thsGen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ~~~~~~~~~~~~~~~~~~~~  0  ~~~~~~~~~~~~~~~~~~~~ \n",
      "(((((((+)))))))......((((((+)))))).......(((((+)))))......(((((((+))))))).......\n",
      "miRNADictVals:  ['UCCUGUACUGAGCUGCCCCGAG', 'AUCCCACCUCUCACCAGC', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "orderedStrands:  ['UCCUGUACUGAGCUGCCCCGAG', 'CUCGGGGAAAAAAUGGGAU', 'AUCCCACCUCUCACCAGC', 'GCUGGAAAAAAUUGCACA', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "strands:  ['CUCGGGGAAAAAAUGGGAU', 'GCUGGAAAAAAUUGCACA', 'UCCUGUACUGAGCUGCCCCGAG', 'AUCCCACCUCUCACCAGC', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "AGGACAUGACUCGACUUUUUUGGAGAGUUUUUUUUAGGUACGUUUUGAGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGUCAAAACGUACCUAAAAAAAACUCUCCAAAAAAAACCUGGCGGCAGCGCAAAAG\n",
      "...............(((((((((((((((((((((((((((((((((...(((((...............)))))...))))))))))))))))))))))))))))))))).....................\n",
      "Toehold binding site exposure percentage:  90.53333333333333 %\n",
      "Start codon exposure percentage:  67.33333333333333 %\n",
      "Ribosome binding site exposure percentage:  100.0 %\n",
      "good\n",
      "Complex results:\n",
      "           Complex       Pfunc dG (kcal/mol) MFE (kcal/mol)\n",
      "0  (toeholdSwitch)  7.4271e+39       -56.582        -55.764\n",
      "\n",
      "MFE proxy structure(s) ths:\n",
      "     0: ...............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....)))..... (-55.76 kcal/mol)\n",
      "max exposure:  90.53333333333333\n",
      "from this switch:  0\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).......((((((+)))))).................\n",
      "1260.0\n",
      " ~~~~~~~~~~~~~~~~~~~~  1  ~~~~~~~~~~~~~~~~~~~~ \n",
      "(((((((+)))))))......(((((((+)))))))........((((((((+))))))))......((((((+))))))........\n",
      "miRNADictVals:  ['UCCUGUACUGAGCUGCCCCGAG', 'UGUGCAAAUCCAUGCAAAACUGA', 'AUCCCACCUCUCACCAGC']\n",
      "orderedStrands:  ['UCCUGUACUGAGCUGCCCCGAG', 'CUCGGGGAAAAAAUUGCACA', 'UGUGCAAAUCCAUGCAAAACUGA', 'UCAGUUUUAAAAAAUGGGAU', 'AUCCCACCUCUCACCAGC']\n",
      "strands:  ['CUCGGGGAAAAAAUUGCACA', 'UCAGUUUUAAAAAAUGGGAU', 'UCCUGUACUGAGCUGCCCCGAG', 'UGUGCAAAUCCAUGCAAAACUGA', 'AUCCCACCUCUCACCAGC']\n",
      "AGGACAUGACUCGACUUUUUUUAGGUACGUUUUUUGGAGAGUGGUCGGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGCGACCACUCUCCAAAAAACGUACCUAAAAAAAGAACCUGGCGGCAGCGCAAAAG\n",
      "..............(((((((((((((((((((((((((((((((((...(((((...............)))))...))))))))))))))))))))))))))))))))).....................\n",
      "Toehold binding site exposure percentage:  89.57142857142857 %\n",
      "Start codon exposure percentage:  65.33333333333333 %\n",
      "Ribosome binding site exposure percentage:  100.0 %\n",
      "good\n",
      "Complex results:\n",
      "           Complex       Pfunc dG (kcal/mol) MFE (kcal/mol)\n",
      "0  (toeholdSwitch)  1.5566e+45       -64.134        -63.518\n",
      "\n",
      "MFE proxy structure(s) ths:\n",
      "     0: ..............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....)))..... (-63.52 kcal/mol)\n",
      "     1: ..............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....)))..... (-63.52 kcal/mol)\n",
      "max exposure:  90.53333333333333\n",
      "from this switch:  0\n",
      "..((......))...(((((((+)))))))......(((((((+)))))))........((((((((+))))))))......((((((+))))))............\n",
      "1260.0\n",
      " ~~~~~~~~~~~~~~~~~~~~  2  ~~~~~~~~~~~~~~~~~~~~ \n",
      "(((((+)))))......(((((((+))))))).......(((((((+)))))))......(((((((+)))))))........\n",
      "miRNADictVals:  ['AUCCCACCUCUCACCAGC', 'UCCUGUACUGAGCUGCCCCGAG', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "orderedStrands:  ['AUCCCACCUCUCACCAGC', 'GCUGGAAAAAAUACAGGA', 'UCCUGUACUGAGCUGCCCCGAG', 'CUCGGGGAAAAAAUUGCACA', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "strands:  ['GCUGGAAAAAAUACAGGA', 'CUCGGGGAAAAAAUUGCACA', 'AUCCCACCUCUCACCAGC', 'UCCUGUACUGAGCUGCCCCGAG', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "UAGGGUGGAGAGUUUUUUUGACUCGACUUUUUUUAGGUACGUUUUGACUGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGAGUCAAAACGUACCUAAAAAAAGUCGAGUCAAAAACCUGGCGGCAGCGCAAAAG\n",
      "................(((((((((((((((((((((((((((((((((...(((((...............)))))...))))))))))))))))))))))))))))))))).....................\n",
      "Toehold binding site exposure percentage:  68.5 %\n",
      "Start codon exposure percentage:  65.99999999999999 %\n",
      "Ribosome binding site exposure percentage:  100.0 %\n",
      "bad\n",
      "Complex results:\n",
      "           Complex       Pfunc dG (kcal/mol) MFE (kcal/mol)\n",
      "0  (toeholdSwitch)  5.5400e+44       -63.497        -62.586\n",
      "\n",
      "MFE proxy structure(s) ths:\n",
      "     0: ..((((.........((((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))))))).(((....)))..... (-62.59 kcal/mol)\n",
      "max exposure:  90.53333333333333\n",
      "from this switch:  0\n",
      ".............(((((+)))))......(((((((+)))))))........(((((((+)))))))......(((((((+))))))).......((....)).\n",
      "1260.0\n",
      " ~~~~~~~~~~~~~~~~~~~~  3  ~~~~~~~~~~~~~~~~~~~~ \n",
      "(((((+)))))......(((((((+))))))).......((((((((+))))))))......(((((((+)))))))........\n",
      "miRNADictVals:  ['AUCCCACCUCUCACCAGC', 'UGUGCAAAUCCAUGCAAAACUGA', 'UCCUGUACUGAGCUGCCCCGAG']\n",
      "orderedStrands:  ['AUCCCACCUCUCACCAGC', 'GCUGGAAAAAAUUGCACA', 'UGUGCAAAUCCAUGCAAAACUGA', 'UCAGUUUUAAAAAAUACAGGA', 'UCCUGUACUGAGCUGCCCCGAG']\n",
      "strands:  ['GCUGGAAAAAAUUGCACA', 'UCAGUUUUAAAAAAUACAGGA', 'AUCCCACCUCUCACCAGC', 'UGUGCAAAUCCAUGCAAAACUGA', 'UCCUGUACUGAGCUGCCCCGAG']\n",
      "UAGGGUGGAGAGUUUUUUUUAGGUACGUUUUUUGACUCGACGGGGCUCGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGGAGCCCCGUCGAGUCAAAAAACGUACCUAAAAAAACCUGGCGGCAGCGCAAAAG\n",
      "...............(((((((((((((((((((((((((((((((((...(((((...............)))))...))))))))))))))))))))))))))))))))).....................\n",
      "Toehold binding site exposure percentage:  67.6 %\n",
      "Start codon exposure percentage:  68.0 %\n",
      "Ribosome binding site exposure percentage:  100.0 %\n",
      "bad\n",
      "Complex results:\n",
      "           Complex       Pfunc dG (kcal/mol) MFE (kcal/mol)\n",
      "0  (toeholdSwitch)  8.7255e+50       -72.292        -70.952\n",
      "\n",
      "MFE proxy structure(s) ths:\n",
      "     0: ..........((.(((((((((((((((((((((((((((((((((((.(.(((((...............))))).).))))))))))))))))))))))))))))))))))).)).(((....)))..... (-70.95 kcal/mol)\n",
