neurospora_no_regulation-supplementary.ipynb

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    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Boltzmann-Simulation\" data-toc-modified-id=\"Boltzmann-Simulation-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Boltzmann Simulation</a></div><div class=\"lev2 toc-item\"><a href=\"#Pentose-phosphate-+-Glycolysis-+-TCA-cycle\" data-toc-modified-id=\"Pentose-phosphate-+-Glycolysis-+-TCA-cycle-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Pentose phosphate + Glycolysis + TCA cycle</a></div><div class=\"lev1 toc-item\"><a href=\"#Models-and-Simulations\" data-toc-modified-id=\"Models-and-Simulations-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Models and Simulations</a></div><div class=\"lev2 toc-item\"><a href=\"#Model-Without-Regulation\" data-toc-modified-id=\"Model-Without-Regulation-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Model Without Regulation</a></div><div class=\"lev3 toc-item\"><a href=\"#Run-Deterministic-Simulation\" data-toc-modified-id=\"Run-Deterministic-Simulation-211\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Run Deterministic Simulation</a></div><div class=\"lev3 toc-item\"><a href=\"#Derivatives-from-ODE-thermodynamic-optimization-simulation\" data-toc-modified-id=\"Derivatives-from-ODE-thermodynamic-optimization-simulation-212\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Derivatives from ODE thermodynamic optimization simulation</a></div><div class=\"lev3 toc-item\"><a href=\"#Thermodynamic-Odds-of-Reaction-from-Deterministic-Simulation\" data-toc-modified-id=\"Thermodynamic-Odds-of-Reaction-from-Deterministic-Simulation-213\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>Thermodynamic Odds of Reaction from Deterministic Simulation</a></div><div class=\"lev3 toc-item\"><a href=\"#Calculate-Net-Odds\" data-toc-modified-id=\"Calculate-Net-Odds-214\"><span class=\"toc-item-num\">2.1.4&nbsp;&nbsp;</span>Calculate Net Odds</a></div><div class=\"lev3 toc-item\"><a href=\"#Map-Net-Likelihoods-onto-Metabolic-Pathways\" data-toc-modified-id=\"Map-Net-Likelihoods-onto-Metabolic-Pathways-215\"><span class=\"toc-item-num\">2.1.5&nbsp;&nbsp;</span>Map Net Likelihoods onto Metabolic Pathways</a></div><div class=\"lev3 toc-item\"><a href=\"#Find-Active-and-Variable-Metabolites\" data-toc-modified-id=\"Find-Active-and-Variable-Metabolites-216\"><span class=\"toc-item-num\">2.1.6&nbsp;&nbsp;</span>Find Active and Variable Metabolites</a></div><div class=\"lev3 toc-item\"><a href=\"#Time-Derivatives-of-Metabolite-Concentration:-S-cdot-Net-Odds-=-flux\" data-toc-modified-id=\"Time-Derivatives-of-Metabolite-Concentration:-S-cdot-Net-Odds-=-flux-217\"><span class=\"toc-item-num\">2.1.7&nbsp;&nbsp;</span>Time Derivatives of Metabolite Concentration: S $\\cdot$ Net Odds = flux</a></div><div class=\"lev3 toc-item\"><a href=\"#Analyze-Variable-Metabolite-Levels-from-Deterministic-Simulation\" data-toc-modified-id=\"Analyze-Variable-Metabolite-Levels-from-Deterministic-Simulation-218\"><span class=\"toc-item-num\">2.1.8&nbsp;&nbsp;</span>Analyze Variable Metabolite Levels from Deterministic Simulation</a></div><div class=\"lev2 toc-item\"><a href=\"#Infer-Regulated-Reactions\" data-toc-modified-id=\"Infer-Regulated-Reactions-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Infer Regulated Reactions</a></div>"
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import subprocess\n",
    "import re\n",
    "import os\n",
    "from IPython.core.display import display\n",
    "pd.set_option('display.max_columns', None,'display.max_rows', None)\n",
    "#from ipynb_latex_setup import *\n",
    "%matplotlib inline\n",
    "#%matplotlib pdf\n",
    "#matplotlib.matplotlib_fname()\n",
    "#import matplotlib.rcsetup as rcsetup\n",
    "#print(rcsetup.all_backends)\n",
    "T = 298.15\n",
    "R = 8.314e-03\n",
    "RT = R*T\n",
    "N_avogadro = 6.022140857e+23\n",
    "VolCell = 1.0e-15\n",
    "Concentration2Count = N_avogadro * VolCell\n",
    "concentration_increment = 1/(N_avogadro*VolCell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change directories to where the simulation data is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comment/uncomment to change to your directory, if needed:\n",
    "cwd = os.getcwd()\n",
    "mydir = cwd+'/simulation_data'\n",
    "if 'simulation_data' not in mydir: \n",
    "  os.chdir(mydir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boltzmann Simulation\n",
    "A beta version of the simulation code is available on GitHub at https://github.com/PNNL-CompBio/Boltzmann.\n",
    "## Pentose phosphate + Glycolysis + TCA cycle\n",
    "Model input file (Reactions):"
   ]
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     "text": [
      "REACTION ME1m\r\n",
      "LEFT\t(S)-MALATE + NAD+ \r\n",
      "RIGHT\tpyruvate + NADH + CO2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "//\r\n",
      "REACTION ME2m\r\n",
      "LEFT    (S)-MALATE + NADP+\r\n",
      "RIGHT   PYRUVATE + NADPH + CO2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "//\r\n",
      "REACTION CSm\r\n",
      "LEFT\tOXALOACETATE + ACETYL-COA + H2O\r\n",
      "RIGHT\tCITRATE + COA\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M \r\n",
      "//\r\n",
      "REACTION ACONTm\r\n",
      "LEFT\tCITRATE\r\n",
      "RIGHT\tISOCITRATE\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\t PH = 7.0, IONIC STRENGTH = 0.15 M \r\n",
      "COMMENT DOn't split into two reactions if max entropy is being used - only one enzyme needs to be epressed\r\n",
      "//\r\n",
      "REACTION ICDHxm\r\n",
      "LEFT\tISOCITRATE + NAD+\r\n",
      "RIGHT\t2-OXOGLUTARATE + NADH + CO2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "//\r\n",
      "REACTION AKGDm\r\n",
      "LEFT\t2-OXOGLUTARATE + COA + NAD+\r\n",
      "RIGHT\tSUCCINYL-COA + CO2 + NADH\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT PH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "//\r\n",
      "REACTION SUCOASm\r\n",
      "LEFT \tSUCCINYL-COA + ADP + Orthophosphate\r\n",
      "RIGHT\tSUCCINATE + ATP + COA\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "//\r\n",
      "REACTION SUCD1m\r\n",
      "LEFT\tSUCCINATE + redox1\r\n",
      "RIGHT\tFUMARATE + redox2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "//\r\n",
      "REACTION FUMm\r\n",
      "LEFT\tFUMARATE + H2O\r\n",
      "RIGHT\t(S)-MALATE \r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "//\r\n",
      "REACTION  MDHm\r\n",
      "LEFT\t(S)-MALATE + NAD+\r\n",
      "RIGHT\tOXALOACETATE + NADH\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "//       \r\n",
      "REACTION GAPD  \r\n",
      "LEFT\tD-GLYCERALDEHYDE-3-PHOSPHATE + ORTHOPHOSPHATE + NAD+\r\n",
      "RIGHT\t3-Phospho-D-glyceroyl_phosphate + NADH\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "PATHWAY GLUCONEOGENESIS, GLYCOLYSIS\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "REACTION PGK\r\n",
      "LEFT\t3-Phospho-D-glyceroyl_phosphate + ADP\r\n",
      "RIGHT\t3-PHOSPHO-D-GLYCERATE + ATP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY\tGLUCONEOGENESIS, GLYCOLYSIS, CALVIN CYCLE\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "REACTION TPI\r\n",
      "LEFT\tGlycerone_phosphate\r\n",
      "RIGHT\tD-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "COMMENT From Equilibrator\r\n",
      "PATHWAY CALVIN CYCLE, GLYCOLYSIS\r\n",
      "//\r\n",
      "REACTION  MDH\r\n",
      "LEFT\t(S)-MALATE + NAD+\r\n",
      "RIGHT\tOXALOACETATE + NADH\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "COMMENT\tPH = 7.0, IONIC STRENGTH = 0.15 M\r\n",
      "COMMENT\tCytosolic\r\n",
      "//\r\n",
      "REACTION PEP_Carboxylase\r\n",
      "LEFT    oxaloacetate + orthophosphate\r\n",
      "RIGHT   phosphoenolpyruvate + CO2 + H2O\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL    0.0\r\n",
      "PATHWAY GLUCONEOGENSESIS\r\n",
