iRxn},'logE')) if ~isempty(jODE) @@ -355,10 +371,10 @@ % TmpHybridFactor = hybridFactor(rand,[nonXTpars{:,2}]',rand(1,length(upstreamODEs))); TmpHybridFactor = subs(expr_t,t,rand); TmpHybridFactor = subs(TmpHybridFactor,nonXTpars(:,1),rand(size(nonXTpars(:,2)))); - TmpHybridFactor = eval(subs(TmpHybridFactor,varODEs,rand(size(varODEs)))); + TmpHybridFactor = double(subs(TmpHybridFactor,varODEs,rand(size(varODEs)))); else TmpHybridFactor = subs(expr_t,t,rand); - TmpHybridFactor = eval(subs(TmpHybridFactor,nonXTpars(:,1),rand(size(nonXTpars(:,2))))); + TmpHybridFactor = double(subs(TmpHybridFactor,nonXTpars(:,1),rand(size(nonXTpars(:,2))))); % TmpHybridFactor = hybridFactor(rand,[nonXTpars{:,2}]'); end end @@ -373,21 +389,21 @@ sym2propfun(signHybridFactor*expr_t, true, false, nonXTpars(:,1), speciesStoch, varODEs, logicTerms(iRxn), true); else [~,expr_dt_vec_dodei] = ... - sym2mFun(signHybridFactor*expr_t, true, false, nonXTpars(:,1), speciesStoch, varODEs, true, false, prefixName); + sym2mFun(signHybridFactor*expr_t, true, false, nonXTpars(:,1), speciesStoch, varODEs, true, false, prefixNameLocal); end - + % Then for the state varying factors. if obj{iRxn}.anonymousX obj{iRxn}.stateDependentFactor =... sym2propfun(signHybridFactor*expr_x, false, true, nonXTpars(:,1), speciesStoch, [], logicTerms(iRxn)); else obj{iRxn}.stateDependentFactor =... - sym2mFun(signHybridFactor*expr_x, false, true, nonXTpars(:,1), speciesStoch, [], false, true, prefixName); + sym2mFun(signHybridFactor*expr_x, false, true, nonXTpars(:,1), speciesStoch, [], false, true, prefixNameLocal); end - + expr_t_vec(iRxn) = signHybridFactor*expr_t; expr_x_vec(iRxn) = signHybridFactor*expr_x; - if ~isempty(expr_dt_vec_dode)&&exist('expr_dt_vec_dodei','var') + if ~isempty(expr_dt_vec_dodei) expr_dt_vec_dode(iRxn,:) = signHybridFactor*expr_dt_vec_dodei; end if ismember(t,symvar(expr_t))||isfield(logicTerms{iRxn},'logT')&&~isempty(logicTerms{iRxn}.logT) @@ -408,7 +424,7 @@ obj{iRxn}.isTimeDependent = true; expr_t_vec(iRxn) = sym(NaN); expr_x_vec(iRxn) = sym(NaN); - + % if computeSens % for iPar = 1:n_pars % expr_tx_vec_sens(iRxn,iPar) = signHybridFactor*diff(expr_tx,nonXTpars{iPar,1}); @@ -418,9 +434,11 @@ end end + prefixNameLocal = prefixName; + % Create a vector function for the time varying part of the % propensity functions so that these can be found all at once. - if anyLogical + if any(anyLogical) hybridFactorVector = sym2propfun(expr_t_vec, true, false, nonXTpars(:,1), speciesStoch, varODEs, logicTerms); xFactorVector = sym2propfun(expr_x_vec, false, true, nonXTpars(:,1), speciesStoch, varODEs, logicTerms); if ~isempty(expr_dt_vec_dode) @@ -437,20 +455,24 @@ end end else - hybridFactorVector = sym2mFun(expr_t_vec, true, false, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixName); - xFactorVector = sym2mFun(expr_x_vec, false, true, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixName); + hybridFactorVector = sym2mFun(expr_t_vec, true, false, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixNameLocal); + xFactorVector = sym2mFun(expr_x_vec, false, true, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixNameLocal); if ~isempty(expr_dt_vec_dode) - obj{1}.DhybridFactorDodesVec = sym2mFun(expr_dt_vec_dode, true, false, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixName); + obj{1}.DhybridFactorDodesVec = sym2mFun(expr_dt_vec_dode, true, false, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixNameLocal); end if computeSens - obj{1}.sensStateFactorVec = sym2mFun(expr_x_vec_sens, false, true, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixName); - for iRxn = 1:n_reactions + obj{1}.sensStateFactorVec = sym2mFun(expr_x_vec_sens, false, true, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixNameLocal); + parfor iRxn = 1:n_reactions obj{iRxn}.sensStateFactor = cell(1,n_pars); for ipar = 1:n_pars - obj{iRxn}.sensStateFactor{ipar} = sym2mFun(expr_x_vec_sens(iRxn,ipar), false, true, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixName); - obj{1}.sensTimeFactorVec{ipar} = sym2mFun(expr_t_vec_sens(:,ipar), true, false, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixName); + prefixNameLocal = [prefixName,'_',num2str(iRxn),'_',num2str(ipar)]; + obj{iRxn}.sensStateFactor{ipar} = sym2mFun(expr_x_vec_sens(iRxn,ipar), false, true, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixNameLocal); end end + for ipar = 1:n_pars + prefixNameLocal = [prefixName,'_',num2str(iRxn),'_',num2str(ipar)]; + obj{1}.sensTimeFactorVec{ipar} = sym2mFun(expr_t_vec_sens(:,ipar), true, false, nonXTpars(:,1), speciesStoch, varODEs, false, true, prefixNameLocal); + end end end obj{1}.hybridFactorVector = hybridFactorVector; @@ -778,7 +800,7 @@ prefixName = prefixName(j+1:end); end -ifn = sum(contains({dir('tmpPropensityFunctions').name},'fun'))+1; +ifn = sum(contains({dir('tmpPropensityFunctions').name},[prefixName,'_fun']))+1; fn = [pwd,'/tmpPropensityFunctions/',prefixName,'_fun_',num2str(ifn),'.m']; if jacobian&&~isempty(varODEs) From 2397b7a2e61f89d52038754cdbba0e615a584b2b Mon Sep 17 00:00:00 2001 From: Munsky Date: Tue, 14 Nov 2023 10:29:46 -0700 Subject: [PATCH 10/14] Add default name for generated propensity functions. --- src/CommandLine/SSIT.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/CommandLine/SSIT.m b/src/CommandLine/SSIT.m index 437992e..484ad9a 100644 --- a/src/CommandLine/SSIT.m +++ b/src/CommandLine/SSIT.m @@ -78,7 +78,7 @@ function obj = formPropensitiesGeneral(obj,prefixName,computeSens) arguments obj - prefixName = []; + prefixName = 'default'; computeSens = true; end % This function starts the process to write m-file for each From 8cd239f1a211b92a8ead68dde96a0ab4e9c029c7 Mon Sep 17 00:00:00 2001 From: Munsky Date: Tue, 14 Nov 2023 10:30:12 -0700 Subject: [PATCH 11/14] fix paths to src codes for test scripts. --- tests/miscelaneousTests.m | 2 +- tests/modelReductionTest.m | 2 +- tests/poisson2Dtest.m | 2 +- tests/poissonTVtest.m | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/miscelaneousTests.m b/tests/miscelaneousTests.m index de38d25..d2ca5b8 100644 --- a/tests/miscelaneousTests.m +++ b/tests/miscelaneousTests.m @@ -8,7 +8,7 @@ methods (TestClassSetup) % Shared setup for the entire test class function createTestModel1(tc) - addpath('../CommandLine') + addpath(genpath('../src')); tc.Model = SSIT; end end diff --git a/tests/modelReductionTest.m b/tests/modelReductionTest.m index 0c546fb..35a2868 100644 --- a/tests/modelReductionTest.m +++ b/tests/modelReductionTest.m @@ -8,7 +8,7 @@ methods (TestClassSetup) % Shared setup for the entire test class function createTestModel1(tc) - addpath('../CommandLine') + addpath(genpath('../src')); %% Test Case 3 - 2 Species Poisson Model tc.TwoDPoiss = SSIT; diff --git a/tests/poisson2Dtest.m b/tests/poisson2Dtest.m index f219ba1..9df29e4 100644 --- a/tests/poisson2Dtest.m +++ b/tests/poisson2Dtest.m @@ -8,7 +8,7 @@ methods (TestClassSetup) % Shared setup for the entire test class function createTestModel1(testCase1) - addpath('../CommandLine') + addpath(genpath('../src')); %% Test Case 3 - 2 Species Poisson Model testCase1.TwoDPoiss = SSIT; diff --git a/tests/poissonTVtest.m b/tests/poissonTVtest.m index 2d4277f..21092b1 100644 --- a/tests/poissonTVtest.m +++ b/tests/poissonTVtest.m @@ -8,7 +8,7 @@ methods (TestClassSetup) % Shared setup for the entire test class function createTestModel1(testCase1) - addpath('../CommandLine') + addpath(genpath('../src')); %% Test Case - a simple Poisson model with time varying production testCase1.TvPoiss = SSIT; testCase1.TvPoiss.species = {'rna'}; From 67ab4558fc92f38bc602033eb412c2521b73632f Mon Sep 17 00:00:00 2001 From: Munsky Date: Tue, 21 Nov 2023 14:11:51 -0700 Subject: [PATCH 12/14] Update a few demo examples and their published results. --- Examples/example_ExtrinsicNoiseModels.m | 3 +- Examples/example_HybridModel.m | 3 +- ...xample_IterativeBayesianExperimentDesign.m | 2 + Examples/example_ModelReductionTool.m | 9 ++- Examples/html/example_EscapeTimes.html | 6 +- Examples/html/example_EscapeTimes.png | Bin 2597 -> 2299 bytes Examples/html/example_EscapeTimes_01.png | Bin 14855 -> 14949 bytes Examples/html/example_EscapeTimes_02.png | Bin 16950 -> 17020 bytes Examples/html/example_EscapeTimes_03.png | Bin 19538 -> 19567 bytes Examples/html/example_EscapeTimes_04.png | Bin 18449 -> 18566 bytes 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Examples/html/example_MultiModelTool_43.png | Bin 24915 -> 24901 bytes Examples/html/example_MultiModelTool_44.png | Bin 33854 -> 34351 bytes Examples/html/example_SSITBasics.html | 8 +- Examples/html/example_SSITBasics.png | Bin 2934 -> 2612 bytes Examples/html/example_SSITBasics_01.png | Bin 7489 -> 7528 bytes Examples/html/example_SSITBasics_02.png | Bin 15905 -> 16030 bytes Examples/html/example_SSITBasics_03.png | Bin 142052 -> 134827 bytes Examples/html/example_SSITBasics_04.png | Bin 14107 -> 14078 bytes 117 files changed, 106 insertions(+), 75 deletions(-) delete mode 100644 Examples/html/example_FittingAndDesigningExperiments_27.png delete mode 100644 Examples/html/example_FittingAndDesigningExperiments_28.png diff --git a/Examples/example_ExtrinsicNoiseModels.m b/Examples/example_ExtrinsicNoiseModels.m index 909691e..de488df 100644 --- a/Examples/example_ExtrinsicNoiseModels.m +++ b/Examples/example_ExtrinsicNoiseModels.m @@ -29,4 +29,5 @@ 2+0.4*randn,... 1+0.2*randn]; extrinsicModel = extrinsicSSIT(Model1,parDistributions,20); -Model1.makePlot(extrinsicModel.averagedResults,'marginals',[],[],[2,3]) \ No newline at end of file +Model1.makePlot(extrinsicModel.averagedResults,'marginals',[],[],[2,3]) +legend('Intrinsic Only','Intrinsic + Extrinsic','Location','EastOutside') \ No newline at end of file diff --git a/Examples/example_HybridModel.m b/Examples/example_HybridModel.m index 689f91d..66bc330 100644 --- a/Examples/example_HybridModel.m +++ b/Examples/example_HybridModel.m @@ -27,6 +27,7 @@ Model2.hybridOptions.upstreamODEs = {'rna'}; [fspSoln2, Model2.fspOptions.bounds] = Model2.solve; Model2.makePlot(fspSoln2,'marginals',[],[],3) +legend('Full','QSSA for (n)','Location','eastoutside') %% Example 2 - 5-species MAPK induction Model % In this example, we consider a model of MAPK translocation to the nucleus @@ -71,4 +72,4 @@ Model5.hybridOptions.upstreamODEs = {'mapkCyt','mapkNuc'}; [fspSoln5, Model5.fspOptions.bounds] = Model5.solve; Model5.makePlot(fspSoln5,'marginals',[],[],[11,12,15]) - +legend('Full','QSSA for (gene,MAKP)','QSSA for (MAKP)','Location','eastoutside') diff --git a/Examples/example_IterativeBayesianExperimentDesign.m b/Examples/example_IterativeBayesianExperimentDesign.m index 799f206..1500042 100644 --- a/Examples/example_IterativeBayesianExperimentDesign.m +++ b/Examples/example_IterativeBayesianExperimentDesign.m @@ -34,6 +34,8 @@ fitParameters = [1:4]; end +experimentDesignCriteria = + saveFileName = ['IterativeExperimentResults_',example]; %% Generate Model Propensity Functions and Solve True Model diff --git a/Examples/example_ModelReductionTool.m b/Examples/example_ModelReductionTool.m index 387321e..1f4287c 100644 --- a/Examples/example_ModelReductionTool.m +++ b/Examples/example_ModelReductionTool.m @@ -135,7 +135,10 @@ % Make Figures to compare the results. Here, we will plot the original % model in blue and the reduced model in red lines. Model1.makePlot(fspSoln,'meansAndDevs',[],[],1,{'Color',[0,0,1]}) -Model1.makePlot(fspSoln,'marginals',[],[],[2,3],{'Color',[0,0,1]}) - Model2.makePlot(fspSolnRed,'meansAndDevs',[],[],1,{'Color',[1,0,0]}) -Model2.makePlot(fspSolnRed,'marginals',[],[],[2,3],{'Color',[1,0,0]}) \ No newline at end of file +figure(1);legend('Full','Reduced','Location','southeast') + +Model1.makePlot(fspSoln,'marginals',[],[],[2,3],{'Color',[0,0,1]}) +Model2.makePlot(fspSolnRed,'marginals',[],[],[2,3],{'Color',[1,0,0]}) +figure(2);legend('Full','Reduced','Location','eastoutside') +figure(3);legend('Full','Reduced','Location','eastoutside') \ No newline at end of file diff --git a/Examples/html/example_EscapeTimes.html b/Examples/html/example_EscapeTimes.html index 54e4027..f6a3baf 100644 --- a/Examples/html/example_EscapeTimes.html +++ b/Examples/html/example_EscapeTimes.html @@ -6,7 +6,7 @@ example_EscapeTimes