      "max exposure:  90.53333333333333\n",
      "from this switch:  0\n",
      ".......(((........+((((((.....(((((((+))))))).)))).))((((((((+))))))))......(((((((+)))))))..)))(......)..\n",
      "1260.0\n",
      " ~~~~~~~~~~~~~~~~~~~~  4  ~~~~~~~~~~~~~~~~~~~~ \n",
      "((((((((+))))))))......(((((((+)))))))........(((((((+)))))))......((((((+))))))........\n",
      "miRNADictVals:  ['UGUGCAAAUCCAUGCAAAACUGA', 'UCCUGUACUGAGCUGCCCCGAG', 'AUCCCACCUCUCACCAGC']\n",
      "orderedStrands:  ['UGUGCAAAUCCAUGCAAAACUGA', 'UCAGUUUUAAAAAAUACAGGA', 'UCCUGUACUGAGCUGCCCCGAG', 'CUCGGGGAAAAAAUGGGAU', 'AUCCCACCUCUCACCAGC']\n",
      "strands:  ['UCAGUUUUAAAAAAUACAGGA', 'CUCGGGGAAAAAAUGGGAU', 'UGUGCAAAUCCAUGCAAAACUGA', 'UCCUGUACUGAGCUGCCCCGAG', 'AUCCCACCUCUCACCAGC']\n",
      "ACACGUUUAGGUACGUUUUUUGACUCGACUUUUUUGGAGAGUGGUCGGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGCGACCACUCUCCAAAAAAGUCGAGUCAAAAAACAACCUGGCGGCAGCGCAAAAG\n",
      "..............(((((((((((((((((((((((((((((((((...(((((...............)))))...))))))))))))))))))))))))))))))))).....................\n",
      "Toehold binding site exposure percentage:  56.00000000000001 %\n",
      "Start codon exposure percentage:  67.33333333333333 %\n",
      "Ribosome binding site exposure percentage:  100.0 %\n",
      "bad\n",
      "Complex results:\n",
      "           Complex       Pfunc dG (kcal/mol) MFE (kcal/mol)\n",
      "0  (toeholdSwitch)  8.3231e+49       -70.843        -70.070\n",
      "\n",
      "MFE proxy structure(s) ths:\n",
      "     0: .......(((((..(((((((((((((((((((((((((((((((((.(.(((((...............))))).).))))))))))))))))))))))))))))))))).)))))(((....)))..... (-70.07 kcal/mol)\n",
      "max exposure:  90.53333333333333\n",
      "from this switch:  0\n",
      "..((((......))))...(((.+.((((((.....(.(((((((+))))))).)))))))(((((((+)))))))......((((((+))))))........))).\n",
      "1260.0\n",
      " ~~~~~~~~~~~~~~~~~~~~  5  ~~~~~~~~~~~~~~~~~~~~ \n",
      "((((((((+))))))))......((((((+)))))).......(((((+)))))......(((((((+))))))).......\n",
      "miRNADictVals:  ['UGUGCAAAUCCAUGCAAAACUGA', 'AUCCCACCUCUCACCAGC', 'UCCUGUACUGAGCUGCCCCGAG']\n",
      "orderedStrands:  ['UGUGCAAAUCCAUGCAAAACUGA', 'UCAGUUUUAAAAAAUGGGAU', 'AUCCCACCUCUCACCAGC', 'GCUGGAAAAAAUACAGGA', 'UCCUGUACUGAGCUGCCCCGAG']\n",
      "strands:  ['UCAGUUUUAAAAAAUGGGAU', 'GCUGGAAAAAAUACAGGA', 'UGUGCAAAUCCAUGCAAAACUGA', 'AUCCCACCUCUCACCAGC', 'UCCUGUACUGAGCUGCCCCGAG']\n",
      "ACACGUUUAGGUACGUUUUUUGGAGAGUUUUUUUGACUCGACGGGGCUGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGAGCCCCGUCGAGUCAAAAAAACUCUCCAAAAAAAACCUGGCGGCAGCGCAAAAG\n",
      "...............(((((((((((((((((((((((((((((((((...(((((...............)))))...))))))))))))))))))))))))))))))))).....................\n",
      "Toehold binding site exposure percentage:  61.199999999999996 %\n",
      "Start codon exposure percentage:  67.33333333333333 %\n",
      "Ribosome binding site exposure percentage:  100.0 %\n",
      "bad\n",
      "Complex results:\n",
      "           Complex       Pfunc dG (kcal/mol) MFE (kcal/mol)\n",
      "0  (toeholdSwitch)  2.3177e+50       -71.475        -70.670\n",
      "\n",
      "MFE proxy structure(s) ths:\n",
      "     0: .......(((((...(((((((((((((((((((((((((((((((((.(.(((((...............))))).).))))))))))))))))))))))))))))))))).)))))(((....)))..... (-70.67 kcal/mol)\n",
      "max exposure:  90.53333333333333\n",
      "from this switch:  0\n",
      "..((((......))))(((((((+)))))))........(((((+)))))........(((((+)))))......(((((((+)))))))...............\n",
      "1200.0\n",
      "done\n",
      "analysing structures...\n",
      "<__main__.ToeholdSwitch object at 0x7f1404381900>\n",
      "analysisList:  [11928.0, 16268.57, 50400.0, 51030.0, 59400.0, 46560.0]\n",
      "best score:  11928.0\n",
      "from this index:  0\n",
      "best strand order is:  ['CUCGGGGAAAAAAUGGGAU', 'GCUGGAAAAAAUUGCACA', 'UCCUGUACUGAGCUGCCCCGAG', 'AUCCCACCUCUCACCAGC', 'UGUGCAAAUCCAUGCAAAACUGA']\n"
     ]
    }
   ],
   "source": [
    "# Dmporting required libraries\n",
    "import string\n",
    "import math as m\n",
    "import itertools as iTools\n",
    "from nupack import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Defining the toehold switch complex analysis model\n",
    "thsModel = Model(material = \"rna\", celsius = 37)\n",
    "\n",
    "# Dictionary input miRNA sequences and names\n",
    "miRNADict = {'asd': 'UCCUGUACUGAGCUGCCCCGAG', 'fgh': 'AUCCCACCUCUCACCAGC', 'jkl': 'UGUGCAAAUCCAUGCAAAACUGA'}\n",
    "# miRNADict = {'asd': 'UCCUGUACUGAGCUGCCCCGAG'}\n",
    "miRNADictVals = []\n",
    "\n",
    "# Dictionary defining complimentary base pairing (no wobble pairing present)\n",
    "baseDict = {\"A\":\"U\", \"C\":\"G\"}\n",
    "\n",
    "# List of bases\n",
    "baseList = [\"A\", \"U\", \"C\", \"G\"]\n",
    "\n",
    "# list of and regions that will be in the centre of and gate strands, these sections will bind to the toehold switch while the rest of the and gate will bind to the 2 miRNA strands it will connect\n",
    "andRegions = (\"AAAAAA\", \"UUUUUU\", \"CCCCCC\", \"GGGGGG\")\n",
    "\n",
    "# List of strands, strands is simply the miRNA strands in the order inputted or the order being tried by the iterate() function followed by the and gates\n",
    "# orderedStrands is the strands list reordered by listAddition() to be passed to NUPACK's functions to form orderd strand complexes\n",
    "strands = []\n",
    "orderedStrands = []\n",
    "\n",
    "#list of domain names for NUPACK\n",
    "domains = list((string.ascii_lowercase))\n",
    "\n",
    "#list of complex.seq\n",
    "seq = []\n",
    "\n",
    "# Number of iterations, incrimented recursively to get best possible ths\n",
    "iterCount = 0\n",
    "\n",
    "# List of toehold switch objects, the class for which is defined below\n",
    "thsObjs = []\n",
    "\n",
    "# Best scores for the miRNA-and gate complex from each iteration \n",
    "minScoreList = []\n",
    "\n",
    "# MiRNA complex structure generated by code\n",
    "complexStructure = []\n",
    "\n",
    "# Class for the toehold switch, one is created each iteration, contains all the important information for the switch, strucEnergy property is an object from NUPACK\n",
    "class ToeholdSwitch:\n",
    "    def __init__(self, strucEnergy, toehold, duplex, sequence, exposure, strandsUsed, triggerStruc, thsSamples, triggerSamples):\n",
    "        self.strucEnergy = strucEnergy\n",
    "        self.toehold = toehold\n",
    "        self.duplex = duplex\n",