      "COMMENTS\r\n",
      "//\r\n",
      "REACTION PPCK\r\n",
      "LEFT\toxaloacetate + ATP \r\n",
      "RIGHT\tphosphoenolpyruvate + ADP + CO2\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tGLUCONEOGENSESIS\r\n",
      "COMMENTS  \r\n",
      "//\r\n",
      "REACTION FBA\r\n",
      "LEFT\tD-FRUCTOSE_1,6-BISPHOSPHATE\r\n",
      "RIGHT\tGlycerone_phosphate + D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY\tGLUCONEOGENSESIS, GLYCOLYSIS, CALVIN CYCLE\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "REACTION FBP\r\n",
      "LEFT\tD-FRUCTOSE_6-PHOSPHATE + Orthophosphate\r\n",
      "RIGHT\tH2O + D-FRUCTOSE_1,6-BISPHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tGLUCONEOGENESIS, CALVIN CYCLE\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "REACTION TKT2\r\n",
      "LEFT\tD-FRUCTOSE_6-PHOSPHATE + D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "RIGHT\tD-ERYTHROSE-4-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tCALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "COMMENT   From Equilibrator pH 7.5, IS 0.15\r\n",
      "//\r\n",
      "REACTION RPE\r\n",
      "LEFT\tD-XYLULOSE-5-PHOSPHATE\r\n",
      "RIGHT\tD-RIBULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tCALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "COMMENT  From Dennis\r\n",
      "//\r\n",
      "REACTION Xylulokinase\r\n",
      "LEFT\tD-XYLULOSE + ATP\r\n",
      "RIGHT\tD-XYLULOSE-5-PHOSPHATE + ADP \r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tCALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "COMMENT  \r\n",
      "//\r\n",
      "REACTION PYK_org\r\n",
      "LEFT\tADP + PHOSPHOENOLPYRUVATE\r\n",
      "RIGHT\tPYRUVATE + ATP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY GLYCOLYSIS, PYRUVATE METABOLISM, RUBISCO SHUNT\r\n",
      "COMMENT  From Dennis\r\n",
      "//\r\n",
      "REACTION PYK\r\n",
      "LEFT\tADP + PHOSPHOENOLPYRUVATE\r\n",
      "RIGHT\tPYRUVATE + ATP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY GLYCOLYSIS, PYRUVATE METABOLISM, RUBISCO SHUNT\r\n",
      "COMMENT  From Dennis\r\n",
      "//\r\n",
      "REACTION RPI\r\n",
      "LEFT\tD-RIBOSE-5-PHOSPHATE\r\n",
      "RIGHT\tD-RIBULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tCALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "COMMENT  From Dennis\r\n",
      "//\r\n",
      "REACTION TKT1\r\n",
      "LEFT\tSEDOHEPTULOSE_7-PHOSPHATE + D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "RIGHT\tD-RIBOSE-5-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY\tCALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "COMMENT  From Equilibrator pH 7.5, IS 0.15\r\n",
      "//\r\n",
      "REACTION TALA\r\n",
      "LEFT\tD-GLYCERALDEHYDE-3-PHOSPHATE + SEDOHEPTULOSE_7-PHOSPHATE\r\n",
      "RIGHT\tD-FRUCTOSE_6-PHOSPHATE + D-ERYTHROSE-4-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY  RUBISCO SHUNT, PENTOSE PHOSPHATE PATHWAY\r\n",
      "COMMENT  From Equilibrator pH 7.5 IS 0.15\r\n",
      "//\r\n",
      "REACTION PGM\r\n",
      "LEFT\t3-PHOSPHO-D-GLYCERATE\r\n",
      "RIGHT\t2-PHOSPHO-D-GLYCERATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY  GLUCONEOGENESIS, RUBISCO SHUNT, GLYCOLYSIS I\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "REACTION  ENO\r\n",
      "LEFT\t2-PHOSPHO-D-GLYCERATE\r\n",
      "RIGHT\tPHOSPHOENOLPYRUVATE + H2O\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY  GLUCONEOGENESIS, RUBISCO SHUNT, GLYCOLYSIS I\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "# Pentose Phosphate reactions (oxidative branch):\r\n",
      "REACTION GND\r\n",
      "LEFT  NADPH + D-RIBULOSE-5-PHOSPHATE + CO2\r\n",
      "RIGHT NADP+ + 6-PHOSPHO-D-GLUCONATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "PATHWAY PENTOSE PHOSPHATE PATHWAY\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "//\r\n",
      "REACTION  PGL\r\n",
      "LEFT  6-PHOSPHO-D-GLUCONATE\r\n",
      "RIGHT D-Glucono-1,5-lactone_6-phosphate + H2O\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "COMMENT From Dennis\r\n",
      "PATHWAY PENTOSE PHOSPHATE PATHWAY\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "//\r\n",
      "REACTION HEX1\r\n",
      "LEFT\tBETA-D-GLUCOSE + ATP\r\n",
      "RIGHT\tBETA-D-GLUCOSE-6-PHOSPHATE + ADP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "NREGULATION ATP 2.80e-03 10\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY\tGLUCONEOGENESIS, (GLYCOLYSIS-YEAST), GLYCOLYSIS\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "REACTION PGI\r\n",
      "LEFT\tBETA-D-GLUCOSE-6-PHOSPHATE\r\n",
      "RIGHT\tD-FRUCTOSE_6-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY\tGLUCONEOGENESIS, (GLYCOLYSIS-YEAST), GLYCOLYSIS\r\n",
      "COMMENTS  From Dennis\r\n",
      "//\r\n",
      "# Gluconeogenesis reactions :\r\n",
      "REACTION  G6PDH2r\r\n",
      "LEFT\tBETA-D-GLUCOSE-6-PHOSPHATE + NADP+\r\n",
      "RIGHT\tD-Glucono-1,5-lactone_6-phosphate + NADPH\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "COMMENT  From Dennis\r\n",
      "PATHWAY GLUCONEOGENESIS\r\n",
      "//\r\n",
      "# Glycolysis reactions :\r\n",
      "REACTION  PFK\r\n",
      "LEFT \tD-FRUCTOSE_6-PHOSPHATE + ATP\r\n",
      "RIGHT\tADP + D-FRUCTOSE_1,6-BISPHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "NREGULATION ATP 2.80e-03 10\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "PATHWAY GLYCOLYSIS\r\n",
      "COMMNENT From Equilibrator\r\n",
      "//\r\n",
      "REACTION PYRt2m\r\n",
      "LEFT\tPYRUVATE\r\n",
      "RIGHT\tPYRUVATE\r\n",
      "DGZERO\t-5.94\r\n",
      "COMMENT -5.94\r\n",
      "DGZERO-UNITS\tKJ/MOL\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "COMMENT PYRUVATE TRANSPORT INTO MITOCHONDRIA - Assuming free energy difference = \\Delta -RT \\Delta pH = -RT log 10\r\n",
      "//\r\n",
      "# Acetyl CoA synthesis\r\n",
      "REACTION PDHm\r\n",
      "LEFT\tCOA + NAD+ + PYRUVATE\r\n",
      "RIGHT\tACETYL-COA + CO2 + NADH\r\n",
      "LEFT_COMPARTMENT  MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL\t1.0\r\n",
      "NREGULATION ACETYL-COA 1.00e-03 20\r\n",
      "PATHWAY ACETYL COA BIOSYNTHESIS\r\n",
      "COMMENT PYRUVATE DEHYDROGENASE SUMMARY REACTION\r\n",
      "//\r\n",
      "REACTION ICL\r\n",
      "LEFT isocitrate\r\n",
      "RIGHT\tglyoxylate + succinate\r\n",
      "LEFT_COMPARTMENT GLYOXYSOME\r\n",
      "RIGHT_COMPARTMENT GLYOXYSOME\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY glyoxylate shunt\r\n",
      "COMMENT \r\n",
      "//\r\n",
      "REACTION MAS\r\n",
      "LEFT acetyl-CoA + glyoxylate + H2O  \r\n",
      "RIGHT\t(S)-MALATE + COA\r\n",
      "LEFT_COMPARTMENT GLYOXYSOME\r\n",
      "RIGHT_COMPARTMENT GLYOXYSOME\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY glyoxylate shunt\r\n",
      "COMMENT \r\n",
      "//\r\n",
      "REACTION PYRDC\r\n",
      "LEFT pyruvate \r\n",
      "RIGHT acetaldehyde + CO2\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY fermentation\r\n",
      "COMMENT \r\n",
      "//\r\n",
      "REACTION ALDD2y\r\n",
      "LEFT acetaldehyde + NADP+ + H2O\r\n",
      "RIGHT acetate + NADPH\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY fermentation\r\n",
      "COMMENT \r\n",
      "//\r\n",
      "REACTION ALCD2X_copy1\r\n",
      "LEFT acetaldehyde + NADH \r\n",
      "RIGHT ethanol + NAD+\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY fermentation\r\n",
      "COMMENT \r\n",
      "//\r\n",
      "REACTION LactateDehydrogenase\r\n",
      "LEFT pyruvate + NADH\r\n",
      "RIGHT lactate + NAD+\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL\t0.0\r\n",
      "PATHWAY fermentation\r\n",
      "COMMENT \r\n",
      "//\r\n"
     ]
    }
   ],
   "source": [
    "cat neurospora_pentose_phos.glycolysis.tca.2.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boltzmann Input File:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RXN_FILE neurospora_pentose_phos.glycolysis.tca.2.dat\r\n",