Contents

example_EscapeTimes

In this script, we demonstrate how to create and solve a fiorst passage time problem.

close all
 clear all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 

Example 1 - a simple transcription/translation model

First create a full model (e.g., for mRNA and protein)

Model1 = SSIT;
 Model1.species = {'rna','protein'};
 Model1.initialCondition = [0;0];
@@ -112,7 +112,7 @@
 % time problem.
 close all
 clear all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 %% Example 1 - a simple transcription/translation model
 % First create a full model (e.g., for mRNA and protein)
 Model1 = SSIT;
diff --git a/Examples/html/example_EscapeTimes.png b/Examples/html/example_EscapeTimes.png
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diff --git a/Examples/html/example_EscapeTimes_01.png b/Examples/html/example_EscapeTimes_01.png
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diff --git a/Examples/html/example_EscapeTimes_02.png b/Examples/html/example_EscapeTimes_02.png
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r1jP{kaIXnX6NK){V}|hm_4t=~pmpeS+bUT!rUoK*M?BdOm45PX07%gv diff --git a/Examples/html/example_FittingAndDesigningExperiments.html b/Examples/html/example_FittingAndDesigningExperiments.html index fcefe2c..1c13ada 100644 --- a/Examples/html/example_FittingAndDesigningExperiments.html +++ b/Examples/html/example_FittingAndDesigningExperiments.html @@ -6,7 +6,7 @@ example_FittingAndDesigningExperiments

Contents

example_FittingAndDesigningExperiments

In this script, we show how the SSIT can be used to identify a time-inhomogeneous model for the activation of Dusp1 mRNA expression under Dexamethasome stimulation of Glucocorticoid Receptors.

clear all
 clc
 close all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 

Define SSIT Model

Here we set up a simple model where there is an upstream transcription factor (GR) that activates a gene. Once active, the gene can transcribe nuclear RNA, which can later decay or leave the nucleus.

Model = SSIT;
 Model.species = {'activeGene';'rna'};
 Model.initialCondition = [0;0];
@@ -83,6 +83,7 @@
 

Solve the model using the FSP

Next, we can solve the model using the FSP. In this example, we show how to run the code twice. First call finds the FSP projection needed to solve the problem, and the second call solves using that projection.

Model.solutionScheme = 'FSP';
 Model.fspOptions.fspTol = 1e-4;
 Model.fspOptions.bounds(3:4) = [2,400];
+Model = Model.formPropensitiesGeneral('Model');
 [fspSoln,Model.fspOptions.bounds] = Model.solve;
 

Load and Fit smFISH Data

Next we load experimental data from a CSV file and associate the species 'rna' with the collumn 'RNA_nuc'.