    "        self.sequence = sequence\n",
    "        self.exposure = exposure\n",
    "        self.strandsUsed = strandsUsed\n",
    "        self.triggerStruc = triggerStruc\n",
    "        self.thsSamples = thsSamples\n",
    "        self.triggerSamples = triggerSamples\n",
    "\n",
    "    # Display method, never called in the code but useful to quickly see the most important stats of the ths\n",
    "    def display(self):\n",
    "        print(f\"this switch has this structure {self.strucEnergy.structure} ({self.strucEnergy.energy}), this toehold {self.toehold}, this duplex {self.duplex}, this sequence {self.sequence}, and this exposure {self.exposure}, using these strands {self.strandsUsed}, with this trigger structure {self.triggerStruc}\")\n",
    "        \n",
    "# Computes complimentary strand according to base pairing rules\n",
    "def compStrand(rnaStrand):\n",
    "    compliment = \"\"\n",
    "    for i in rnaStrand:\n",
    "        for key, value in baseDict.items():\n",
    "            if i == key:\n",
    "                compliment += value\n",
    "            elif i == value:\n",
    "                compliment += key\n",
    "    return compliment\n",
    "\n",
    "# Severses string\n",
    "def reverse(rnaStrand):\n",
    "  return rnaStrand[::-1]\n",
    "\n",
    "# Splits each miRNAInput into approximate thirds and adds those to the storageArray, these parts are then used to make and gates\n",
    "def splitString(miRNAInput):\n",
    "    storageArray = []\n",
    "    miRNAInput = miRNAInput.replace(\" \",\"\")\n",
    "    n = round(len(miRNAInput)//3)\n",
    "    if (len(miRNAInput) == 18):\n",
    "        part1 = miRNAInput[:n]\n",
    "        part2 = miRNAInput[2*n+1:]\n",
    "        part3 = miRNAInput[n:2*n+1]\n",
    "    else:\n",
    "        part1 = miRNAInput[:n]\n",
    "        part2 = miRNAInput[2*n+1:]\n",
    "        part3 = miRNAInput[n:2*n+1]\n",
    "    storageArray.append(compStrand(part1))\n",
    "    storageArray.append(compStrand(part2))\n",
    "    storageArray.append(part3)\n",
    "    return storageArray\n",
    "\n",
    "# Computes and gate\n",
    "def andGateGen(sequenceDict):\n",
    "   \n",
    "    storageArray = []\n",
    "    miRNAPartArray = []\n",
    "    global unpairedMiddleArray\n",
    "    unpairedMiddleArray = []\n",
    " \n",
    "    # Splits the input strands into the required components for genetic and gate\n",
    "    for element in miRNADictVals:\n",
    "        storageArray = splitString(element)\n",
    "        miRNAPartArray.append(storageArray[0])\n",
    "        miRNAPartArray.append(storageArray[1])\n",
    "        unpairedMiddleArray.append(storageArray[2])\n",
    "\n",
    "    # Sakes sure code runs on the end of 1 strand and the beginning of the next strand so the and gate had ends complimentary to both\n",
    "    del miRNAPartArray[0]\n",
    "    del miRNAPartArray[len(miRNAPartArray)-1]\n",
    "    middleIndex = 0\n",
    "    for element in miRNAPartArray:\n",
    "\n",
    "        if(miRNAPartArray.index(element) % 2==0):\n",
    "            # Index is incrimented later to update the andRegion such that its adjacent base doesn't match itself\n",
    "            index = 0\n",
    "\n",
    "            # This sets the default value of the andRegion to AAAAAA\n",
    "            andRegion = andRegions[index]\n",
    "           \n",
    "           \n",
    "            # These are the parts of the and gates that bind to the toehold switch, specifically those complimentary to the miRNA\n",
    "            global miRNA1, miRNA2\n",
    "\n",
    "            miRNA1 = reverse(miRNAPartArray[miRNAPartArray.index(element)])\n",
    "\n",
    "            miRNA2 = miRNAPartArray[miRNAPartArray.index(element) + 1]  \n",
    "           \n",
    "            # Checking to see if the andRegion borders an identical base and changing it to correct this by incrimenting the index, this would be a problem as the and gate complex may bind offset by 1 base\n",
    "            # This only needs to be done twice as in the \"worst case scenario\", ALL of the below code will run, making the anndRegion different to BOTH of its neighboring bases\n",
    "            if (miRNA1[-1] == andRegion[1]):\n",
    "                index += 1\n",
    "                andRegion = andRegions[index]\n",
    "            if (reverse(miRNA2)[0] == andRegion[1]):\n",
    "                index += 1\n",
    "                andRegion = andRegions[index]\n",
    "            if (reverse(miRNA2)[0] and miRNA1[-1] == andRegion[1]):\n",
    "                index += 1\n",
    "                andRegion = andRegions[index]  \n",
    "\n",
    "            # Defining a repeating unit for the complex, doesn't include the unpaired start of the complex\n",
    "            duplex1 = \"D\" + str(len(miRNA1)) + \"+\"\n",
    "            complexAnd = \"U\" + str(len(andRegion))\n",
    "            duplex2 = \"D\" + str(len(miRNA2)) + \"+\"\n",
    "            unpairedMiddle = \"U\" + str(len(unpairedMiddleArray[middleIndex]))\n",
    "       \n",
    "            repeatUnit = [duplex1, complexAnd, duplex2, unpairedMiddle]      \n",
    "           \n",
    "            andGateDict = {}\n",
    "            andGateArray = []\n",
    "           \n",
    "            # Construcing and gate out of end of 1 miRNA< start of the next one and the AND region in between consisting of 6 repeated bases\n",
    "            andGate = miRNA1 + andRegion + reverse(miRNA2)\n",
    "           \n",
    "            # Strands array contains UNORDERED strands, starting with and gates, there will always be 1 fewer and gates than miRNA\n",
    "            strands.append(andGate)\n",
    "            middleIndex+=1#modified\n",
    "\n",
    "            # Adding the repeat unit to the complexStructure as many times as required\n",
    "            complexStructure.extend(repeatUnit)\n",
    "        else:\n",
    "            continue\n",
    "    if (iterCount > 0):\n",
    "        # This adds the NEW permutation of miRNA to miRNADictVals for the code to run on it, must be done here as opposed to start as start does this automatically but only on the first iteration\n",
    "        strands.extend(miRNADictVals)\n",
    "    \n",
    "    # Mod3 error being corrected by force because I couldn't find another way to fix it :/\n",
    "    if (len(miRNADictVals) == 3 and len(miRNADictVals[1]) == 18):\n",
    "        fix = int(complexStructure[3][-1]) - 1\n",
    "        complexStructure[3] = \"U\" + str(fix)\n",