      "INIT_FILE neurospora_pentose_phos.glycolysis.tca.2_reg.rstrt\r\n",
      "INIT_FILE yeast_centralMetab_concs2.in \r\n",
      "INIT_FILE neurospora_pentose_phos.glycolysis.tca_concs.in\r\n",
      "INIT_FILE neurospora_pentose_phos.glycolysis.tca.2_reg.rstrt\r\n",
      "LOG_FILE neurospora_pentose_phos.glycolysis.tca.2_reg.log\r\n",
      "OUT_FILE neurospora_pentose_phos.glycolysis.tca.2_reg.out\r\n",
      "USE_DEQ 1\r\n",
      "NO_ROUND_FROM_DEQ 1\r\n",
      "ODE_T_FINAL 100000\r\n",
      "DELTA_CONCS_CHOICE 13\r\n",
      "DELTA_CONCS_CHOICE 8\r\n",
      "DERIV_THRESH  5.0e-17\r\n",
      "ODE_RXN_VIEW_FREQ 1\r\n",
      "WARMUP_STEPS 00000000\r\n",
      "RECORD_STEPS 000000\r\n",
      "TEMP_KELVIN 298.15\r\n",
      "PH 7.0\r\n",
      "IONIC_STRENGTH 0.15\r\n",
      "PRINT_OUTPUT 2\r\n",
      "CONCS_OR_COUNTS 3\r\n",
      "RXN_VIEW_FREQ 100\r\n",
      "COUNT_VIEW_FREQ 100\r\n",
      "LKLHD_VIEW_FREQ 100\r\n",
      "USE_BULK_WATER 1\r\n",
      "USE_REGULATION 1\r\n",
      "USE_ENZYME_LEVELS 1\r\n",
      "USE_PSEUDOISOMERS 1\r\n",
      "USE_DGZERO 1\r\n",
      "USE_ACTIVITIES 1\r\n",
      "NUM_METABOLIC_GROUPS 1\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "cat neurospora_pentose_phos.glycolysis.tca_reg.in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models and Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Without Regulation\n",
    "### Run Deterministic Simulation\n",
    "Uncomment the lines below to run Boltzmann from the Jupyter notebook. We prefer not to run from the notebook because it may take several runs before the system converges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#args = (\"boltzmann\", \"neurospora_pentose_phos.glycolysis.tca_no_reg.in\")\n",
    "#popen = subprocess.Popen(args, stdout=subprocess.PIPE)\n",
    "#popen.wait()\n",
    "#output = popen.stdout.read()\n",
    "#print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Derivatives from ODE thermodynamic optimization simulation\n",
    "For the maximum entropy optimization without any regulation, multiple rounds of optimization were used to obtain sufficiently converged derivatives. The maximum entropy distribution has a wide range of metabolite concentrations, from $10^{-22}$ for oxaloacetate to $10^{6}M$ for fructose-1,6-bisphosphate and $10^{9}M$ for acetyl-CoA. Despite these large differences, derivatives remain stable until the the derivatives for acetyl-CoA go below 0.05. At this point the solver becomes unstable because even a small time step results in a large change the derivative. However, the derivatives are sufficiently converged at this point that the loss function can be applied to the metabolite levels to infer reactions that should be regulated. To put this another way, it does not matter for the purpose of infering regulation if the acetyl-CoA concentraion is $1.0\\cdot10^{9}$ or $6.0\\cdot10^{9}$, both are well beyond the expected millimolar level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(S)-MALATE:CYTOSOL                           0.000000e+00\n",
       "NADP+:CYTOSOL                                0.000000e+00\n",
       "NADPH:CYTOSOL                                0.000000e+00\n",
       "ORTHOPHOSPHATE:CYTOSOL                       0.000000e+00\n",
       "SEDOHEPTULOSE_7-PHOSPHATE:CYTOSOL            0.000000e+00\n",
       "(S)-MALATE:GLYOXYSOME                        0.000000e+00\n",
       "ACETYL-COA:GLYOXYSOME                        0.000000e+00\n",
       "COA:GLYOXYSOME                               0.000000e+00\n",
       "GLYOXYLATE:GLYOXYSOME                        0.000000e+00\n",
       "ISOCITRATE:GLYOXYSOME                        0.000000e+00\n",
       "SUCCINATE:GLYOXYSOME                         0.000000e+00\n",
       "ADP:MITOCHONDRIA                             0.000000e+00\n",
       "ATP:MITOCHONDRIA                             0.000000e+00\n",
       "CO2:MITOCHONDRIA                             0.000000e+00\n",
       "COA:MITOCHONDRIA                             0.000000e+00\n",
       "NAD+:MITOCHONDRIA                            0.000000e+00\n",
       "NADH:MITOCHONDRIA                            0.000000e+00\n",
       "NADP+:MITOCHONDRIA                           0.000000e+00\n",
       "NADPH:MITOCHONDRIA                           0.000000e+00\n",
       "ORTHOPHOSPHATE:MITOCHONDRIA                  0.000000e+00\n",
       "REDOX1:MITOCHONDRIA                          0.000000e+00\n",
       "REDOX2:MITOCHONDRIA                          0.000000e+00\n",
       "NADH:CYTOSOL                                 0.000000e+00\n",
       "NAD+:CYTOSOL                                 0.000000e+00\n",
       "OXALOACETATE:CYTOSOL                         0.000000e+00\n",
       "CO2:CYTOSOL                                  0.000000e+00\n",
       "LACTATE:CYTOSOL                              0.000000e+00\n",
       "D-ERYTHROSE-4-PHOSPHATE:CYTOSOL              0.000000e+00\n",
       "ADP:CYTOSOL                                  0.000000e+00\n",
       "D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL    0.000000e+00\n",
       "ATP:CYTOSOL                                  0.000000e+00\n",
       "D-RIBOSE-5-PHOSPHATE:CYTOSOL                 0.000000e+00\n",
       "ACETATE:CYTOSOL                              0.000000e+00\n",
       "ACETALDEHYDE:CYTOSOL                         0.000000e+00\n",
       "6-PHOSPHO-D-GLUCONATE:CYTOSOL                0.000000e+00\n",
       "D-XYLULOSE:CYTOSOL                           0.000000e+00\n",
       "D-XYLULOSE-5-PHOSPHATE:CYTOSOL               0.000000e+00\n",
       "ETHANOL:CYTOSOL                              0.000000e+00\n",
       "D-RIBULOSE-5-PHOSPHATE:CYTOSOL               0.000000e+00\n",
       "BETA-D-GLUCOSE:CYTOSOL                       0.000000e+00\n",
       "ISOCITRATE:MITOCHONDRIA                      8.881784e-16\n",
       "(S)-MALATE:MITOCHONDRIA                      8.881784e-16\n",
       "2-OXOGLUTARATE:MITOCHONDRIA                  8.881784e-16\n",
       "FUMARATE:MITOCHONDRIA                        8.881784e-16\n",
       "OXALOACETATE:MITOCHONDRIA                    2.664535e-15\n",
       "SUCCINYL-COA:MITOCHONDRIA                    2.664535e-15\n",
       "SUCCINATE:MITOCHONDRIA                       2.664535e-15\n",
       "CITRATE:MITOCHONDRIA                         2.664535e-15\n",
       "D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL         9.769963e-15\n",
       "GLYCERONE_PHOSPHATE:CYTOSOL                  5.000445e-13\n",
       "PHOSPHOENOLPYRUVATE:CYTOSOL                  5.385301e-10\n",
       "2-PHOSPHO-D-GLYCERATE:CYTOSOL                9.066543e-10\n",
       "BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL           9.005226e-09\n",
       "D-FRUCTOSE_6-PHOSPHATE:CYTOSOL               1.085331e-08\n",
       "3-PHOSPHO-D-GLYCERATE:CYTOSOL                6.297109e-08\n",
       "3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL      2.462820e-07\n",
       "PYRUVATE:CYTOSOL                             5.712797e-07\n",
       "PYRUVATE:MITOCHONDRIA                        8.997118e-07\n",
       "D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL          5.837993e-04\n",
       "ACETYL-COA:MITOCHONDRIA                      1.248513e+00\n",
       "Name: 9999981.0, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc30470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc3e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read boltzmann stochastic reaction likelihoods\n",
    "ode_derivatives = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_no_reg.ode_dconcs',header=0,index_col=0)\n",
    "# Get the absolute value of the derivatives:\n",
    "abs_derivatives = np.abs(ode_derivatives.iloc[-1,:])\n",
    "display(abs_derivatives.sort_values())\n",
    "#plt.figure();ode_derivatives.iloc[-5000:-1500,:].plot(legend=False,logx=True,logy=False,figsize=(10, 10))\n",