Model = Model.loadData('../ExampleData/DUSP1_Dex_100nM_Rep1_Rep2.csv',{'rna','RNA_nuc'});
 Model.fittingOptions.modelVarsToFit = 1:7;
@@ -106,7 +107,7 @@
 Model.fspOptions.fspTol = 1e-6;
 [sensSoln] = Model.solve(fspSoln.stateSpace);
 Model.makePlot(sensSoln,'marginals')
-

Compute Fisher Information (in log-parameter space)

Model.pdoOptions.unobservedSpecies = {'activeGene'};
+

Compute Fisher Information (in log-parameter space)

Model.pdoOptions.unobservedSpecies = {'activeGene'};
 fimResults = Model.computeFIM(sensSoln.sens,'log');
 FIMlog =  Model.evaluateExperiment(fimResults,Model.dataSet.nCells);
 

Metropolis Hastings to Quantify Parameter Uncertainty

Next, we determine the uncertainty in a subset of the estimated parameters. In this case, we assume that the GR parameters ([5:7]) are correct, and we only care about the uncertainity in the other parameters ([1:4]).

Model.solutionScheme = 'FSP';
@@ -122,54 +123,54 @@
 % calculated using the first parameters set, this will cause the MH to
 % converge more slowly.  You will likely want to re-run the fit again
 % multiple times to have a better chance of convergence.
-
n=0; acc=0.5. TMPmh_5.mat
-n=100; acc=0.485. TMPmh_5.mat
-n=200; acc=0.47667. TMPmh_5.mat
-n=300; acc=0.495. TMPmh_5.mat
-n=400; acc=0.504. TMPmh_5.mat
-n=500; acc=0.50167. TMPmh_5.mat
-n=600; acc=0.49143. TMPmh_5.mat
-n=700; acc=0.485. TMPmh_5.mat
-n=800; acc=0.48444. TMPmh_5.mat
-n=900; acc=0.48. TMPmh_5.mat
-n=1000; acc=0.48273. TMPmh_5.mat
-n=1100; acc=0.48083. TMPmh_5.mat
-n=1200; acc=0.48. TMPmh_5.mat
-n=1300; acc=0.47643. TMPmh_5.mat
-n=1400; acc=0.47467. TMPmh_5.mat
-n=1500; acc=0.475. TMPmh_5.mat
-n=1600; acc=0.47353. TMPmh_5.mat
-n=1700; acc=0.47611. TMPmh_5.mat
-n=1800; acc=0.47053. TMPmh_5.mat
-n=1900; acc=0.474. TMPmh_5.mat
-n=2000; acc=0.48. TMPmh_5.mat
-

Solve for Fisher Information Matrix at all Time Points

Using the sensitivity calculation, we next computer the Fisher Information Matrix. Because we only can observe one species 'rna'), we must include that the species 'activeGene' is not observed when formulating the PDO.

Model.pdoOptions.unobservedSpecies = {'activeGene'};
+
n=0; acc=0.51. TMPmh_1.mat
+n=100; acc=0.52. TMPmh_1.mat
+n=200; acc=0.5. TMPmh_1.mat
+n=300; acc=0.4775. TMPmh_1.mat
+n=400; acc=0.474. TMPmh_1.mat
+n=500; acc=0.47833. TMPmh_1.mat
+n=600; acc=0.48571. TMPmh_1.mat
+n=700; acc=0.4925. TMPmh_1.mat
+n=800; acc=0.5. TMPmh_1.mat
+n=900; acc=0.496. TMPmh_1.mat
+n=1000; acc=0.48818. TMPmh_1.mat
+n=1100; acc=0.49417. TMPmh_1.mat
+n=1200; acc=0.49615. TMPmh_1.mat
+n=1300; acc=0.49143. TMPmh_1.mat
+n=1400; acc=0.48933. TMPmh_1.mat
+n=1500; acc=0.48875. TMPmh_1.mat
+n=1600; acc=0.48882. TMPmh_1.mat
+n=1700; acc=0.48667. TMPmh_1.mat
+n=1800; acc=0.48316. TMPmh_1.mat
+n=1900; acc=0.4805. TMPmh_1.mat
+n=2000; acc=0.48476. TMPmh_1.mat
+

Solve for Fisher Information Matrix at all Time Points

Using the sensitivity calculation, we next computer the Fisher Information Matrix. Because we only can observe one species 'rna'), we must include that the species 'activeGene' is not observed when formulating the PDO.

Model.pdoOptions.unobservedSpecies = {'activeGene'};
 fims = Model.computeFIM(sensSoln.sens);
 FIM = Model.evaluateExperiment(fims,Model.dataSet.nCells);
 Model.plotMHResults(mhResults,FIM);
-

Optimize Experiment Design (Same Number of Cells and Timepoints)

Now that we have calculate the FIM and PDO, we can optimize an experiment design to match the total number of cellular measurements as conducted in the original experiment.

nTotal = sum(Model.dataSet.nCells);
+

Optimize Experiment Design (Same Number of Cells and Timepoints)

Now that we have calculate the FIM and PDO, we can optimize an experiment design to match the total number of cellular measurements as conducted in the original experiment.

nTotal = sum(Model.dataSet.nCells);
 nCellsOpt = Model.optimizeCellCounts(fims,nTotal,'TR[1:4]');
 fimOpt = Model.evaluateExperiment(fims,nCellsOpt);
 Model.plotMHResults(mhResults,[FIM,fimOpt]);
-

Calibrate PDO from Multi-Modal Experimental Data

Calibration the PDO from empirical data. Here, the number of spots has been measured using different assays in data columns 'nTotal' for the 'true' data set and in the columns 'nSpots0' for a different label or 'intens1' for the integrated intensity. We calibrate two different PDOs for this case. In both cases, we assume an 'AffinePoiss' PDO where the obervation probability is a Poisson distribution where the mean value is affine linearly related to the true value: P(y|x) = Poiss(a0 + a1*x);

ModelPDOSpots = Model.calibratePDO('../ExampleData/pdoCalibrationData.csv',...
+

Calibrate PDO from Multi-Modal Experimental Data

Calibration the PDO from empirical data. Here, the number of spots has been measured using different assays in data columns 'nTotal' for the 'true' data set and in the columns 'nSpots0' for a different label or 'intens1' for the integrated intensity. We calibrate two different PDOs for this case. In both cases, we assume an 'AffinePoiss' PDO where the obervation probability is a Poisson distribution where the mean value is affine linearly related to the true value: P(y|x) = Poiss(a0 + a1*x);

ModelPDOSpots = Model.calibratePDO('../ExampleData/pdoCalibrationData.csv',...
     {'rna'},{'nTotal'},{'nSpots0'},'AffinePoiss',true);
 
 ModelPDOIntens = Model.calibratePDO('../ExampleData/pdoCalibrationData.csv',...
     {'rna'},{'nTotal'},{'intens1'},'AffinePoiss',true,[1,1000,1]);
-

Compute the FIM for the calculated PDO

Now that we have the new PDO, we can calculate the FIM for the different distorted observation experiments. In each case, we compare the results to the Metropolis Hastings results in which the data was assumed to be undistorted. First, for the alternate label distortion.

fimsPDOSpot = ModelPDOSpots.computeFIM(sensSoln.sens);
+

Compute the FIM for the calculated PDO

Now that we have the new PDO, we can calculate the FIM for the different distorted observation experiments. In each case, we compare the results to the Metropolis Hastings results in which the data was assumed to be undistorted. First, for the alternate label distortion:

fimsPDOSpot = ModelPDOSpots.computeFIM(sensSoln.sens);
 fimPDOSpots = ModelPDOSpots.evaluateExperiment(fimsPDOSpot,nCellsOpt);
 Model.plotMHResults(mhResults,[FIM,fimOpt,fimPDOSpots]);
 
-% Next, for the intensity integration distortion.
+% Next, for the intensity integration distortion:
 fimsPDOIntens = ModelPDOIntens.computeFIM(sensSoln.sens);
 fimPDOIntens = ModelPDOIntens.evaluateExperiment(fimsPDOIntens,nCellsOpt);
 Model.plotMHResults(mhResults,[FIM,fimOpt,fimPDOSpots,fimPDOIntens]);
 
-% Finally for an extended experiment with a larger number of cells.
+% Finally, for an extended experiment with a larger number of cells;
 fimsPDOIntens = ModelPDOIntens.computeFIM(sensSoln.sens);
 fimPDOIntens2x = ModelPDOIntens.evaluateExperiment(fimsPDOIntens,2.218*nCellsOpt);
 Model.plotMHResults(mhResults,[FIM,fimOpt,fimPDOSpots,fimPDOIntens,fimPDOIntens2x]);
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Contents

example_HybridModel

In this script, we demonstrate how to adjust a model to treat some species (i.e., upstream reactions) using an ODE formulation, while having other species (i.e., downstream species) evolving in a discrete stochastic manner.

close all
 clear all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 

Example 1 - transcription and translation

First create a full model (e.g., for mRNA and protein)

Model1 = SSIT;
 Model1.species = {'rna','protein'};
 Model1.initialCondition = [0;0];
@@ -86,6 +86,7 @@
 Model2.hybridOptions.upstreamODEs = {'rna'};
 [fspSoln2, Model2.fspOptions.bounds] = Model2.solve;
 Model2.makePlot(fspSoln2,'marginals',[],[],3)
+legend('Full','QSSA for (n)','Location','eastoutside')
 

Example 2 - 5-species MAPK induction Model

In this example, we consider a model of MAPK translocation to the nucleus followed by binding to a gene and then transcription activation. We will assume that there are two alleles with one each starting in the active and inactive state.