    "\n",
    "# Ensures correct inputs\n",
    "def checkCorrectCharacters(characterList, userInput:string):\n",
    "    userInput = userInput.replace(\" \",\"\")\n",
    "    for element in userInput:\n",
    "        if(characterList.count(element)==0):\n",
    "            break\n",
    "        else:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "# Combines two lists together into one array so that their constituent variables appear in an alternating sequence (miRNA, AND, miRNA, AND.... miRNA)\n",
    "def listAddition(array1, array2):\n",
    "    # Dummy element makes the length of the list divisible by 2\n",
    "    array1.append(\"DUMMY\")\n",
    "    array2 = [item for sublist in zip(array2, array1) for item in sublist]\n",
    "    del array2[-1]\n",
    "    del array1[-1]\n",
    "   \n",
    "    return array2\n",
    "   \n",
    "\n",
    "def domainOutputs():\n",
    "    seq.clear()\n",
    "    domainCodes = []\n",
    "\n",
    "    for strand, domain in zip(strands, domains):\n",
    "        domainCode = \"domain \" + domain + \" = \" + strand\n",
    "        domainCodes.append(domainCode)\n",
    "\n",
    "    # This takes letters of the alphabet in order until there is 1 for each miRNA and AND gate, this is so that Strand objects can be created to passed to NUPACK's algorithms\n",
    "    global domainsUsed\n",
    "    domainsUsed = domains[0:len(domainCodes)]\n",
    "\n",
    "    #the domainsUsed list is split into 2 halves, one with letters to be assigned to miRNAs and the other to AND gates, for n miRNA there will be (n-1) and gates\n",
    "    mid = int((len(domainsUsed) + 1)/2)\n",
    "    miRNAs = list(domainsUsed[mid-1:])\n",
    "    andGates = list(domainsUsed[0:mid-1])\n",
    "\n",
    "    seq.extend(miRNAs)\n",
    "\n",
    "    seq.extend(listAddition(andGates, seq))\n",
    "    del seq[0:len(andGates)+1]\n",
    "    global computedComplexStructure\n",
    "    computedComplexStructure = complexStructure\n",
    "    DUPlusToDPPlus(computedComplexStructure)\n",
    "\n",
    "def toeholdSwitchGen(numMiRNA):\n",
    "    # Handles generation of toehold switch\n",
    "    # Assigning start and end of toeholdRegion\n",
    "    global orderedStrands\n",
    "    # Ordering strands using listAddition as that is the order in which the formed complex's bases will bind to the switch\n",
    "    orderedStrands = listAddition(strands[0:int((len(strands)-1)/2)], strands[int((len(strands)-1)/2):int(len(strands))])\n",
    "    # print(\" ~~~~~~~~~~~~~~~~~~~~ \", iterCount,\" ~~~~~~~~~~~~~~~~~~~~ \")\n",
    "    print(\"miRNADictVals: \", miRNADictVals)\n",
    "    print(\"orderedStrands: \", orderedStrands)\n",
    "    print(\"strands: \", strands)\n",
    "    # This is the unpaired region of the toehold switch where the miRNA complex binds\n",
    "    toehold = round((len(miRNADictVals[0])//2))\n",
    "   \n",
    "    # This is the unpaired part of the toehold switch the miRNA, AND GATE complex would first bind to before the switch unzips\n",
    "    toeholdRegion = []\n",
    "    \n",
    "    unpairedStartLength = len(miRNADictVals[0]) - round((len(miRNADictVals[0])//3))\n",
    "   \n",
    "    if(numMiRNA !=1):\n",
    "        toeholdStart = miRNADictVals[0][0:unpairedStartLength]\n",
    "        toeholdEnd = miRNADictVals[-1][len(miRNA2):]\n",
    "        thsAnds = []\n",
    "        thsUnpaired = []\n",
    "       \n",
    "        for i,strand in enumerate(orderedStrands):\n",
    "            # Determining AND gates in the strands list, now ordered with listAddition, then determining what AND sections are used, eg AAAAAA, GGGGGG, UUUUUU, CCCCCC and adding them to another list\n",
    "            if (i % 2 == 1):\n",
    "                for j in andRegions:\n",
    "                    if (j in strand):\n",
    "                        thsAnd = j\n",
    "                        thsAnds.append(thsAnd)\n",
    "                        continue\n",
    "           \n",
    "            # If it's not an and gate, it must be and miRNA, a distinction is made between first and final miRNA in ordered strand complexes as they will have different amount of bases paired with the switch when compared to middle miRNA\n",
    "            else:\n",
    "                if (i == 0):\n",
    "                    # First miRNA\n",
    "                    for j in andRegions:\n",
    "                        if j in (orderedStrands[i+1]):\n",
    "                            # Computes the part of the miRNA binding to the switch by finding the and region within the miRNA's corresponding andgate, then taking the corresponding number of bases from the miRNA\n",
    "                            # u variables are components/parts of the toehold switch's toehold region which will be stuck together in the correct order to be complimentary to the unpaired bases of the and gate complex\n",
    "                            u = strand[0:len(strand) - orderedStrands[i+1].find(j)]\n",
    "                            thsUnpaired.append(u)\n",
    "                        else:\n",
    "                            continue\n",
    "                elif (i == len(orderedStrands) - 1):\n",
    "                    # Final miRNA\n",
    "                    for j in andRegions:\n",
    "                        if j in (orderedStrands[i-1]):\n",
    "                            u = strand[reverse(orderedStrands[i-1]).find(j):len(orderedStrands[i-1])+3]\n",
    "                            thsUnpaired.append(u)\n",
    "                        else:\n",
    "                            continue\n",
    "                else:\n",
    "                    # Middle miRNA\n",
    "                    for j in andRegions:\n",
    "                        if j in (orderedStrands[i-1]):\n",
    "                            u1 = reverse(orderedStrands[i-1]).find(j)\n",
    "                        else:\n",
    "                            continue\n",
    "                           \n",
    "                    for j in andRegions:\n",
    "                        if j in (orderedStrands[i+1]):\n",
    "                            u2 = orderedStrands[i+1].find(j)\n",
    "                        else:\n",
    "                            continue\n",
    "                           \n",
    "                    u3 = strand[u1:-u2]\n",
    "\n",
    "                    thsUnpaired.append(u3)\n",
    "       \n",
    "        # Reorders the ang regions used and miRNA binding regions as per listAddition            \n",