    "plt.figure();ode_derivatives.plot(legend=False,logx=False,logy=False,figsize=(10, 10))\n",
    "plt.yscale('symlog')\n",
    "#plt.legend(bbox_to_anchor=(1.35, 1.05),bbox_transform=plt.gcf().transFigure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values reported in the paper are those obtained just before the solver became unstable, at relative time 9.250000e+09."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#display(ode_derivatives.loc[9.250000e+09].sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thermodynamic Odds of Reaction from Deterministic Simulation\n",
    "The thermodynamic likelihoods are read in from the output files, where the likelihood is $e^{A_{\\alpha}/RT} = K_{\\alpha}Q_{\\alpha}^{-1}$. $A_{\\alpha}$ is the reaction affinity for reaction $\\alpha$, $K_{\\alpha}$ is the equilibrium constant, and $Q_{\\alpha}$ is the reaction quotient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ME1m                    0.000000\n",
       "ME2m                    0.000000\n",
       "CSm                     6.554156\n",
       "ACONTm                  6.703329\n",
       "ICDHxm                  6.582371\n",
       "AKGDm                   6.582372\n",
       "SUCOASm                 6.703335\n",
       "SUCD1m                  6.703332\n",
       "FUMm                    6.703327\n",
       "MDHm                    6.554156\n",
       "GAPD                    7.933921\n",
       "PGK                     7.933929\n",
       "TPI                     4.145176\n",
       "MDH                     0.000000\n",
       "PEP_Carboxylase         0.000000\n",
       "PPCK                    0.000000\n",
       "FBA                     4.145176\n",
       "FBP                     0.000000\n",
       "TKT2                    0.000000\n",
       "RPE                     0.000000\n",
       "Xylulokinase            0.000000\n",
       "PYK_org                 0.000000\n",
       "PYK                     7.933929\n",
       "RPI                     0.000000\n",
       "TKT1                    0.000000\n",
       "TALA                    0.000000\n",
       "PGM                     7.933930\n",
       "ENO                     7.933930\n",
       "GND                     0.000000\n",
       "PGL                     0.000000\n",
       "HEX1                    4.145744\n",
       "PGI                     4.145744\n",
       "G6PDH2r                 0.000000\n",
       "PFK                     4.145744\n",
       "PYRt2m                  7.933929\n",
       "PDHm                    7.933923\n",
       "ICL                     0.000000\n",
       "MAS                     0.000000\n",
       "PYRDC                   0.000000\n",
       "ALDD2y                  0.000000\n",
       "ALCD2X_copy1            0.000000\n",
       "LactateDehydrogenase    0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1050add68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1221ffac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read boltzmann ODE reaction likelihoods\n",
    "ode_likelihoods_timeseries = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_no_reg.ode_lklhd',header=1, index_col = 0, quoting=2)\n",
    "\n",
    "temp = [x for x in ode_likelihoods_timeseries.columns if 'f_' in x]\n",
    "ode_fwd_likelihoods_timeseries = ode_likelihoods_timeseries[temp]\n",
    "temp = [x for x in ode_likelihoods_timeseries.columns if 'r_' in x]\n",
    "ode_rev_likelihoods_timeseries = ode_likelihoods_timeseries[temp]\n",
    "\n",
    "ode_fwd_likelihoods_timeseries.columns = [x.split(\"f_\")[-1] for x in ode_fwd_likelihoods_timeseries.columns]\n",
    "ode_rev_likelihoods_timeseries.columns = [x.split(\"r_\")[-1] for x in ode_rev_likelihoods_timeseries.columns]\n",
    "\n",
    "plt.figure()\n",
    "ode_fwd_likelihoods_timeseries.plot(legend=False,logx=False,figsize=(12, 12))\n",
    "#plt.yscale('log')\n",
    "#plt.yscale('symlog')\n",
    "plt.legend(bbox_to_anchor=(0.02, 0.9),bbox_transform=plt.gcf().transFigure)\n",
    "#display(ode_fwd_likelihoods_timeseries.loc[:9.250000e+09].mean())\n",
    "display(ode_fwd_likelihoods_timeseries.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, when the the derivatives for acetyl-CoA go below 0.05, the solver becomes unstable because even a small time step results in a large change the derivative. However, the likelihoods  are sufficiently converged at this point that good estimates of the steady state reaction fluxes can be obtained. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Net Odds\n",
    "The net odds of a reaction are proportional to the reaction flux. To get the steady state values of the likelihoods, we'll use the average of the last 30 steps before the solver became unstable (relative time from 9.247000e+09 to 9.250000e+09)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forward</th>\n",
       "      <th>Reverse</th>\n",
       "      <th>For-Rev</th>\n",
       "      <th>Rxn Probabilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ME1m</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME2m</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSm</th>\n",
       "      <td>6.554156</td>\n",
       "      <td>5.290375e-14</td>\n",
       "      <td>6.554156</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACONTm</th>\n",
       "      <td>6.703329</td>\n",
       "      <td>1.491725e-01</td>\n",
       "      <td>6.554156</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICDHxm</th>\n",
       "      <td>6.582371</td>\n",
       "      <td>2.821428e-02</td>\n",
       "      <td>6.554157</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKGDm</th>\n",
       "      <td>6.582372</td>\n",
       "      <td>2.821556e-02</td>\n",
       "      <td>6.554156</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCOASm</th>\n",
       "      <td>6.703335</td>\n",
       "      <td>1.491783e-01</td>\n",
       "      <td>6.554157</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCD1m</th>\n",
       "      <td>6.703332</td>\n",
       "      <td>1.491752e-01</td>\n",
       "      <td>6.554157</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMm</th>\n",
       "      <td>6.703327</td>\n",
       "      <td>1.491710e-01</td>\n",
       "      <td>6.554156</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDHm</th>\n",
       "      <td>6.554156</td>\n",
       "      <td>5.290088e-14</td>\n",
       "      <td>6.554156</td>\n",
       "      <td>0.051767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <td>7.933921</td>\n",
       "      <td>1.260317e-01</td>\n",
       "      <td>7.807889</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <td>7.933929</td>\n",
       "      <td>1.260406e-01</td>\n",
       "      <td>7.807889</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPI</th>\n",
       "      <td>4.145176</td>\n",
       "      <td>2.412312e-01</td>\n",
       "      <td>3.903944</td>\n",
       "      <td>0.030834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDH</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEP_Carboxylase</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPCK</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBA</th>\n",
       "      <td>4.145176</td>\n",
       "      <td>2.412312e-01</td>\n",
       "      <td>3.903944</td>\n",
       "      <td>0.030834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBP</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKT2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylulokinase</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK_org</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK</th>\n",
       "      <td>7.933929</td>\n",
       "      <td>1.260406e-01</td>\n",
       "      <td>7.807889</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKT1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TALA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGM</th>\n",
       "      <td>7.933930</td>\n",