Model3 = SSIT;
 Model3.species = {'geneInactive','geneActive','mapkCyt','mapkNuc','rna'};
 Model3.initialCondition = [1;1;50;0;0];
@@ -114,14 +115,13 @@
 Model5.hybridOptions.upstreamODEs = {'mapkCyt','mapkNuc'};
 [fspSoln5, Model5.fspOptions.bounds] = Model5.solve;
 Model5.makePlot(fspSoln5,'marginals',[],[],[11,12,15])
-
Warning: Reaction 3 changes both
-ODE and stochastic species.
-Removing effect on upstream
-species. 
-Warning: Reaction 4 changes both
-ODE and stochastic species.
-Removing effect on upstream
-species. 
+legend('Full','QSSA for (gene,MAKP)','QSSA for (MAKP)','Location','eastoutside')
+
Warning: Reaction 4 changes both ODE and stochastic species. Removing effect on upstream species.
+> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('parallel_function>make_general_channel/channel_general', '/Applications/MATLAB_R2023a.app/toolbox/matlab/lang/parallel_function.m', 837)" style="font-weight:bold">parallel_function>make_general_channel/channel_general</a> (<a href="matlab: opentoline('/Applications/MATLAB_R2023a.app/toolbox/matlab/lang/parallel_function.m',837,0)">line 837</a>)
+In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('parallel.internal.parfor.cppRemoteParallelFunction', '/Applications/MATLAB_R2023a.app/toolbox/parallel/cluster/+parallel/+internal/+parfor/cppRemoteParallelFunction.m', 53)" style="font-weight:bold">parallel.internal.parfor.cppRemoteParallelFunction</a> (<a href="matlab: opentoline('/Applications/MATLAB_R2023a.app/toolbox/parallel/cluster/+parallel/+internal/+parfor/cppRemoteParallelFunction.m',53,0)">line 53</a>)
+Warning: Reaction 3 changes both ODE and stochastic species. Removing effect on upstream species.
+> In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('parallel_function>make_general_channel/channel_general', '/Applications/MATLAB_R2023a.app/toolbox/matlab/lang/parallel_function.m', 837)" style="font-weight:bold">parallel_function>make_general_channel/channel_general</a> (<a href="matlab: opentoline('/Applications/MATLAB_R2023a.app/toolbox/matlab/lang/parallel_function.m',837,0)">line 837</a>)
+In <a href="matlab:matlab.internal.language.introspective.errorDocCallback('parallel.internal.parfor.cppRemoteParallelFunction', '/Applications/MATLAB_R2023a.app/toolbox/parallel/cluster/+parallel/+internal/+parfor/cppRemoteParallelFunction.m', 53)" style="font-weight:bold">parallel.internal.parfor.cppRemoteParallelFunction</a> (<a href="matlab: opentoline('/Applications/MATLAB_R2023a.app/toolbox/parallel/cluster/+parallel/+internal/+parfor/cppRemoteParallelFunction.m',53,0)">line 53</a>)
 
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    Contents

    Using the SSIT to fit Multiple Models and Data sets with Shared Parameters

    In this script, we show how multiple SSIT models and data sets can be fit simultaneously. This is most useful in situations where: 1) the analysis considers different experimental conditions (e.g., different time points, different inducer concentrations, different genetic mutations). 2) replica to replica variations are expected that would result in slightly different parameter combinations

    close all
     clear all
    -addpath('../CommandLine');
    +addpath(genpath('../src'));
     

    Define SSIT Model

    SSIT models are defined as usual:

    Model1 = SSIT;
     Model1.species = {'onGene';'rna'};
     Model1.initialCondition = [0;0];
    @@ -87,6 +87,8 @@
     
     % We generate functions for model propensities
     Model1 = Model1.formPropensitiesGeneral('Model1FSP');
    +
    Starting parallel pool (parpool) using the 'Processes' profile ...
    +Connected to parallel pool with 6 workers.
     

    Create Second Model and associate to its own data

    Model2 = Model1;
     Model2 = Model2.loadData('../ExampleData/DUSP1_Dex_100nM_Rep1_Rep2.csv',{'rna','RNA_nuc'},...
         {'Rep_num','2'}); % This would load the data assign onGene and rna and condition on Rep_num = 1;
    @@ -208,7 +210,7 @@
     %   slightly different parameter combinations
     close all
     clear all
    -addpath('../CommandLine');
    +addpath(genpath('../src'));
     
     %% Define SSIT Model
     % SSIT models are defined as usual:
    diff --git a/Examples/html/example_MultiModelTool.png b/Examples/html/example_MultiModelTool.png
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    diff --git a/Examples/html/example_MultiModelTool_02.png b/Examples/html/example_MultiModelTool_02.png
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      Contents

      clear all
       close all
      +addpath(genpath('../src'));
       

      (1) Creating and Modifying SSIT Model

      (1A) Choosing a Pre-Made Model

      ModelChoice = 'BirthDeath';    % One species problem
       % ModelChoice = 'ToggleSwitch';  % Two species problem (non-linear toggle switch)
       % ModelChoice = 'CentralDogmaTV';  % Two species problem (mRNa and protein) with time varying transcription rate
      @@ -84,6 +85,8 @@
       F1.initialTime = -1;
       F1.solutionScheme = 'FSP';    % Set solutions scheme to FSP.
       [FSPsoln,F1.fspOptions.bounds] = F1.solve;  % Solve the FSP analysis
      +
      Starting parallel pool (parpool) using the 'Processes' profile ...
      +Connected to parallel pool with 6 workers.
       

      (2A.1) Solve FSP Model again using the bounds from the last solution

      If we start with the bounds computed in the first analysis, the solution is often much faster.

      [FSPsoln] = F1.solve(FSPsoln.stateSpace);  % Solve the FSP analysis
       

      (2A.2) Make plots of FSP solution

      F1.makePlot(FSPsoln,'meansAndDevs',[],[],1,{'linewidth',3,'color',[0,1,1]}) % Make plot of mean vs. time.
       F1.makePlot(FSPsoln,'marginals',[],[],2,{'linewidth',3,'color',[0,0,1]}) % Make plot of mean vs. time.
      @@ -97,15 +100,14 @@
       

      (3) Sensitivity Analysis using FSP

      F4 = F1;
       F4.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity
       [sensSoln,bounds] = F4.solve(FSPsoln.stateSpace);  % Solve the sensitivity problem
      -
      Error with Analytical Sensitivity Calculations - Switching to Finite Difference Method
       

      (3A) Make plot of sensitivities

      F4.makePlot(sensSoln,'marginals',[],[],4,{'linewidth',3,'color',[0,0,1]}) % Plot marginal sensitivities
       