    "        toeholdRegion = (listAddition(thsAnds, thsUnpaired))\n",
    "       \n",
    "        toeholdRegion = \"\".join(toeholdRegion)\n",
    "       \n",
    "        # length of the unpaired binding region of the ths\n",
    "        toehold = 15\n",
    "        thsDuplex = len(toeholdRegion) - toehold\n",
    "       \n",
    "        # thsDuplex is the closed/zipped part of the switch\n",
    "       \n",
    "        # VV IMPORTANT, MAKES THE TOEHOLD DUPLEX REGION A MULTIPULE OF 3 SO THAT ANY RNA AFTER IT CODES FOR THEIR INDENDED CODONS AND AMINO ACIDS, eg linker region and reporter protein\n",
    "        if (thsDuplex % 3 == 1):\n",
    "            thsDuplex = thsDuplex - 1\n",
    "            toehold +=1\n",
    "        elif (thsDuplex % 3 ==2):\n",
    "            thsDuplex +=1\n",
    "            toehold = toehold -1\n",
    "           \n",
    "\n",
    "    # Sequence of the toehold switch, mostly constructed using the computed toeholdRegion and the RBS and linker region used by CLS's Project Ribotox and First used by Dr A.Green\n",
    "    toeholdSwitchSequence = (compStrand(toeholdRegion) + \"GGAUUUGCAAAAAAAAGAGGAGAGUAAAAUG\" + reverse(toeholdRegion)[0:thsDuplex] + \"AACCUGGCGGCAGCGCAAAAG\")\n",
    "    print(toeholdSwitchSequence)\n",
    "   \n",
    "    #the entire switch structure in DU+ including the hairpin loop\n",
    "    thsStructure = (\"U\"+ str(toehold) + \"D\"+ str(thsDuplex) + \"(U3D5(U15)U3)U21\")\n",
    "    thsStrucDPP = \".\"*int(toehold) + \"(\"*int(thsDuplex) + \"...(((((...............)))))...\" + \")\"*int(thsDuplex) + \".\"*21\n",
    "    print(str(thsStrucDPP))\n",
    "   \n",
    "    bindingSites = \"\"\n",
    "    AUGs = \"\"\n",
    "    RBSs = \"\"\n",
    "   \n",
    "    # Creating a strand object for the switch so tube analysis and boltzmann sampling can be performed\n",
    "    thsStrand = Strand(str(toeholdSwitchSequence), name=\"toeholdSwitch\")\n",
    "   \n",
    "    # Taking boltzmann samples to calculate RBS, AUG and toehold site exposure percentages\n",
    "    thsStrandSamples = sample(strands = [thsStrand], num_sample = 50, model = thsModel)\n",
    "       \n",
    "    for struc in list(thsStrandSamples):       \n",
    "        # Computing where RBS, AUG and toehold are located\n",
    "        bindingSites += (str(struc)[0:int(toehold)])\n",
    "        AUGs += (str(struc)[(int(toehold) + int(thsDuplex) + 28):(int(toehold) + int(thsDuplex) + 31)])\n",
    "        RBSs += (str(struc)[(int(toehold) + int(thsDuplex) + 8):(int(toehold) + int(thsDuplex) + 23)])\n",
    "   \n",
    "    # Calculating the unbound percentages as an average of all such values of each boltzmann sampled structure\n",
    "    global toeholdExpMin\n",
    "    toeholdExpMin = 70\n",
    "    global toeholdExp\n",
    "    toeholdExp = (1 - (bindingSites.count(\"(\") + bindingSites.count(\")\"))/len(bindingSites))*100\n",
    "    AUGExp = (1 - (AUGs.count(\"(\") + AUGs.count(\")\"))/len(AUGs))*100\n",
    "    RBSExp = (1 - (RBSs.count(\"(\") + RBSs.count(\")\"))/len(RBSs))*100\n",
    "    print(\"Toehold binding site exposure percentage: \", toeholdExp, \"%\")\n",
    "    print(\"Start codon exposure percentage: \", AUGExp, \"%\")\n",
    "    print(\"Ribosome binding site exposure percentage: \", RBSExp, \"%\")\n",
    "    if (toeholdExp < toeholdExpMin):\n",
    "        print(\"bad\")\n",
    "    else:\n",
    "        print(\"good\")\n",
    "\n",
    "    # Performing tube analysis of ths strand to get an MFE structure, NOT ALWAYS REPRESENTATIVE OF RBS, AUG AND TOEHOLD UNBOUND PERCENTAGES\n",
    "    t2 = Tube(strands={thsStrand:1e-5}, complexes=SetSpec(max_size=1), name=\"thsTube\")\n",
    "    t2Result = tube_analysis(tubes=[t2], compute=[\"pairs\", \"mfe\"], model = thsModel)\n",
    "    print(t2Result)\n",
    "    thsMFE = t2Result[\"(toeholdSwitch)\"]\n",
    "    print('\\nMFE proxy structure(s) ths:')\n",
    "    for i, s in enumerate(thsMFE.mfe):\n",
    "        print('    %2d: %s (%.2f kcal/mol)' % (i, s.structure, s.energy))\n",
    "        sampleDPP = str(s.structure)\n",
    "     \n",
    "    # Adding the toehold switch and its stats for THIS iteration to thsObjs list to be analysed\n",
    "    thsObjs.append(ToeholdSwitch(thsMFE.mfe[0], toeholdRegion, thsDuplex, toeholdSwitchSequence, toeholdExp, list(strands), str(), list(thsStrandSamples)[0:10], list()))\n",
    "\n",
    "    print(\"max exposure: \", max([x.exposure for x in thsObjs]))\n",
    "    print(\"from this switch: \", [x.exposure for x in thsObjs].index(max([x.exposure for x in thsObjs])))\n",
    "    global maxExpIdx\n",
    "    \n",
    "    # Finding the best toehold switch within thsObjs\n",
    "    maxExpIdx = [x.exposure for x in thsObjs].index(max([x.exposure for x in thsObjs]))\n",
    "   \n",
    "# comverts DU+ to dot parens plus to be passed to structDiff\n",
    "def DUPlusToDPPlus(computedComplexStructure):\n",
    "    print(\" ~~~~~~~~~~~~~~~~~~~~ \", iterCount,\" ~~~~~~~~~~~~~~~~~~~~ \")\n",
    "    global DPPlus\n",
    "    DPPlus = \"\"\n",
    "    for i in computedComplexStructure:\n",
    "        if(i[0] == \"U\"):\n",
    "            num = int(i[1:len(i)])\n",
    "            DPPlus += \".\"*num\n",
    "            # print(DPPlus)\n",
    "        else:\n",
    "            num = int(i[1:-1])\n",
    "            DPPlus += \"(\"*num\n",
    "            DPPlus += \"+\"\n",
    "            DPPlus += \")\"*num\n",
    "    print(DPPlus)\n",
    "           \n",
    "# I'm aware nupack has their own version of this function, but it only operates on strand objects, this just works on strings, file containing this is the analysis file in src [I think]\n",
    "def structDiff(countList, struct, DPPlus):\n",
    "    counter = 0\n",
    "    for a, b in zip(struct, DPPlus):\n",
    "        if a != b:\n",
    "            counter += 1\n",
    "   \n",
    "    countList.append(counter)\n",
    "    return counter\n",
    "\n",
    "def tubeDef(numMiRNA):\n",
    "    countList = []\n",
    "   \n",
    "    # Attribute dict is a dictionary pairing each strand to a corresponding letter, these are for analysing the MFE of complexes\n",
    "    attributeDict = {}\n",
    "\n",
    "    # Strand dict is the dictionary pairing each strand object to a corresponding concentration in M to then be passed onto a tube\n",
    "    strandDict = {}\n",
    "\n",
    "    # Passing attribute dict to the Strand class' object constructor and outputting that to a strand dict\n",