       "      <td>1.260410e-01</td>\n",
       "      <td>7.807889</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <td>7.933930</td>\n",
       "      <td>1.260410e-01</td>\n",
       "      <td>7.807889</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GND</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGL</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEX1</th>\n",
       "      <td>4.145744</td>\n",
       "      <td>2.412103e-01</td>\n",
       "      <td>3.904533</td>\n",
       "      <td>0.030839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <td>4.145744</td>\n",
       "      <td>2.412112e-01</td>\n",
       "      <td>3.904533</td>\n",
       "      <td>0.030839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6PDH2r</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFK</th>\n",
       "      <td>4.145744</td>\n",
       "      <td>2.412105e-01</td>\n",
       "      <td>3.904533</td>\n",
       "      <td>0.030839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRt2m</th>\n",
       "      <td>7.933929</td>\n",
       "      <td>1.260410e-01</td>\n",
       "      <td>7.807888</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDHm</th>\n",
       "      <td>7.933923</td>\n",
       "      <td>1.260362e-01</td>\n",
       "      <td>7.807887</td>\n",
       "      <td>0.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICL</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRDC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALDD2y</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALCD2X_copy1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LactateDehydrogenase</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Forward       Reverse   For-Rev  Rxn Probabilities\n",
       "ME1m                  0.000000  0.000000e+00  0.000000           0.000000\n",
       "ME2m                  0.000000  0.000000e+00  0.000000           0.000000\n",
       "CSm                   6.554156  5.290375e-14  6.554156           0.051767\n",
       "ACONTm                6.703329  1.491725e-01  6.554156           0.051767\n",
       "ICDHxm                6.582371  2.821428e-02  6.554157           0.051767\n",
       "AKGDm                 6.582372  2.821556e-02  6.554156           0.051767\n",
       "SUCOASm               6.703335  1.491783e-01  6.554157           0.051767\n",
       "SUCD1m                6.703332  1.491752e-01  6.554157           0.051767\n",
       "FUMm                  6.703327  1.491710e-01  6.554156           0.051767\n",
       "MDHm                  6.554156  5.290088e-14  6.554156           0.051767\n",
       "GAPD                  7.933921  1.260317e-01  7.807889           0.061669\n",
       "PGK                   7.933929  1.260406e-01  7.807889           0.061669\n",
       "TPI                   4.145176  2.412312e-01  3.903944           0.030834\n",
       "MDH                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "PEP_Carboxylase       0.000000  0.000000e+00  0.000000           0.000000\n",
       "PPCK                  0.000000  0.000000e+00  0.000000           0.000000\n",
       "FBA                   4.145176  2.412312e-01  3.903944           0.030834\n",
       "FBP                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "TKT2                  0.000000  0.000000e+00  0.000000           0.000000\n",
       "RPE                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "Xylulokinase          0.000000  0.000000e+00  0.000000           0.000000\n",
       "PYK_org               0.000000  0.000000e+00  0.000000           0.000000\n",
       "PYK                   7.933929  1.260406e-01  7.807889           0.061669\n",
       "RPI                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "TKT1                  0.000000  0.000000e+00  0.000000           0.000000\n",
       "TALA                  0.000000  0.000000e+00  0.000000           0.000000\n",
       "PGM                   7.933930  1.260410e-01  7.807889           0.061669\n",
       "ENO                   7.933930  1.260410e-01  7.807889           0.061669\n",
       "GND                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "PGL                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "HEX1                  4.145744  2.412103e-01  3.904533           0.030839\n",
       "PGI                   4.145744  2.412112e-01  3.904533           0.030839\n",
       "G6PDH2r               0.000000  0.000000e+00  0.000000           0.000000\n",
       "PFK                   4.145744  2.412105e-01  3.904533           0.030839\n",
       "PYRt2m                7.933929  1.260410e-01  7.807888           0.061669\n",
       "PDHm                  7.933923  1.260362e-01  7.807887           0.061669\n",
       "ICL                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "MAS                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "PYRDC                 0.000000  0.000000e+00  0.000000           0.000000\n",
       "ALDD2y                0.000000  0.000000e+00  0.000000           0.000000\n",
       "ALCD2X_copy1          0.000000  0.000000e+00  0.000000           0.000000\n",
       "LactateDehydrogenase  0.000000  0.000000e+00  0.000000           0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ode_likelihoods_steadystate = pd.DataFrame(data = ode_fwd_likelihoods_timeseries.loc[9.247000e+09:9.250000e+09].mean(), \n",
    "ode_likelihoods_steadystate = pd.DataFrame(data = ode_fwd_likelihoods_timeseries.mean(), \n",
    "                                           index = ode_fwd_likelihoods_timeseries.columns,\n",
    "                                          columns =['Forward'])\n",
    "#ode_likelihoods_steadystate['Reverse'] = ode_rev_likelihoods_timeseries.loc[9.247000e+09:9.250000e+09].mean()\n",
    "ode_likelihoods_steadystate['Reverse'] = ode_rev_likelihoods_timeseries.mean()\n",
    "#ode_likelihoods_steadystate['Reverse'] = ode_likelihoods_steadystate['Forward'].rdiv(1)\n",
    "ode_likelihoods_steadystate['For-Rev'] = ode_likelihoods_steadystate['Forward'] - ode_likelihoods_steadystate['Reverse']\n",
    "ode_likelihoods_steadystate['Rxn Probabilities'] = ode_likelihoods_steadystate['For-Rev']/np.sum(abs(ode_likelihoods_steadystate['For-Rev']))\n",
    "display(ode_likelihoods_steadystate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map Net Likelihoods onto Metabolic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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7-phosphate\",\"label_x\":5925.436091216573,\"label_y\":4694.675349836323,\"node_is_primary\":true},\"681158\":{\"node_type\":\"multimarker\",\"x\":5457.144433282116,\"y\":4416.475666956701},\"681159\":{\"node_type\":\"midmarker\",\"x\":5437.14779781253,\"y\":4416.108829259021},\"681160\":{\"node_type\":\"multimarker\",\"x\":5417.151162342941,\"y\":4415.7419915613455},\"681161\":{\"node_type\":\"metabolite\",\"x\":5582.446887031156,\"y\":4600.768145035434,\"bigg_id\":\"e4p_c\",\"name\":\"D-Erythrose 4-phosphate\",\"label_x\":5628.504992499906,\"label_y\":4596.514727066684,\"node_is_primary\":true},\"681162\":{\"node_type\":\"metabolite\",\"x\":4884.633825,\"y\":5452.171119140625,\"bigg_id\":\"g3p_c\",\"name\":\"Glyceraldehyde 3-phosphate\",\"label_x\":4793.57962578125,\"label_y\":5470.519751953125,\"node_is_primary\":true},\"681164\":{\"node_type\":\"multimarker\",\"x\":5757.229485938987,\"y\":4745.1911923221305},\"681165\":{\"node_type\":\"midmarker\",\"x\":5737.233118184583,\"y\":4744.81004060606},\"681166\":{\"node_type\":\"multimarker\",\"x\":5717.236750430179,\"y\":4744.42888888999},\"681170\":{\"node_type\":\"multimarker\",\"x\":5811.7205574297495,\"y\":5017.583115735961},\"681171\":{\"node_type\":\"midmarker\",\"x\":5791.727965419229,\"y\":5018.127417766906},\"681172\":{\"node_type\":\"multimarker\",\"x\":5771.735373408715,\"y\":5018.67171979786},\"681173\":{\"node_type\":\"metabolite\",\"x\":5902.775825538994,\"y\":5055.11894061772,\"bigg_id\":\"atp_c\",\"name\":\"ATP\",\"label_x\":5932.775825538994,\"label_y\":5065.11894061772,\"node_is_primary\":false},\"681174\":{\"node_type\":\"metabolite\",\"x\":5641.783525340335,\"y\":5022.209682999042,\"bigg_id\":\"s17bp_c\",\"name\":\"Sedoheptulose 