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z%7gy|gT`QKP@!Mz$$18$0{UcWaq;uRrEE}^KB!%}Uuf{XQ$lC~Z24$;Ryw<$W|c;; zE{8`(0F)073;<=Jg?CTDH-*_o@N(!SO21#odG`)zc9`zQ7RXy?D~4y&s2^v&mO>y> zLfHaWrUw|+{gJOg01^oj-@ZjX-!$cI17|OS_GV*QDfkrt9c<~M<1z!FZ)j#IPepem z-P{6$IFGH*s?4fPl$qI2w;HtT)TpS>My)bEB@M+HCt3kzKw9Hw~%1v*)KO2{{%ugPY-O)qFtX>0+f5qz2)E#{~{R(KLFE3`hg zwY{I)55o}2BZ&mVJkDg}_1^XA>7U!$a(a{9zyymLrep=|z%zd%2e6Sc1*mlY2d@vw zIl+rQmkd3k)+rEffr{EyM7w!R`F=$OC-MyRRHO5tLxHX>;&QDIbe=7EN#{=~I|3w_ zYr5rOH74Yw*M)?>Kr3%c!AKRF#w7Jikg$34>O|Z zL8l%genVo=5)Vdv-P!JXaP-bs`;6#s#U$YqZu~ zn2E`_?+ Date: Tue, 21 Nov 2023 15:02:53 -0700 Subject: [PATCH 13/14] Update FIM calculation example. --- Examples/example_FIMCalculation.m | 27 ++++- Examples/html/example_FIMCalculation.html | 123 +++++++++++++++----- Examples/html/example_FIMCalculation.png | Bin 4550 -> 4633 bytes Examples/html/example_FIMCalculation_01.png | Bin 21038 -> 13721 bytes Examples/html/example_FIMCalculation_02.png | Bin 0 -> 15520 bytes Examples/html/example_FIMCalculation_03.png | Bin 0 -> 16166 bytes Examples/html/example_FIMCalculation_04.png | Bin 0 -> 17069 bytes Examples/html/example_FIMCalculation_05.png | Bin 0 -> 22204 bytes Examples/html/example_FIMCalculation_06.png | Bin 0 -> 24542 bytes Examples/html/example_FIMCalculation_07.png | Bin 0 -> 21041 bytes Examples/html/example_FIMCalculation_08.png | Bin 0 -> 27275 bytes src/CommandLine/SSIT.m | 19 +-- 12 files changed, 129 insertions(+), 40 deletions(-) create mode 100644 Examples/html/example_FIMCalculation_02.png create mode 100644 Examples/html/example_FIMCalculation_03.png create mode 100644 Examples/html/example_FIMCalculation_04.png create mode 100644 Examples/html/example_FIMCalculation_05.png create mode 100644 Examples/html/example_FIMCalculation_06.png create mode 100644 Examples/html/example_FIMCalculation_07.png create mode 100644 Examples/html/example_FIMCalculation_08.png diff --git a/Examples/example_FIMCalculation.m b/Examples/example_FIMCalculation.m index 3fb610a..054ac45 100644 --- a/Examples/example_FIMCalculation.m +++ b/Examples/example_FIMCalculation.m @@ -13,21 +13,35 @@ %% (2) Solve FSP for model F2.solutionScheme = 'FSP'; % Set solution scheme to FSP. [FSPsoln,F2.fspOptions.bounds] = F2.solve; % Solve the FSP analysis +% Plot the results from the FSP analysis +fig1 = figure(1);clf; set(fig1,'Name','Marginal Distributions, mRNA'); +fig2 = figure(2);clf; set(fig2,'Name','Marginal Distributions, Protein'); +F2.makePlot(FSPsoln,'marginals',[1:4:21],false,[fig1,fig2],{'r-','linewidth',2}) %% (3) Solve FSP Sensitivity F2.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity [sensSoln,bounds] = F2.solve(FSPsoln.stateSpace); % Solve the sensitivity problem +% Plot the results from the sensitivity analysis +fig3 = figure(3);clf; set(fig3,'Name','Marginal Sensitivity, mRNA'); +fig4 = figure(4);clf; set(fig4,'Name','Marginal Sensitivity, Protein'); +F2.makePlot(sensSoln,'marginals',[],false,[fig3,fig4],{'b','linewidth',2}) %% (4) Compute FIM using FSP Sensitivity Results fimResults = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion. cellCounts = 10*ones(size(F2.tSpan)); % Number of cells in each experiment. [fimTotal,mleCovEstimate,fimMetrics] = F2.evaluateExperiment(fimResults,cellCounts) +fig5 = figure(5);clf; set(fig5,'Name','Fim-Predicted Uncertainty Ellipses'); +F2.plotMHResults([],fimTotal,'lin',[],fig5) +legend('FIM - Full Observation') %% (5) Compute FIM for Partial Observations F2.pdoOptions.PDO=[]; F2.pdoOptions.unobservedSpecies = 'x1'; [fimResults_partialObs] = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion. [fimTotal_partialObs,mleCovEstimate_partialObs,fimMetrics_partialObs] = F2.evaluateExperiment(fimResults_partialObs,cellCounts) +fig6 = figure(6);clf; set(fig6,'Name','Fim-Predicted Uncertainty Ellipses'); +F2.plotMHResults([],[fimTotal,fimTotal_partialObs],'lin',[],fig6) +legend('FIM - Full Observation','FIM - Protein Only') %% (6) Compute FIM for Distorted Observation (Probabilistic Distortion Operator) F2.pdoOptions.unobservedSpecies = 'x1'; @@ -36,17 +50,20 @@ pdoOptions.props.CaptureProbabilityS1 = 0; % Use zero for unobserved species. pdoOptions.props.CaptureProbabilityS2 = 0.9; -% call method to generate the PDO. +% Call method to generate the PDO. F2.pdoOptions.PDO = F2.generatePDO(pdoOptions,[],FSPsoln.fsp); -% plot the PDO +% Plot the PDO N = size(F2.pdoOptions.PDO.conditionalPmfs{1}); -figure; +fig7 = figure(7); set(fig7,'Name','Probabilistic Distortion Operator for Protein'); contourf([0:N(1)-1],[0:N(2)-1],log10(F2.pdoOptions.PDO.conditionalPmfs{1})); % Here we wanted the first PDO for 'x2' because 'x1' was unobserved. xlabel('Actual');ylabel('Observable');colorbar; -% solve FIM using the specified PDO +% Solve FIM using the specified PDO [fimResults_BinomialPDO] = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion. [fimTotal_BinomialPDO,mleCovEstimate_BinomialPDO,fimMetrics_BinomialPDO] =... - F2.evaluateExperiment(fimResults_BinomialPDO,cellCounts) \ No newline at end of file + F2.evaluateExperiment(fimResults_BinomialPDO,cellCounts) +fig8 = figure(8);clf; set(fig8,'Name','Fim-Predicted Uncertainty Ellipses'); +F2.plotMHResults([],[fimTotal,fimTotal_partialObs,fimTotal_BinomialPDO],'lin',[],fig8) +legend('FIM - Full Observation','FIM - Protein Only','FIM - Protein with Error') diff --git a/Examples/html/example_FIMCalculation.html b/Examples/html/example_FIMCalculation.html index b4ebc4a..a77ed26 100644 --- a/Examples/html/example_FIMCalculation.html +++ b/Examples/html/example_FIMCalculation.html @@ -6,7 +6,7 @@ example_FIMCalculation

      Contents

      fimExample

      In this script, we show how to set up and solve the FSP-FIM matrix with partial observations and probabilistic distortion.

      clear all
       close all
      +addpath(genpath('../src'));
       

      (1) Set up Model

      ModelChoice = 'CentralDogma';  % Two species problem (mRNa and protein)
       F2 = SSIT(ModelChoice);
       F2 = F2.formPropensitiesGeneral('FIMExample');
       

      (2) Solve FSP for model

      F2.solutionScheme = 'FSP';    % Set solution scheme to FSP.
       [FSPsoln,F2.fspOptions.bounds] = F2.solve;  % Solve the FSP analysis
      -

      (3) Solve FSP Sensitivity

      F2.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity
      +% Plot the results from the FSP analysis
      +fig1 = figure(1);clf; set(fig1,'Name','Marginal Distributions, mRNA');
      +fig2 = figure(2);clf; set(fig2,'Name','Marginal Distributions, Protein');
      +F2.makePlot(FSPsoln,'marginals',[1:4:21],false,[fig1,fig2],{'r-','linewidth',2})
      +

      (3) Solve FSP Sensitivity

      F2.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity
       [sensSoln,bounds] = F2.solve(FSPsoln.stateSpace);  % Solve the sensitivity problem
      -
      Error with Analytical Sensitivity Calculations - Switching to Finite Difference Method
      -

      (4) Compute FIM using FSP Sensitivity Results

      fimResults = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
      +% Plot the results from the sensitivity analysis
      +fig3 = figure(3);clf; set(fig3,'Name','Marginal Sensitivity, mRNA');
      +fig4 = figure(4);clf; set(fig4,'Name','Marginal Sensitivity, Protein');
      +F2.makePlot(sensSoln,'marginals',[],false,[fig3,fig4],{'b','linewidth',2})
      +

      (4) Compute FIM using FSP Sensitivity Results

      fimResults = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
       cellCounts = 10*ones(size(F2.tSpan));  % Number of cells in each experiment.
       [fimTotal,mleCovEstimate,fimMetrics] = F2.evaluateExperiment(fimResults,cellCounts)
      -
      fimTotal =
      +fig5 = figure(5);clf; set(fig5,'Name','Fim-Predicted Uncertainty Ellipses');
      +F2.plotMHResults([],fimTotal,'lin',[],fig5)
      +legend('FIM - Full Observation')
      +
      +fimTotal =
      +
         1×1 cell array
      +
           {4×4 double}
      +
      +
       mleCovEstimate =
      +
         1×1 cell array
      +
           {4×4 double}
      +
      +
       fimMetrics = 
      +
         struct with fields:
       
      -          det: 15056909185.6849
      -        trace: 31032.4672821342
      -    minEigVal: 1.01963637716716
      -

      (5) Compute FIM for Partial Observations

      F2.pdoOptions.PDO=[];
      +          det: 15056904911.6332
      +        trace: 31032.4672181693
      +    minEigVal: 1.01963610988029
      +
      +

      (5) Compute FIM for Partial Observations

      F2.pdoOptions.PDO=[];
       F2.pdoOptions.unobservedSpecies = 'x1';
       [fimResults_partialObs] = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
       [fimTotal_partialObs,mleCovEstimate_partialObs,fimMetrics_partialObs] = F2.evaluateExperiment(fimResults_partialObs,cellCounts)
      -
      fimTotal_partialObs =
      +fig6 = figure(6);clf; set(fig6,'Name','Fim-Predicted Uncertainty Ellipses');
      +F2.plotMHResults([],[fimTotal,fimTotal_partialObs],'lin',[],fig6)
      +legend('FIM - Full Observation','FIM - Protein Only')
      +
      +fimTotal_partialObs =
      +
         1×1 cell array
      +
           {4×4 double}
      +
      +
       mleCovEstimate_partialObs =
      +
         1×1 cell array
      +
           {4×4 double}
      +
      +
       fimMetrics_partialObs = 
      +
         struct with fields:
       