    "    for strand, letter in zip(strands, domainsUsed):\n",
    "        attributeDict[strand] = letter\n",
    "        name = letter\n",
    "        strandDict[Strand(strand, name = name)] = 5e-6\n",
    "\n",
    "    # Passing contructed strand objects to a test tube\n",
    "    t1 = Tube(strands = strandDict, complexes = SetSpec(max_size = len(strands)), name = \"tube 1\")\n",
    "   \n",
    "    if(numMiRNA != 1):\n",
    "        global sampledStructures\n",
    "        sampledStructures = sample(strands = list(listAddition(strands[0:int((len(strands)-1)/2)], strands[int((len(strands)-1)/2):int(len(strands))])), num_sample = 50, model = thsModel)\n",
    "   \n",
    "    for i in range(len(sampledStructures)):\n",
    "        structDiff(countList, str(sampledStructures[i]), DPPlus)\n",
    "        \n",
    "    # Analyses each sampled structure and adds weights to it depending o how unfavourable it is/how far away it is from the desired complex\n",
    "    weightList = []\n",
    "    for i in range(len(sampledStructures)):\n",
    "        weightList.append(10)\n",
    "   \n",
    "    for i in range(len(sampledStructures)):\n",
    "        indices = [[i-1, i+1] for i, x in enumerate(str(sampledStructures[i])) if x == \"+\"]\n",
    "        if (str(sampledStructures[i]).startswith(\"(\") or str(sampledStructures[i]).endswith(\")\")):\n",
    "                weightList[i] += 1\n",
    "        for j in indices:              \n",
    "            if ((str(sampledStructures[i]))[j[0]] == \"(\" and (str(sampledStructures[i]))[j[-1]] == \")\"):\n",
    "                weightList[i] += 2.5\n",
    "            else:\n",
    "                weightList[i] += 5\n",
    "   \n",
    "    # Final score list multiplies the structure difference and weighting of each complex to achieve a final, more accurate score\n",
    "    scoreList = []\n",
    "\n",
    "    for a, b in zip(weightList, countList):\n",
    "        scoreList.append(a*b)\n",
    "\n",
    "    # Getting 10 samples for the trigger structure to be passed to a toehold switch object\n",
    "    triggerSampleList = []\n",
    "    orderedScoreList = sorted(scoreList)\n",
    "    for i in orderedScoreList[:10]:\n",
    "        triggerSampleList.append(str(sampledStructures[scoreList.index(i)]))\n",
    "    thsObjs[-1].triggerSamples = list(triggerSampleList)\n",
    "    \n",
    "    # Getting the best structure and it's score/index\n",
    "    global bestStruc\n",
    "    bestStruc = str(sampledStructures[scoreList.index(min(scoreList))])\n",
    "    print(bestStruc)\n",
    "    thsObjs[-1].triggerStruc = str(bestStruc)\n",
    "    global lowScore\n",
    "    lowScore = min(scoreList)\n",
    "    minScoreList.append(lowScore)\n",
    "    print(min(minScoreList))\n",
    "       \n",
    "# Analysis to be performed on all computed toehold switches\n",
    "def analysis():\n",
    "    print(\"analysing structures...\")\n",
    "    # Numberical rating for each toehyold switch, takes into account the toehold switch unbind %s and the trigger structure's score\n",
    "    analysisList = []\n",
    "   \n",
    "    # making sure no values in either of the lists are 0 as that will make the resulting value (a product of list values from both lists) 0, anomalously low\n",
    "    for a,b in zip([(100 - x.exposure) for x in thsObjs], minScoreList):\n",
    "        if (a == 0):\n",
    "            a +=1\n",
    "       \n",
    "        if (b == 0):\n",
    "            b +=1\n",
    "   \n",
    "        analysisList.append(round(a*b, 2))\n",
    "       \n",
    "    global bestThs\n",
    "    bestThs = thsObjs[analysisList.index(min(analysisList))]\n",
    "    print(bestThs)\n",
    "   \n",
    "    print(\"analysisList: \", analysisList)\n",
    "    print(\"best score: \", str(min(analysisList)))\n",
    "    print(\"from this index: \", str(analysisList.index(min(analysisList))))\n",
    "    print(\"best strand order is: \", str(bestThs.strandsUsed))\n",
    "   \n",
    "    global strands\n",
    "    strands.clear()\n",
    "    strands = bestThs.strandsUsed\n",
    "   \n",
    "    seq.clear()\n",
    "    domainCodes = []\n",
    "\n",
    "    for strand, domain in zip(list(bestThs.strandsUsed), domains):\n",
    "        domainCode = \"domain \" + domain + \" = \" + strand\n",
    "        domainCodes.append(domainCode)\n",
    "       \n",
    "    # Creating a strand for the best ths and a complex to see the structure for the opened ths, miRNA and and gates not used here as the computation time is extremely long, especially for 3 miRNA\n",
    "    bestThsStrand = Strand(str(bestThs.sequence), name = \"ths\")\n",
    "    triggerBindingSite = Strand(reverse(str(bestThs.toehold)), name = \"trigger\")\n",
    "    thsComplex = Complex([triggerBindingSite, bestThsStrand])\n",
    "    complexSet = ComplexSet(strands={bestThsStrand: 1e-8, triggerBindingSite: 1e-8}, complexes=SetSpec(max_size=0, include=[thsComplex]))\n",
    "    gap = 1.0\n",
    "    global finalResult\n",
    "    finalResult = complex_analysis(complexSet, compute=['pfunc', 'pairs', 'mfe', 'subopt'], options={'energy_gap': gap}, model=thsModel)\n",
    "    global finalComplex\n",
    "    finalComplex = finalResult[thsComplex]\n",
    "   \n",
    "# This function is called after every toehold switch and miRNA complex is formed, it reorders the miRNA into a different permutation and re-runs the code, then analyses all the switches generated to produce the best one\n",
    "def iterate():\n",
    "    global miRNADictVals\n",
    "    global iterCount\n",
    "    if (iterCount < m.factorial(len(miRNADictVals)) - 1):\n",
    "        iterCount += 1\n",
    "        strands.clear()\n",
    "        orderedStrands.clear()\n",
    "        miRNADictVals.clear()\n",
    "        seq.clear()\n",
    "        complexStructure.clear()\n",
    "        for i in list(miRNAPerms[iterCount]):\n",
    "            miRNADictVals.append(i)\n",
    "            # print(strands)\n",
    "        andGateGen(miRNADictVals)\n",
    "        domainOutputs()\n",
    "        toeholdSwitchGen(numMiRNA)\n",
    "        tubeDef(numMiRNA)\n",
    "        iterate()\n",
    "    else:\n",
    "        print(\"done\")\n",
    "        analysis()\n",
    "       \n",
    "# Start function takes a dict of {miRNA name: miRNA sequence}, it also handles all the computation for 1 strand for miRNA since there is barely any code required to do so, no and gates present\n",
    "def start():  \n",