1,7-bisphosphate\",\"label_x\":5576.448564402835,\"label_y\":5061.393765030292,\"node_is_primary\":true},\"681175\":{\"node_type\":\"metabolite\",\"x\":5682.857313423275,\"y\":5061.1062629581775,\"bigg_id\":\"adp_c\",\"name\":\"ADP\",\"label_x\":5695.135633735775,\"label_y\":5097.6892707706775,\"node_is_primary\":false},\"681176\":{\"node_type\":\"metabolite\",\"x\":5680.680105299472,\"y\":4977.244781634852,\"bigg_id\":\"h_c\",\"name\":\"H+\",\"label_x\":5678.189870924472,\"label_y\":4957.708160541102,\"node_is_primary\":false},\"681177\":{\"node_type\":\"multimarker\",\"x\":5466.602565490323,\"y\":5021.370169710601},\"681178\":{\"node_type\":\"midmarker\",\"x\":5446.602595122823,\"y\":5021.404597883198},\"681179\":{\"node_type\":\"multimarker\",\"x\":5426.602624755323,\"y\":5021.439026055796},\"681196\":{\"node_type\":\"multimarker\",\"x\":3656.21822941599,\"y\":6497.805287603545},\"681197\":{\"node_type\":\"midmarker\",\"x\":3598.941102012005,\"y\":6539.033187616906},\"681198\":{\"node_type\":\"multimarker\",\"x\":3552.28262695177,\"y\":6566.100931380266},\"681199\":{\"node_type\":\"metabolite\",\"x\":3809.70605843637,\"y\":6454.362214350203,\"bigg_id\":\"coa_m\",\"name\":\"Coenzyme A\",\"label_x\":3824.70605843637,\"label_y\":6454.362214350203,\"node_is_primary\":false},\"681200\":{\"node_type\":\"metabolite\",\"x\":3709.9612274701094,\"y\":6346.559257618323,\"bigg_id\":\"nad_m\",\"name\":\"Nicotinamide adenine dinucleotide\",\"label_x\":3664.9612274701094,\"label_y\":6376.559257618323,\"node_is_primary\":false},\"681202\":{\"node_type\":\"metabolite\",\"x\":3335.720292960151,\"y\":6710.265906677991,\"bigg_id\":\"co2_m\",\"name\":\"CO2\",\"label_x\":3350.720292960151,\"label_y\":6710.265906677991,\"node_is_primary\":false},\"681203\":{\"node_type\":\"metabolite\",\"x\":3250.1336651188894,\"y\":6559.99029369611,\"bigg_id\":\"nadh_m\",\"name\":\"Nicotinamide adenine dinucleotide - reduced\",\"label_x\":3196.1336651188894,\"label_y\":6589.99029369611,\"node_is_primary\":false}},\"text_labels\":{\"667999\":{\"x\":4450.6298828125,\"y\":4739.9862060546875,\"text\":\"Glycolysis\"},\"668016\":{\"x\":3003.05224609375,\"y\":5951.006811718751,\"text\":\"TCA Cycle\"},\"668017\":{\"x\":5425.350830078125,\"y\":5632.343017578125,\"text\":\"Pentose Phosphate Pathway\"},\"668018\":{\"x\":2617.043701171875,\"y\":4210.3056640625,\"text\":\"Glycolysis\"}},\"canvas\":{\"x\":1891.530700976562,\"y\":1850.3106803671876,\"width\":4960.490337304691,\"height\":7502.07660657031}}]'),\n",
       "               model_data: JSON.parse('null'),\n",
       "               static_site_index: JSON.parse('null'),\n",
       "           };\n",
       "       }\n",
       "   })();\n",
       "  </script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from escher import Builder\n",
    "\n",
    "reaction_data = ode_likelihoods_steadystate['For-Rev'].to_dict()\n",
    "#b = Builder(map_name=\"iMM904.PPP_TCA_Glycolysis.json\",reaction_data=reaction_data)\n",
    "b = Builder(map_name=\"iMM904.compact_Glycolysis_TCA_PPP.json\",reaction_data=reaction_data)\n",
    "#b.display_in_browser(menu='all')\n",
    "b.display_in_notebook(menu='zoom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Active and Variable Metabolites\n",
    "Some metabolites concentrations are parameters - those that form the boundary conditions such as cofactors, glucose and CO$_{2}$ - and others are free variables. Here we find the subset of metabolites that are free variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find active reactions\n",
    "# find chemicals that are in at least one reaction. Includes chemicals that are fixed as well as\n",
    "# variable ones:\n",
    "S_active = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_no_reg.amat',header=0, index_col = 0, quoting=2)\n",
    "del S_active['forward reaction']\n",
    "\n",
    "active_metabolites_idx = (S_active != 0).any(axis=0) #any searches down the column\n",
    "inactive_metabolites_idx = ~active_metabolites_idx\n",
    "\n",
    "metabolites_status = pd.read_csv('neurospora_pentose_phos.glycolysis.tca.2_no_reg.rstrt',delimiter = '\\t', index_col=0,skiprows = 2, header=None,quoting=2,usecols=[0,2])\n",
    "metabolites_status.rename(columns={2:'Value'},inplace=True)\n",
    "metabolites_status.index = metabolites_status.index.str.strip()\n",
    "metabolites_status['Variable?'] = metabolites_status == 'V'\n",
    "metabolites_status.insert(1,'Active?',active_metabolites_idx.values)\n",
    "del metabolites_status['Value']\n",
    "\n",
    "metabolites_status['Variable & Active'] = metabolites_status['Variable?']&metabolites_status['Active?']\n",
    "variable_metabolite_idx = list(metabolites_status[metabolites_status['Variable & Active']==True].index)\n",
    "variable_metabolite_idx = list(map(str.strip, variable_metabolite_idx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Derivatives of Metabolite Concentration: S $\\cdot$ Net Odds = flux\n",
    "This is a demonstration that the product of the stoichiometric matrix S and the net thermodynamic odds are close to zero, which is the steady state condition discussed in the manuscript. Here the stoichiometric matrix $S^{m,n}$is defined as $m$ reactions by $n$ metabolites, which is the transpose of how it is used in the metabolic modeling literature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CITRATE:MITOCHONDRIA                      -4.990009e-07\n",
       "SUCCINYL-COA:MITOCHONDRIA                 -2.600002e-07\n",
       "ISOCITRATE:MITOCHONDRIA                   -2.200002e-07\n",
       "SUCCINATE:MITOCHONDRIA                    -9.900094e-08\n",
       "D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL      -2.457542e-08\n",
       "BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL        -1.108892e-08\n",
       "2-PHOSPHO-D-GLYCERATE:CYTOSOL             -6.193806e-09\n",
       "PHOSPHOENOLPYRUVATE:CYTOSOL               -1.598404e-09\n",
       "(S)-MALATE:MITOCHONDRIA                   -9.989307e-10\n",
       "OXALOACETATE:MITOCHONDRIA                  0.000000e+00\n",
       "GLYCERONE_PHOSPHATE:CYTOSOL                1.098900e-09\n",
       "D-FRUCTOSE_6-PHOSPHATE:CYTOSOL             5.944056e-08\n",
       "3-PHOSPHO-D-GLYCERATE:CYTOSOL              7.042957e-08\n",
       "3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL    2.501498e-07\n",
       "2-OXOGLUTARATE:MITOCHONDRIA                2.800002e-07\n",
       "PYRUVATE:CYTOSOL                           5.807193e-07\n",
       "FUMARATE:MITOCHONDRIA                      7.990009e-07\n",
       "PYRUVATE:MITOCHONDRIA                      8.989011e-07\n",
       "D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL        5.887064e-04\n",
       "ACETYL-COA:MITOCHONDRIA                    1.253731e+00\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_no_reg.mat',header=0, index_col = 0, quoting=2)\n",
    "del S['forward reaction']\n",
    "\n",
    "S = S.astype(np.float64)\n",
    "# S.T.dot(ode_likelihoods_steadystate['For-Rev']) =  time derivative of concentrations\n",
    "#                                                    0 at steady state and maximum entropy.\n",
    "# If boundary conditions prevent maximum entropy, then the flux !=0 for boundary species.\n",
    "derivatives2 = (S.T.dot(ode_likelihoods_steadystate['For-Rev'])).sort_values()\n",
    "display(derivatives2[variable_metabolite_idx].sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Variable Metabolite Levels from Deterministic Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105048e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1221f8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read boltzmann stochastic reaction likelihoods\n",