      -          det: 15334575.7734958
      -        trace: 28026.2035489583
      -    minEigVal: 0.265831411968639
      -

      (6) Compute FIM for Distorted Observation (Probabilistic Distortion Operator)

      F2.pdoOptions.unobservedSpecies = 'x1';
      +          det: 15334574.1344478
      +        trace: 28026.2035042016
      +    minEigVal: 0.265831373533071
      +
      +

      (6) Compute FIM for Distorted Observation (Probabilistic Distortion Operator)

      F2.pdoOptions.unobservedSpecies = 'x1';
       pdoOptions.type = 'Binomial';
       % Need to define loss parameter for each species S1, S2,...
       pdoOptions.props.CaptureProbabilityS1 = 0;  % Use zero for unobserved species.
       pdoOptions.props.CaptureProbabilityS2 = 0.9;
       
      -% call method to generate the PDO.
      +% Call method to generate the PDO.
       F2.pdoOptions.PDO = F2.generatePDO(pdoOptions,[],FSPsoln.fsp);
       
      -% plot the PDO
      +% Plot the PDO
       N = size(F2.pdoOptions.PDO.conditionalPmfs{1});
      -figure;
      +fig7 = figure(7); set(fig7,'Name','Probabilistic Distortion Operator for Protein');
       contourf([0:N(1)-1],[0:N(2)-1],log10(F2.pdoOptions.PDO.conditionalPmfs{1}));
       % Here we wanted the first PDO for 'x2' because 'x1' was unobserved.
       xlabel('Actual');ylabel('Observable');colorbar;
       
      -% solve FIM using the specified PDO
      +% Solve FIM using the specified PDO
       [fimResults_BinomialPDO] = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
       [fimTotal_BinomialPDO,mleCovEstimate_BinomialPDO,fimMetrics_BinomialPDO] =...
           F2.evaluateExperiment(fimResults_BinomialPDO,cellCounts)
      -
      fimTotal_BinomialPDO =
      +fig8 = figure(8);clf; set(fig8,'Name','Fim-Predicted Uncertainty Ellipses');
      +F2.plotMHResults([],[fimTotal,fimTotal_partialObs,fimTotal_BinomialPDO],'lin',[],fig8)
      +legend('FIM - Full Observation','FIM - Protein Only','FIM - Protein with Error')
      +
      +fimTotal_BinomialPDO =
      +
         1×1 cell array
      +
           {4×4 double}
      +
      +
       mleCovEstimate_BinomialPDO =
      +
         1×1 cell array
      +
           {4×4 double}
      +
      +
       fimMetrics_BinomialPDO = 
      +
         struct with fields:
       
      -          det: 11753388.9719856
      -        trace: 26697.1703866449
      -    minEigVal: 0.240145247197538
      -
      \ No newline at end of file diff --git a/Examples/html/example_FIMCalculation.png b/Examples/html/example_FIMCalculation.png index 91240b2e1e6c70a171f0010dafacda7e9375fe10..bffcd662ac089e10dc710a14889e6bf570f2faec 100644 GIT binary patch literal 4633 zcmV+!66WoRP)eF z76KKJ!cL6<01?eeL_t(&f$dxSa~sEf|L(nj!{I@I0AJvn5-Ez3Og%V~sMxY2yB^nl zB^hU?{h^)lOxpYbZGLTjZf81^P9`&*b~0@;ooPKzDor$7iH01GUGnF0u!;1QX`7-O*BMpac+RrznmSUR1?7&ir)9Aa0Y zikYJzSw(Jvi3~RWwg4gI_xnXr>!~zpyuYs_7770ss&~ zR#sL91_tVxRLYeqt^R!Vv-u^Lf@Ila`I^|CP}AX1Z&YqwABcOA&*ud}2m}JP&R~o& z#w#l;;c!?KgcM1iPfkrHO1L5mOreY-*$M{H(azSf;9ej2C@=t6Sy|CEtu~JL4Dn9( z|6;Ldnr2H&3%~p@_xUFi`B&PGjdTV*Do_fkoc-4AFFoDaKkxmz2qGaQ9*_6-_HL5% z%iO1vA4k4>X2|0Khd{}+XY*?}tFy#gJQW!ncJ~QzPZu+pjG`zt{v3?4g@uLQ{+@R; zS1x|3KNmbS+SStJ6%`361H*!3-ux^czfEtS*&jU}d#0YjE?oQPy-*) zj8Vo?j4d%%s(UxDlYjd3`i~cXGP4_>^OuiU7qN?liEP!7Ci~;bC&gT(4)L*1! z1nUhj#)e@y&N|~(Xx=v55$7#LVyUIu_{ zHd`*25kd^;a;dm45~(%20)PR&Rxb`A#wcZ!&z+Hx5x?J`$z%W^kw{={zZyBY>Ycxo 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.../html/example_Fit_STL1_mRNA_Data_17.png | Bin 20708 -> 20865 bytes .../html/example_Fit_STL1_mRNA_Data_18.png | Bin 18278 -> 18676 bytes .../html/example_Fit_STL1_mRNA_Data_19.png | Bin 14219 -> 13823 bytes .../html/example_Fit_STL1_mRNA_Data_20.png | Bin 28101 -> 32814 bytes .../html/example_Fit_STL1_mRNA_Data_21.png | Bin 30712 -> 32203 bytes .../html/example_Fit_STL1_mRNA_Data_22.png | Bin 28765 -> 32067 bytes .../html/example_Fit_STL1_mRNA_Data_23.png | Bin 24912 -> 24982 bytes .../html/example_Fit_STL1_mRNA_Data_24.png | Bin 29363 -> 29659 bytes .../html/example_Fit_STL1_mRNA_Data_25.png | Bin 21831 -> 22264 bytes .../html/example_Fit_STL1_mRNA_Data_26.png | Bin 14588 -> 14961 bytes .../html/example_Fit_STL1_mRNA_Data_27.png | Bin 30934 -> 35623 bytes .../html/example_Fit_STL1_mRNA_Data_28.png | Bin 30712 -> 32131 bytes .../html/example_Fit_STL1_mRNA_Data_29.png | Bin 28765 -> 32136 bytes .../html/example_Fit_STL1_mRNA_Data_30.png | Bin 24846 -> 24590 bytes .../html/example_Fit_STL1_mRNA_Data_31.png | Bin 29366 -> 29622 bytes .../html/example_Fit_STL1_mRNA_Data_32.png | Bin 44503 -> 45669 bytes .../html/example_Fit_STL1_mRNA_Data_33.png | Bin 10896 -> 10422 bytes 35 files changed, 131 insertions(+), 130 deletions(-) diff --git a/Examples/html/example_Fit_STL1_mRNA_Data.html b/Examples/html/example_Fit_STL1_mRNA_Data.html index fa3c6ae..b4e3323 100644 --- a/Examples/html/example_Fit_STL1_mRNA_Data.html +++ b/Examples/html/example_Fit_STL1_mRNA_Data.html @@ -6,7 +6,7 @@ example_Fit_STL1_mRNA_Data

      Contents

      Example script to show SSIT for the Vanderbilt Q_BIO Group

      In this script, we are going to show how to create, solve and fit a CME model to some single-cell smFISH data. For this example, we will use some data collected in Dr. Gregor Neuert's laboratory at Vanderbilt.

      close all
       clear all
      -addpath('../CommandLine')
      +addpath(genpath('../src'));
       

      Create SSIT Model

      First, we are going to create an FSP model for a bursting gene expression model. This model will consist of 3 species: OFF Gene, ON Gene, and mRNA. There will be four reactions: activation, inactivation, transcription and degradation. The activation rate will be assumed to be time varying and controlled by a MAPK signal.

      Model = SSIT;    % Create SSIT instance and call it 'Model'.
       