    "    # print(\"Input the number of miRNA strands\")\n",
    "    global numMiRNA\n",
    "    numMiRNA = len(miRNADict)\n",
    "    if (numMiRNA == 1):\n",
    "        print(compStrand(list(miRNADict.values())[0]))\n",
    "        \n",
    "        miRNA = list(miRNADict.values())[0]\n",
    "        \n",
    "        ths = compStrand(miRNA) + \"GGAUUUGCAAAAAAAAGAGGAGAGUAAAAUG\" + reverse(miRNA)[0:(len(miRNA) - 6)] + \"AACCUGGCGGCAGCGCAAAAG\"\n",
    "        print(\"miRNA: \", miRNA)\n",
    "        print(ths)\n",
    "        thsStrand = Strand(ths, name=\"ths\")\n",
    "        miRNAStrand = Strand(miRNA, name=\"miRNA\")\n",
    "\n",
    "        tube = Tube(strands = {thsStrand:1e-5}, complexes=SetSpec(max_size = 1), name=\"thsTube\")\n",
    "        tubeResult = tube_analysis(tubes = [tube], compute = [\"pairs\", \"mfe\"], model=thsModel)\n",
    "        thsSamples = sample(strands = [thsStrand], num_sample = 10, model = thsModel)\n",
    "        \n",
    "        print(tubeResult)\n",
    "        thsMFE = tubeResult[\"(ths)\"]\n",
    "        print(thsMFE.mfe[0])\n",
    "        print(thsMFE.mfe[0].structure)\n",
    "        print(thsMFE.mfe[0].energy)\n",
    "        print(str(thsMFE.mfe[0].structure)[0:6])\n",
    "        thsExposure = (int(str(thsMFE.mfe[0].structure)[0:6].count(\".\"))/6)*100\n",
    "        print(thsExposure)\n",
    "        print(thsSamples)\n",
    "                        \n",
    "        thsComplex = Complex([miRNAStrand, thsStrand])\n",
    "        complexSet = ComplexSet(strands={thsStrand: 1e-8, miRNAStrand: 1e-8}, complexes=SetSpec(max_size=0, include=[thsComplex]))\n",
    "        global finalResult \n",
    "        gap = 1.0\n",
    "        finalResult = complex_analysis(complexSet, compute = [\"pfunc\", \"pairs\", \"mfe\", \"subopt\"], options={\"energy_gap\": gap}, model=thsModel)\n",
    "        global finalComplex\n",
    "        finalComplex = finalResult[thsComplex]\n",
    "        \n",
    "\n",
    "        global bestThs\n",
    "        bestThs = ToeholdSwitch(thsMFE.mfe[0], str(), str(), ths, thsExposure, [miRNA], str(), thsSamples, str())\n",
    "    else:\n",
    "        miRNADictVals.extend(list(miRNADict.values()))\n",
    "        andGateGen(miRNADictVals)\n",
    "        domainOutputs()\n",
    "\n",
    "    # Adds the base sequences of each miRNA and AND gate to strands to be used later\n",
    "    for value in miRNADict.values():\n",
    "        strands.append(value)\n",
    "\n",
    "    if(numMiRNA != 1):\n",
    "        global miRNAPerms\n",
    "        miRNAPerms = list(iTools.permutations(miRNADictVals))\n",
    "        toeholdSwitchGen(numMiRNA)\n",
    "        tubeDef(numMiRNA)\n",
    "        iterate()\n",
    "start()\n",
    "\n",
    "# This function is just to get the name of the miRNA given a sequence\n",
    "def getKey(val):\n",
    "    for key, value in miRNADict.items():\n",
    "        if val == value:\n",
    "            return key\n",
    "\n",
    "# The 3 functions below are just used in jupyter lab to display the switches and graphs     \n",
    "    \n",
    "def thsDisplay():\n",
    "    print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n",
    "    print(\"toehold switch sequence and structure: \")\n",
    "    print(bestThs.sequence)\n",
    "    print(str(bestThs.strucEnergy.structure), \" Free energy: \", str(round(bestThs.strucEnergy.energy, 2)), \" (kcal/mol)\")\n",
    "    print(\"with these other possible structures\")\n",
    "    for x in bestThs.thsSamples:\n",
    "        print(str(x))\n",
    "    print(\"and an overall toehold region binding site exposure of: \", str(round(bestThs.exposure, 2)), \"%\")\n",
    "    print(\"equilibrium probability pair matrix for toehold switch: \")\n",
    "    thsPairs = pairs(strands=[str(bestThs.sequence)], model=thsModel)\n",
    "    plt.imshow(thsPairs.to_array())\n",
    "    plt.xlabel(\"Base index\")\n",
    "    plt.ylabel(\"Base index\")\n",
    "    plt.title(\"Pair probabilities for toehold switch\")\n",
    "    plt.colorbar()\n",
    "    plt.clim(0, 1)\n",
    "    plt.savefig('ths.png')    \n",
    "\n",
    "def triggerDisplay():\n",
    "    print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n",
    "    bestThsOrderedStrands = listAddition(bestThs.strandsUsed[0:int((len(strands)-1)/2)], strands[int((len(strands)-1)/2):int(len(strands))])\n",
    "    triggerMFE = mfe(strands=bestThsOrderedStrands, model=thsModel)\n",
    "    triggerMFEStruc = triggerMFE[0]\n",
    "    print(\"trigger complex strand order and structure: \")\n",
    "    print(bestThsOrderedStrands)\n",
    "    print(\"By considering Boltzmann samples: \", bestThs.triggerStruc)\n",
    "    print(\"By considering MFE structure: \", str(triggerMFEStruc.structure), \" Free energy: \", str(round(triggerMFEStruc.energy, 2)), \" (kcal/nol)\")\n",
    "    print(\"with these other possible structures: \")\n",
    "    for x in bestThs.triggerSamples:\n",
    "        print(x)\n",
    "    for i, j in enumerate(bestThsOrderedStrands):\n",
    "        if (i % 2 == 1):\n",
    "            print(j, \" is the and gate between \", getKey(bestThsOrderedStrands[i-1]), \" (\", bestThsOrderedStrands[i-1], \") \", \" and \", getKey(bestThsOrderedStrands[i+1]), \" (\", bestThsOrderedStrands[i+1], \") \")\n",
    "    triggerPairs = pairs(strands=bestThsOrderedStrands, model=thsModel)\n",
    "    print(\"equilibrium probability pair matrix for trigger complex: \")\n",
    "    plt.imshow(triggerPairs.to_array())\n",
    "    plt.xlabel(\"Base index\")\n",
    "    plt.ylabel(\"Base index\")\n",
    "    plt.title(\"Pair probabilities for trigger complex MFE\")\n",
    "    print(\"*MFE structure may not be fully representative of the trigger complex's structure, which is why Boltzmann samples are taken. If the MFE structure closely resembles the boltzmann sampled ones, it is extremely likely to form, if not it will still be possible although at lower concentrations\")\n",
    "    print(\"*Boltzmann samples will be more reliable and representative of in vitro reactions as hundreds of samples are taken, MFE structure is only taken into account to give an approximate free energy value and equilibrium pair probability matrix\")\n",
    "    plt.colorbar()\n",
    "    plt.clim(0, 1)\n",
    "    plt.savefig('trigger.png')    \n",
    "\n",
    "def complexDisplay(): \n",
    "    print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n",
    "    print(\"actiavted toehold switch structure: \")\n",