    "ode_metabolites = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_no_reg.ode_concs',header=0,index_col=0)\n",
    "\n",
    "fig = plt.figure();\n",
    "plot = ode_metabolites[variable_metabolite_idx].plot(legend=False,logy=True,logx=True,figsize=(10, 10))\n",
    "fig = plot.get_figure()\n",
    "fig.savefig('metabolites_no_regulation.png')\n",
    "plt.legend(bbox_to_anchor=(1.40, 0.90),bbox_transform=plt.gcf().transFigure)\n",
    "fig.savefig('metabolites_no_regulation_legend.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LACTATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6-PHOSPHO-D-GLUCONATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETALDEHYDE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHANOL:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OXALOACETATE:MITOCHONDRIA</th>\n",
       "      <td>5.723803e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-OXOGLUTARATE:MITOCHONDRIA</th>\n",
       "      <td>3.787650e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-ERYTHROSE-4-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>5.149502e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-RIBULOSE-5-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.890365e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-RIBOSE-5-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>3.189369e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLYOXYLATE:GLYOXYSOME</th>\n",
       "      <td>1.000000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADP+:MITOCHONDRIA</th>\n",
       "      <td>2.100000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADP+:CYTOSOL</th>\n",
       "      <td>2.100000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-XYLULOSE-5-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.320598e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>3.084260e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISOCITRATE:MITOCHONDRIA</th>\n",
       "      <td>3.539343e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-MALATE:MITOCHONDRIA</th>\n",
       "      <td>4.706638e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMARATE:MITOCHONDRIA</th>\n",
       "      <td>7.350155e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADH:MITOCHONDRIA</th>\n",
       "      <td>8.300000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADH:CYTOSOL</th>\n",
       "      <td>8.300000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2:MITOCHONDRIA</th>\n",
       "      <td>1.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2:CYTOSOL</th>\n",
       "      <td>1.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADPH:MITOCHONDRIA</th>\n",
       "      <td>1.200000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADPH:CYTOSOL</th>\n",
       "      <td>1.200000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDOHEPTULOSE_7-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>3.035266e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCINATE:MITOCHONDRIA</th>\n",
       "      <td>4.927172e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP:CYTOSOL</th>\n",
       "      <td>5.600000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP:MITOCHONDRIA</th>\n",
       "      <td>5.600000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCINATE:GLYOXYSOME</th>\n",
       "      <td>5.700000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISOCITRATE:GLYOXYSOME</th>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OXALOACETATE:CYTOSOL</th>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDOX1:MITOCHONDRIA</th>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDOX2:MITOCHONDRIA</th>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COA:MITOCHONDRIA</th>\n",
       "      <td>1.400000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COA:GLYOXYSOME</th>\n",
       "      <td>1.400000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-MALATE:CYTOSOL</th>\n",
       "      <td>1.700000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-MALATE:GLYOXYSOME</th>\n",
       "      <td>1.700000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETA-D-GLUCOSE:CYTOSOL</th>\n",
       "      <td>2.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAD+:MITOCHONDRIA</th>\n",
       "      <td>2.600000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAD+:CYTOSOL</th>\n",
       "      <td>2.600000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLYCERONE_PHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.808746e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETYL-COA:GLYOXYSOME</th>\n",
       "      <td>2.863050e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRATE:MITOCHONDRIA</th>\n",
       "      <td>3.467543e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-XYLULOSE:CYTOSOL</th>\n",
       "      <td>8.800000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCINYL-COA:MITOCHONDRIA</th>\n",
       "      <td>8.866397e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP:CYTOSOL</th>\n",
       "      <td>9.600000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP:MITOCHONDRIA</th>\n",
       "      <td>9.600000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTHOPHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTHOPHOSPHATE:MITOCHONDRIA</th>\n",
       "      <td>2.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHOSPHOENOLPYRUVATE:CYTOSOL</th>\n",
       "      <td>6.645813e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-PHOSPHO-D-GLYCERATE:CYTOSOL</th>\n",
       "      <td>1.330016e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-FRUCTOSE_6-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>4.584099e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>7.607134e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-PHOSPHO-D-GLYCERATE:CYTOSOL</th>\n",
       "      <td>1.138637e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL</th>\n",
       "      <td>5.803109e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRUVATE:CYTOSOL</th>\n",
       "      <td>6.084000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRUVATE:MITOCHONDRIA</th>\n",
       "      <td>8.427092e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL</th>\n",
       "      <td>1.643838e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETYL-COA:MITOCHONDRIA</th>\n",
       "      <td>1.043645e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    ODE\n",
       "D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL  0.000000e+00\n",
       "LACTATE:CYTOSOL                            0.000000e+00\n",
       "6-PHOSPHO-D-GLUCONATE:CYTOSOL              0.000000e+00\n",
       "ACETALDEHYDE:CYTOSOL                       0.000000e+00\n",
       "ACETATE:CYTOSOL                            0.000000e+00\n",
       "ETHANOL:CYTOSOL                            0.000000e+00\n",
       "OXALOACETATE:MITOCHONDRIA                  5.723803e-22\n",
       "2-OXOGLUTARATE:MITOCHONDRIA                3.787650e-10\n",
       "D-ERYTHROSE-4-PHOSPHATE:CYTOSOL            5.149502e-08\n",
       "D-RIBULOSE-5-PHOSPHATE:CYTOSOL             2.890365e-07\n",
       "D-RIBOSE-5-PHOSPHATE:CYTOSOL               3.189369e-07\n",
       "GLYOXYLATE:GLYOXYSOME                      1.000000e-06\n",
       "NADP+:MITOCHONDRIA                         2.100000e-06\n",
       "NADP+:CYTOSOL                              2.100000e-06\n",
       "D-XYLULOSE-5-PHOSPHATE:CYTOSOL             2.320598e-06\n",
       "D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL       3.084260e-05\n",
       "ISOCITRATE:MITOCHONDRIA                    3.539343e-05\n",
       "(S)-MALATE:MITOCHONDRIA                    4.706638e-05\n",
       "FUMARATE:MITOCHONDRIA                      7.350155e-05\n",
       "NADH:MITOCHONDRIA                          8.300000e-05\n",
       "NADH:CYTOSOL                               8.300000e-05\n",