       % Set species names for bursting gene expression model:
      @@ -110,7 +110,7 @@
       
       % Set the code to start at steady state at t=0;
       Model.fspOptions.initApproxSS =true;
      -Model = Model.formPropensitiesGeneral('STL1Model');
      +Model = Model.formPropensitiesGeneral('STL1Model',true);
       [FSPsoln,Model.fspOptions.bounds] = Model.solve;  % Solve the FSP analysis
       
       % Next we make plots of the marginal distributions at time points 3, 5, 7,
      @@ -260,37 +260,37 @@
       % again - it is possible you can still find a better model to explain your
       % data.  This can take several rounds of iteration before convergence.  I
       % recommend creating a while loop to make it automated.
      -
      n=0; acc=0.08. TMPmh_24.mat
      -n=100; acc=0.125. TMPmh_24.mat
      -n=200; acc=0.093333. TMPmh_24.mat
      -n=300; acc=0.09. TMPmh_24.mat
      -n=400; acc=0.084. TMPmh_24.mat
      -n=500; acc=0.071667. TMPmh_24.mat
      -n=600; acc=0.077143. TMPmh_24.mat
      -n=700; acc=0.075. TMPmh_24.mat
      -n=800; acc=0.068889. TMPmh_24.mat
      -n=900; acc=0.067. TMPmh_24.mat
      -n=1000; acc=0.063636. TMPmh_24.mat
      -n=1100; acc=0.066667. TMPmh_24.mat
      -n=1200; acc=0.065385. TMPmh_24.mat
      -n=1300; acc=0.065. TMPmh_24.mat
      -n=1400; acc=0.064667. TMPmh_24.mat
      -n=1500; acc=0.066875. TMPmh_24.mat
      -n=1600; acc=0.069412. TMPmh_24.mat
      -n=1700; acc=0.070556. TMPmh_24.mat
      -n=1800; acc=0.071053. TMPmh_24.mat
      -n=1900; acc=0.0715. TMPmh_24.mat
      -n=2000; acc=0.071905. TMPmh_24.mat
      -n=2100; acc=0.075455. TMPmh_24.mat
      -n=2200; acc=0.075217. TMPmh_24.mat
      -n=2300; acc=0.077083. TMPmh_24.mat
      -n=2400; acc=0.0768. TMPmh_24.mat
      -n=2500; acc=0.076154. TMPmh_24.mat
      -n=2600; acc=0.074074. TMPmh_24.mat
      -n=2700; acc=0.073571. TMPmh_24.mat
      -n=2800; acc=0.072759. TMPmh_24.mat
      -n=2900; acc=0.072. TMPmh_24.mat
      -n=3000; acc=0.07129. TMPmh_24.mat
      +
      n=0; acc=0.13. TMPmh_2.mat
      +n=100; acc=0.135. TMPmh_2.mat
      +n=200; acc=0.15. TMPmh_2.mat
      +n=300; acc=0.1375. TMPmh_2.mat
      +n=400; acc=0.128. TMPmh_2.mat
      +n=500; acc=0.12333. TMPmh_2.mat
      +n=600; acc=0.12286. TMPmh_2.mat
      +n=700; acc=0.12625. TMPmh_2.mat
      +n=800; acc=0.12556. TMPmh_2.mat
      +n=900; acc=0.122. TMPmh_2.mat
      +n=1000; acc=0.12. TMPmh_2.mat
      +n=1100; acc=0.12. TMPmh_2.mat
      +n=1200; acc=0.11615. TMPmh_2.mat
      +n=1300; acc=0.11857. TMPmh_2.mat
      +n=1400; acc=0.11467. TMPmh_2.mat
      +n=1500; acc=0.10938. TMPmh_2.mat
      +n=1600; acc=0.10941. TMPmh_2.mat
      +n=1700; acc=0.10722. TMPmh_2.mat
      +n=1800; acc=0.10474. TMPmh_2.mat
      +n=1900; acc=0.1065. TMPmh_2.mat
      +n=2000; acc=0.10476. TMPmh_2.mat
      +n=2100; acc=0.10545. TMPmh_2.mat
      +n=2200; acc=0.10435. TMPmh_2.mat
      +n=2300; acc=0.10208. TMPmh_2.mat
      +n=2400; acc=0.1028. TMPmh_2.mat
      +n=2500; acc=0.10269. TMPmh_2.mat
      +n=2600; acc=0.10111. TMPmh_2.mat
      +n=2700; acc=0.10107. TMPmh_2.mat
      +n=2800; acc=0.098621. TMPmh_2.mat
      +n=2900; acc=0.096667. TMPmh_2.mat
      +n=3000; acc=0.095161. TMPmh_2.mat
       

      Iterating between MLE and MH.

      Let's run a few rounds of MLE and MH to see if we can get better convergence.

      Model.parameters(:,2) = num2cell(pars);
       for i=1:3
           % Maximize likelihood
      @@ -319,109 +319,110 @@
        the current x satisfies the termination criteria using OPTIONS.TolX of 1.000000e-04 
        and F(X) satisfies the convergence criteria using OPTIONS.TolFun of 1.000000e-04 
       
      -n=0; acc=0.31. TMPmh_25.mat
      -n=100; acc=0.395. TMPmh_25.mat
      -n=200; acc=0.39. TMPmh_25.mat
      -n=300; acc=0.39. TMPmh_25.mat
      -n=400; acc=0.4. TMPmh_25.mat
      -n=500; acc=0.42. TMPmh_25.mat
      -n=600; acc=0.42571. TMPmh_25.mat
      -n=700; acc=0.43375. TMPmh_25.mat
      -n=800; acc=0.43333. TMPmh_25.mat
      -n=900; acc=0.435. TMPmh_25.mat
      -n=1000; acc=0.43091. TMPmh_25.mat
      -n=1100; acc=0.42917. TMPmh_25.mat
      -n=1200; acc=0.43308. TMPmh_25.mat
      -n=1300; acc=0.43286. TMPmh_25.mat
      -n=1400; acc=0.426. TMPmh_25.mat
      -n=1500; acc=0.42438. TMPmh_25.mat
      -n=1600; acc=0.42941. TMPmh_25.mat
      -n=1700; acc=0.43111. TMPmh_25.mat
      -n=1800; acc=0.42895. TMPmh_25.mat
      -n=1900; acc=0.427. TMPmh_25.mat
      -n=2000; acc=0.42762. TMPmh_25.mat
      -n=2100; acc=0.42545. TMPmh_25.mat
      -n=2200; acc=0.4287. TMPmh_25.mat
      -n=2300; acc=0.42792. TMPmh_25.mat
      -n=2400; acc=0.4312. TMPmh_25.mat
      -n=2500; acc=0.43077. TMPmh_25.mat
      -n=2600; acc=0.42852. TMPmh_25.mat
      -n=2700; acc=0.42929. TMPmh_25.mat
      -n=2800; acc=0.43138. TMPmh_25.mat
      -n=2900; acc=0.43333. TMPmh_25.mat
      -n=3000; acc=0.43484. TMPmh_25.mat
      +n=0; acc=0.42. TMPmh_3.mat
      +n=100; acc=0.385. TMPmh_3.mat
      +n=200; acc=0.42667. TMPmh_3.mat
      +n=300; acc=0.425. TMPmh_3.mat
      +n=400; acc=0.422. TMPmh_3.mat
      +n=500; acc=0.43167. TMPmh_3.mat
      +n=600; acc=0.43571. TMPmh_3.mat
      +n=700; acc=0.43125. TMPmh_3.mat
      +n=800; acc=0.42778. TMPmh_3.mat
      +n=900; acc=0.422. TMPmh_3.mat
      +n=1000; acc=0.42545. TMPmh_3.mat
      +n=1100; acc=0.42083. TMPmh_3.mat
      +n=1200; acc=0.41692. TMPmh_3.mat
      +n=1300; acc=0.41714. TMPmh_3.mat
      +n=1400; acc=0.41867. TMPmh_3.mat
      +n=1500; acc=0.41625. TMPmh_3.mat
      +n=1600; acc=0.41647. TMPmh_3.mat
      +n=1700; acc=0.41889. TMPmh_3.mat
      +n=1800; acc=0.41789. TMPmh_3.mat
      +n=1900; acc=0.4205. TMPmh_3.mat
      +n=2000; acc=0.42143. TMPmh_3.mat
      +n=2100; acc=0.41591. TMPmh_3.mat
      +n=2200; acc=0.4187. TMPmh_3.mat
      +n=2300; acc=0.42208. TMPmh_3.mat
      +n=2400; acc=0.4216. TMPmh_3.mat
      +n=2500; acc=0.42192. TMPmh_3.mat
      +n=2600; acc=0.42222. TMPmh_3.mat
      +n=2700; acc=0.41929. TMPmh_3.mat
      +n=2800; acc=0.42241. TMPmh_3.mat
      +n=2900; acc=0.42533. TMPmh_3.mat
      +n=3000; acc=0.42742. TMPmh_3.mat
        
       Optimization terminated:
        the current x satisfies the termination criteria using OPTIONS.TolX of 1.000000e-04 
        and F(X) satisfies the convergence criteria using OPTIONS.TolFun of 1.000000e-04 
       