    "    print(str(finalComplex.mfe[0].structure), \" Free energy: \", str(round(finalComplex.mfe[0].energy, 2)), \" (kcal/mol)\")\n",
    "    print(\"with these other suboptimal energy structures: \")\n",
    "    for x in finalComplex.subopt[:10]:\n",
    "        print(x.structure, \" Free energy: \", str(round(x.energy, 2)), \" (kcal/mol)\")\n",
    "    bindingSites = \"\"\n",
    "    for x in finalComplex.subopt:\n",
    "        bindingSite = str(x.structure)[:int(str(x.structure).find(\"+\"))]\n",
    "        bindingSites += bindingSite\n",
    "    print(\"with an activation percentage of: \", str(round(100-bindingSites.count(\".\")*100/len(bindingSites), 2)), \"%\")\n",
    "    print(\"equilibrium probability pair matrix for activated toehold switch: \")\n",
    "    plt.imshow(finalComplex.pairs.to_array())\n",
    "    plt.xlabel(\"Base index\")\n",
    "    plt.ylabel(\"Base index\")\n",
    "    plt.title(\"Pair probabilities for activated toehold switch\")\n",
    "    plt.colorbar()\n",
    "    plt.clim(0, 1)\n",
    "    plt.savefig('complex.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "toehold switch sequence and structure: \n",
      "AGGACAUGACUCGACUUUUUUGGAGAGUUUUUUUUAGGUACGUUUUGAGGAUUUGCAAAAAAAAGAGGAGAGUAAAAUGUCAAAACGUACCUAAAAAAAACUCUCCAAAAAAAACCUGGCGGCAGCGCAAAAG\n",
      "...............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....))).....  Free energy:  -55.76  (kcal/mol)\n",
      "with these other possible structures\n",
      ".......(...)...(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....))).....\n",
      "..((......))...((((((((((((((((((((((((((((.((((.(.(((((...............))))).).)))).))))))))))))))))))))))))))))......(((....))).....\n",
      "...............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....))).....\n",
      "...............(((((.(((((((((((((((((((((((((((.(.(((((...............))))).).))))))))))))))))))))))))))).)))))......(((....))).....\n",
      "............(..(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))..)...(((....))).....\n",
      "...............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....))).....\n",
      ".((......).)...(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....))).....\n",
      "................((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))).......(((....))).....\n",
      "...............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))......(((....))).....\n",
      "...............(((((((((((((((((((((((((((((((((.(.(((((...............))))).).)))))))))))))))))))))))))))))))))....(.(((....)))..)..\n",
      "and an overall toehold region binding site exposure of:  90.53 %\n",
      "equilibrium probability pair matrix for toehold switch: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thsDisplay()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "trigger complex strand order and structure: \n",
      "['UCCUGUACUGAGCUGCCCCGAG', 'CUCGGGGAAAAAAUGGGAU', 'AUCCCACCUCUCACCAGC', 'GCUGGAAAAAAUUGCACA', 'UGUGCAAAUCCAUGCAAAACUGA']\n",
      "By considering Boltzmann samples:  ...............(((((((+)))))))......((((((+)))))).......(((((+))))).......((((((+)))))).................\n",
      "By considering MFE structure:  ...............(((((((+)))))))......((((((+)))))).......(((((+)))))......((((((.+.))))))................  Free energy:  -50.92  (kcal/nol)\n",
      "with these other possible structures: \n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).......((((((+)))))).................\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).....((((((((+))))).)))..............\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).....((((((((+))))).)))..............\n",
      "..((......))...(((((((+)))))))......((((((+)))))).......(((((+)))))......((((((.+.))))))................\n",
      "..((......))...(((((((+)))))))......((((((+)))))).......(((((+)))))......((((((.+.))))))................\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).......(((((.+.))))).................\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).......(((((.+.))))).................\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).......(((((.+.))))).................\n",
      "...............(((((((+)))))))......((((((+)))))).......(((((+))))).......(((((.+.))))).................\n",
      "((.......))....(((((((+)))))))...(..(((((.+.)))))..)....(((((+)))))......(((((((+)))))))................\n",
      "CUCGGGGAAAAAAUGGGAU  is the and gate between  asd  ( UCCUGUACUGAGCUGCCCCGAG )   and  fgh  ( AUCCCACCUCUCACCAGC ) \n",
      "GCUGGAAAAAAUUGCACA  is the and gate between  fgh  ( AUCCCACCUCUCACCAGC )   and  jkl  ( UGUGCAAAUCCAUGCAAAACUGA ) \n",
      "equilibrium probability pair matrix for trigger complex: \n",
      "*MFE structure may not be fully representative of the trigger complex's structure, which is why Boltzmann samples are taken. If the MFE structure closely resembles the boltzmann sampled ones, it is extremely likely to form, if not it will still be possible although at lower concentrations\n",
      "*Boltzmann samples will be more reliable and representative of in vitro reactions as hundreds of samples are taken, MFE structure is only taken into account to give an approximate free energy value and equilibrium pair probability matrix\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "triggerDisplay()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "actiavted toehold switch structure: \n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "with these other suboptimal energy structures: \n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "((((((((((((((((((((((((((((((((((((((((((((((((+))))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "(((((((((((((((((((((((((((((((((((((((((((((((.+.)))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "(((((((((((((((((((((((((((((((((((((((((((((((.+.)))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "(((((((((((((((((((((((((((((((((((((((((((((((.+.)))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "(((((((((((((((((((((((((((((((((((((((((((((((.+.)))))))))))))))))))))))))))))))))))))))))))))))..................(((((((..........................)))))))............(((....))).....  Free energy:  -97.61  (kcal/mol)\n",
      "with an activation percentage of:  98.88 %\n",
      "equilibrium probability pair matrix for activated toehold switch: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "complexDisplay()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "print(\"done\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}