       "CO2:MITOCHONDRIA                           1.000000e-04\n",
       "CO2:CYTOSOL                                1.000000e-04\n",
       "NADPH:MITOCHONDRIA                         1.200000e-04\n",
       "NADPH:CYTOSOL                              1.200000e-04\n",
       "SEDOHEPTULOSE_7-PHOSPHATE:CYTOSOL          3.035266e-04\n",
       "SUCCINATE:MITOCHONDRIA                     4.927172e-04\n",
       "ADP:CYTOSOL                                5.600000e-04\n",
       "ADP:MITOCHONDRIA                           5.600000e-04\n",
       "SUCCINATE:GLYOXYSOME                       5.700000e-04\n",
       "ISOCITRATE:GLYOXYSOME                      1.000000e-03\n",
       "OXALOACETATE:CYTOSOL                       1.000000e-03\n",
       "REDOX1:MITOCHONDRIA                        1.000000e-03\n",
       "REDOX2:MITOCHONDRIA                        1.000000e-03\n",
       "COA:MITOCHONDRIA                           1.400000e-03\n",
       "COA:GLYOXYSOME                             1.400000e-03\n",
       "(S)-MALATE:CYTOSOL                         1.700000e-03\n",
       "(S)-MALATE:GLYOXYSOME                      1.700000e-03\n",
       "BETA-D-GLUCOSE:CYTOSOL                     2.000000e-03\n",
       "NAD+:MITOCHONDRIA                          2.600000e-03\n",
       "NAD+:CYTOSOL                               2.600000e-03\n",
       "GLYCERONE_PHOSPHATE:CYTOSOL                2.808746e-03\n",
       "ACETYL-COA:GLYOXYSOME                      2.863050e-03\n",
       "CITRATE:MITOCHONDRIA                       3.467543e-03\n",
       "D-XYLULOSE:CYTOSOL                         8.800000e-03\n",
       "SUCCINYL-COA:MITOCHONDRIA                  8.866397e-03\n",
       "ATP:CYTOSOL                                9.600000e-03\n",
       "ATP:MITOCHONDRIA                           9.600000e-03\n",
       "ORTHOPHOSPHATE:CYTOSOL                     2.000000e-02\n",
       "ORTHOPHOSPHATE:MITOCHONDRIA                2.000000e-02\n",
       "PHOSPHOENOLPYRUVATE:CYTOSOL                6.645813e-01\n",
       "2-PHOSPHO-D-GLYCERATE:CYTOSOL              1.330016e+00\n",
       "D-FRUCTOSE_6-PHOSPHATE:CYTOSOL             4.584099e+01\n",
       "BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL         7.607134e+01\n",
       "3-PHOSPHO-D-GLYCERATE:CYTOSOL              1.138637e+02\n",
       "3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL    5.803109e+02\n",
       "PYRUVATE:CYTOSOL                           6.084000e+02\n",
       "PYRUVATE:MITOCHONDRIA                      8.427092e+02\n",
       "D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL        1.643838e+06\n",
       "ACETYL-COA:MITOCHONDRIA                    1.043645e+09"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ode_metabolites_steadystate = \\\n",
    "  pd.DataFrame(data = ode_metabolites.iloc[-20:,:].mean(),columns=['ODE'])\n",
    "ode_metabolites_steadystate.sort_values(by=['ODE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer Regulated Reactions\n",
    "Regulated reactions are inferred using a loss function defined as the log ratio of the observed values $n_{i(\\alpha)}$ to the predicted values $\\tilde{n}_{i(\\alpha)}$ of the $M(\\alpha )$ reaction products of reaction $\\alpha$,\n",
    "\\[\n",
    "L_{\\alpha }=\\log \\prod\\limits_{i(\\alpha )}^{M(\\alpha )}{\\frac{n_{i(\\alpha )}}{\\tilde{n}_{i(\\alpha )}}}.\n",
    "\\] \t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ODE</th>\n",
       "      <th>Expected</th>\n",
       "      <th>$L_{reg}$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reaction title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CSm</th>\n",
       "      <td>4.854560e-06</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>1.579919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCOASm</th>\n",
       "      <td>6.622119e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.890415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <td>6.645813e-01</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>6.499157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGM</th>\n",
       "      <td>1.330016e+00</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>7.192947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEX1</th>\n",
       "      <td>4.259995e-02</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>10.659608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <td>4.584099e+01</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>10.732934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <td>4.816581e-02</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>10.782405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRt2m</th>\n",
       "      <td>8.427092e+02</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>13.644377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <td>1.093091e+00</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>13.904520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK</th>\n",
       "      <td>5.840640e+00</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>15.580351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFK</th>\n",
       "      <td>9.205491e+02</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>20.640481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDHm</th>\n",
       "      <td>8.662250e+00</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>22.882240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ODE      Expected  $L_{reg}$\n",
       "reaction title                                       \n",
       "CSm             4.854560e-06  1.000000e-06   1.579919\n",
       "SUCOASm         6.622119e-09  1.000000e-09   1.890415\n",
       "ENO             6.645813e-01  1.000000e-03   6.499157\n",
       "PGM             1.330016e+00  1.000000e-03   7.192947\n",
       "HEX1            4.259995e-02  1.000000e-06  10.659608\n",
       "PGI             4.584099e+01  1.000000e-03  10.732934\n",
       "GAPD            4.816581e-02  1.000000e-06  10.782405\n",
       "PYRt2m          8.427092e+02  1.000000e-03  13.644377\n",
       "PGK             1.093091e+00  1.000000e-06  13.904520\n",
       "PYK             5.840640e+00  1.000000e-06  15.580351\n",
       "PFK             9.205491e+02  1.000000e-06  20.640481\n",
       "PDHm            8.662250e+00  1.000000e-09  22.882240"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ode_metabolites_steadystate['Expected'] = 1.0e-03\n",
    "\n",
    "S = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_no_reg.mat',header=0, index_col = 0, quoting=2)\n",
    "P = (S>0)\n",
    "del P['forward reaction']\n",
    "\n",
    "P = P.astype(np.float64)\n",
    "#display(R)\n",
    "product_concentrations = P.multiply(ode_metabolites_steadystate['ODE'],axis=1)\n",
    "product_concentrations[product_concentrations == 0] = 1\n",
    "product_concentrations_rxns = pd.DataFrame(data = product_concentrations.T.product(), columns=['ODE'])\n",
    "expect_product_concentrations = P.multiply(ode_metabolites_steadystate['Expected'],axis=1)\n",
    "expect_product_concentrations[expect_product_concentrations == 0] = 1\n",
    "product_concentrations_rxns['Expected'] = (expect_product_concentrations.T).product()\n",
    "\n",
    "s_regulation = '$L_{reg}$'\n",
    "product_concentrations_rxns[s_regulation] = \\\n",
    "    np.log(product_concentrations_rxns['ODE']/product_concentrations_rxns['Expected'])\n",
    "\n",
    "idx = (ode_likelihoods_steadystate['Reverse'] != 0) & (ode_likelihoods_steadystate['Forward'] != 0) & \\\n",
    "            (product_concentrations_rxns[s_regulation]>1)\n",
    "#display(product_concentrations_rxns[product_concentrations_rxns[s_regulation]>1])\n",
    "display(product_concentrations_rxns[idx].sort_values(by=s_regulation))\n",
    "product_concentrations_rxns[idx].to_csv(path_or_buf='loss_no_regulation.txt',sep='\\t')"
   ]
  }
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