      -n=0; acc=0.33. TMPmh_26.mat
      -n=100; acc=0.375. TMPmh_26.mat
      -n=200; acc=0.39667. TMPmh_26.mat
      -n=300; acc=0.4. TMPmh_26.mat
      -n=400; acc=0.408. TMPmh_26.mat
      -n=500; acc=0.41. TMPmh_26.mat
      -n=600; acc=0.40429. TMPmh_26.mat
      -n=700; acc=0.415. TMPmh_26.mat
      -n=800; acc=0.41889. TMPmh_26.mat
      -n=900; acc=0.425. TMPmh_26.mat
      -n=1000; acc=0.42273. TMPmh_26.mat
      -n=1100; acc=0.4175. TMPmh_26.mat
      -n=1200; acc=0.41385. TMPmh_26.mat
      -n=1300; acc=0.41571. TMPmh_26.mat
      -n=1400; acc=0.41933. TMPmh_26.mat
      -n=1500; acc=0.42562. TMPmh_26.mat
      -n=1600; acc=0.42235. TMPmh_26.mat
      -n=1700; acc=0.42111. TMPmh_26.mat
      -n=1800; acc=0.42211. TMPmh_26.mat
      -n=1900; acc=0.424. TMPmh_26.mat
      -n=2000; acc=0.42286. TMPmh_26.mat
      -n=2100; acc=0.42182. TMPmh_26.mat
      -n=2200; acc=0.42304. TMPmh_26.mat
      -n=2300; acc=0.42125. TMPmh_26.mat
      -n=2400; acc=0.4208. TMPmh_26.mat
      -n=2500; acc=0.42038. TMPmh_26.mat
      -n=2600; acc=0.42333. TMPmh_26.mat
      -n=2700; acc=0.4225. TMPmh_26.mat
      -n=2800; acc=0.42345. TMPmh_26.mat
      -n=2900; acc=0.421. TMPmh_26.mat
      -n=3000; acc=0.41839. TMPmh_26.mat
      +n=0; acc=0.44. TMPmh_4.mat
      +n=100; acc=0.425. TMPmh_4.mat
      +n=200; acc=0.4. TMPmh_4.mat
      +n=300; acc=0.415. TMPmh_4.mat
      +n=400; acc=0.418. TMPmh_4.mat
      +n=500; acc=0.42833. TMPmh_4.mat
      +n=600; acc=0.41143. TMPmh_4.mat
      +n=700; acc=0.39875. TMPmh_4.mat
      +n=800; acc=0.39333. TMPmh_4.mat
      +n=900; acc=0.4. TMPmh_4.mat
      +n=1000; acc=0.41091. TMPmh_4.mat
      +n=1100; acc=0.4125. TMPmh_4.mat
      +n=1200; acc=0.41769. TMPmh_4.mat
      +n=1300; acc=0.42143. TMPmh_4.mat
      +n=1400; acc=0.42267. TMPmh_4.mat
      +n=1500; acc=0.41937. TMPmh_4.mat
      +n=1600; acc=0.42647. TMPmh_4.mat
      +n=1700; acc=0.42667. TMPmh_4.mat
      +n=1800; acc=0.42632. TMPmh_4.mat
      +n=1900; acc=0.428. TMPmh_4.mat
      +n=2000; acc=0.42857. TMPmh_4.mat
      +n=2100; acc=0.42545. TMPmh_4.mat
      +n=2200; acc=0.42522. TMPmh_4.mat
      +n=2300; acc=0.42. TMPmh_4.mat
      +n=2400; acc=0.4244. TMPmh_4.mat
      +n=2500; acc=0.42385. TMPmh_4.mat
      +n=2600; acc=0.42259. TMPmh_4.mat
      +n=2700; acc=0.4225. TMPmh_4.mat
      +n=2800; acc=0.42345. TMPmh_4.mat
      +n=2900; acc=0.42533. TMPmh_4.mat
      +n=3000; acc=0.42613. TMPmh_4.mat
        
       Optimization terminated:
        the current x satisfies the termination criteria using OPTIONS.TolX of 1.000000e-04 
        and F(X) satisfies the convergence criteria using OPTIONS.TolFun of 1.000000e-04 
       
      -n=0; acc=0.44. TMPmh_27.mat
      -n=100; acc=0.415. TMPmh_27.mat
      -n=200; acc=0.43667. TMPmh_27.mat
      -n=300; acc=0.4075. TMPmh_27.mat
      -n=400; acc=0.404. TMPmh_27.mat
      -n=500; acc=0.40167. TMPmh_27.mat
      -n=600; acc=0.40286. TMPmh_27.mat
      -n=700; acc=0.38875. TMPmh_27.mat
      -n=800; acc=0.39222. TMPmh_27.mat
      -n=900; acc=0.398. TMPmh_27.mat
      -n=1000; acc=0.39455. TMPmh_27.mat
      -n=1100; acc=0.39417. TMPmh_27.mat
      -n=1200; acc=0.39385. TMPmh_27.mat
      -n=1300; acc=0.395. TMPmh_27.mat
      -n=1400; acc=0.396. TMPmh_27.mat
      -n=1500; acc=0.39937. TMPmh_27.mat
      -n=1600; acc=0.39412. TMPmh_27.mat
      -n=1700; acc=0.39778. TMPmh_27.mat
      -n=1800; acc=0.40105. TMPmh_27.mat
      -n=1900; acc=0.403. TMPmh_27.mat
      -n=2000; acc=0.41048. TMPmh_27.mat
      -n=2100; acc=0.41045. TMPmh_27.mat
      -n=2200; acc=0.40435. TMPmh_27.mat
      -n=2300; acc=0.40542. TMPmh_27.mat
      -n=2400; acc=0.404. TMPmh_27.mat
      -n=2500; acc=0.40077. TMPmh_27.mat
      -n=2600; acc=0.4037. TMPmh_27.mat
      -n=2700; acc=0.4075. TMPmh_27.mat
      -n=2800; acc=0.40862. TMPmh_27.mat
      -n=2900; acc=0.40967. TMPmh_27.mat
      -n=3000; acc=0.41323. TMPmh_27.mat
      +Parallel pool using the 'Processes' profile is shutting down.
      +n=0; acc=0.42. TMPmh_5.mat
      +n=100; acc=0.41. TMPmh_5.mat
      +n=200; acc=0.44333. TMPmh_5.mat
      +n=300; acc=0.4475. TMPmh_5.mat
      +n=400; acc=0.452. TMPmh_5.mat
      +n=500; acc=0.44. TMPmh_5.mat
      +n=600; acc=0.44143. TMPmh_5.mat
      +n=700; acc=0.4475. TMPmh_5.mat
      +n=800; acc=0.44111. TMPmh_5.mat
      +n=900; acc=0.439. TMPmh_5.mat
      +n=1000; acc=0.43818. TMPmh_5.mat
      +n=1100; acc=0.44. TMPmh_5.mat
      +n=1200; acc=0.43923. TMPmh_5.mat
      +n=1300; acc=0.435. TMPmh_5.mat
      +n=1400; acc=0.43333. TMPmh_5.mat
      +n=1500; acc=0.43875. TMPmh_5.mat
      +n=1600; acc=0.43529. TMPmh_5.mat
      +n=1700; acc=0.43167. TMPmh_5.mat
      +n=1800; acc=0.43421. TMPmh_5.mat
      +n=1900; acc=0.4395. TMPmh_5.mat
      +n=2000; acc=0.43429. TMPmh_5.mat
      +n=2100; acc=0.43409. TMPmh_5.mat
      +n=2200; acc=0.43304. TMPmh_5.mat
      +n=2300; acc=0.43042. TMPmh_5.mat
      +n=2400; acc=0.432. TMPmh_5.mat
      +n=2500; acc=0.43038. TMPmh_5.mat
      +n=2600; acc=0.4337. TMPmh_5.mat
      +n=2700; acc=0.43393. TMPmh_5.mat
      +n=2800; acc=0.43621. TMPmh_5.mat
      +n=2900; acc=0.43467. TMPmh_5.mat
      +n=3000; acc=0.43484. TMPmh_5.mat
       

      Evaluating the MH results

      Here we will generate three plots. The first one will show the Likelihood function as we search over parameter space. In order to get a good estimate of the parameter uncertainty, we want this to quickly reach ts maximum value and then to fluctuate around that value for a significant amount of time. If you see that it is still incereasing, you know that the fit has not yet converged.

      figure
       subplot(1,3,1)
       plot(chainResults.mhValue)
      @@ -469,7 +470,7 @@
       
       Neff =
       
      -           41.266228308708
      +          43.1954111381028
       
       
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Contents

example_ExtrinsicNoiseModels

In this example, we show how to sample an FSM model over intrinsic noise in its various parameters.

close all
 clear all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 

Example 1 - transcription and translation

First create a full model (e.g., for mRNA and protein)

Model1 = SSIT();
 Model1.species = {'rna','protein'};
 Model1.initialCondition = [0;0];
@@ -80,6 +80,7 @@
 Model1.fspOptions.initApproxSS = false;
 Model1.tSpan = linspace(0,5,10);
 [fspSoln1,Model1.fspOptions.bounds] = Model1.solve;
+Model1 = Model1.formPropensitiesGeneral('Model1');
 Model1.makePlot(fspSoln1,'marginals',[],[],[2,3])
 

Generate, solve and plot results for an extrinsic noise version

Specify the rules for the extrinsic noise. This must be a function that returns a vector of parameters, that are in the same order as the parameters provided. You can choose a different distribution for the extrinsic noise in each parameter.

parDistributions = @()[10+2*randn,...
     0.5+0.1*randn,...
@@ -87,6 +88,7 @@
     1+0.2*randn];
 extrinsicModel = extrinsicSSIT(Model1,parDistributions,20);
 Model1.makePlot(extrinsicModel.averagedResults,'marginals',[],[],[2,3])
+legend('Intrinsic Only','Intrinsic + Extrinsic','Location','EastOutside')