neurospora_regulation_rate_constants-supplementary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Boltzmann\" data-toc-modified-id=\"Boltzmann-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Boltzmann</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=\"lev2 toc-item\"><a href=\"#Turning-on-Regulation-and-Setting-Enzyme-Levels\" data-toc-modified-id=\"Turning-on-Regulation-and-Setting-Enzyme-Levels-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Turning on Regulation and Setting Enzyme Levels</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-With-Regulation\" data-toc-modified-id=\"Model-With-Regulation-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Model With 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-kinetic-rate-law-simulation\" data-toc-modified-id=\"Derivatives-from-ODE-kinetic-rate-law-simulation-212\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Derivatives from ODE kinetic rate law simulation</a></div><div class=\"lev3 toc-item\"><a href=\"#Forward-and-Reverse-Rates-from-Deterministic-Simulation\" data-toc-modified-id=\"Forward-and-Reverse-Rates-from-Deterministic-Simulation-213\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>Forward and Reverse Rates from Deterministic Simulation</a></div><div class=\"lev2 toc-item\"><a href=\"#Analyze-Metabolite-Levels-from-Deterministic-Simulation\" data-toc-modified-id=\"Analyze-Metabolite-Levels-from-Deterministic-Simulation-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Analyze Metabolite Levels from Deterministic Simulation</a></div><div class=\"lev2 toc-item\"><a href=\"#Reaction-Rates-from-Kinetic-Rate-Law\" data-toc-modified-id=\"Reaction-Rates-from-Kinetic-Rate-Law-23\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Reaction Rates from Kinetic Rate Law</a></div><div class=\"lev2 toc-item\"><a href=\"#Calculate-Power\" data-toc-modified-id=\"Calculate-Power-24\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Calculate Power</a></div><div class=\"lev3 toc-item\"><a href=\"#Power-Characteristics\" data-toc-modified-id=\"Power-Characteristics-241\"><span class=\"toc-item-num\">2.4.1&nbsp;&nbsp;</span>Power Characteristics</a></div><div class=\"lev2 toc-item\"><a href=\"#Calculate-Resistance-and-Conductance\" data-toc-modified-id=\"Calculate-Resistance-and-Conductance-25\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Calculate Resistance and Conductance</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "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",
    "#from ipynb_latex_setup import *\n",
    "%matplotlib inline\n",
    "N_avogadro = 6.022140857e+23\n",
    "VolCell = 1.0e-15\n",
    "Concentration2Count = N_avogadro * VolCell\n",
    "concentration_increment = 1/(N_avogadro*VolCell)\n",
    "RT = 8.3144598e-03 * 298.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change directories to where the simulation data is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "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\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",
    "Reactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REACTION\t ME1m\r\n",
      "LEFT (S)-MALATE + NAD+\r\n",
      "RIGHT pyruvate + NADH + CO2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t 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 0.0\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t CSm\r\n",
      "LEFT OXALOACETATE + ACETYL-COA + H2O\r\n",
      "RIGHT CITRATE + COA\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 3.46538159457e-06\r\n",
      "k_REVERSE 6.50610331938e-14\r\n",
      "//\r\n",
      "REACTION\t ACONTm\r\n",
      "LEFT CITRATE\r\n",
      "RIGHT ISOCITRATE\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 1.73623049906e-07\r\n",
      "k_REVERSE 2.53743943371e-06\r\n",
      "//\r\n",
      "REACTION\t ICDHxm\r\n",
      "LEFT ISOCITRATE + NAD+\r\n",
      "RIGHT 2-OXOGLUTARATE + NADH + CO2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 3.85046925276e-12\r\n",
      "k_REVERSE 5.28080915281e-12\r\n",
      "//\r\n",
      "REACTION\t AKGDm\r\n",
      "LEFT 2-OXOGLUTARATE + COA + NAD+\r\n",
      "RIGHT SUCCINYL-COA + CO2 + NADH\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 1.95516451027e-13\r\n",
      "k_REVERSE 5.56456043321e-19\r\n",
      "//\r\n",
      "REACTION\t SUCOASm\r\n",
      "LEFT SUCCINYL-COA + ADP + Orthophosphate\r\n",
      "RIGHT SUCCINATE + ATP + COA\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 1.17011859566e-21\r\n",
      "k_REVERSE 2.61761132872e-21\r\n",
      "//\r\n",
      "REACTION\t SUCD1m\r\n",
      "LEFT SUCCINATE + redox1\r\n",
      "RIGHT FUMARATE + redox2\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 6.72237050766e-14\r\n",
      "k_REVERSE 6.72237050766e-14\r\n",
      "//\r\n",
      "REACTION\t FUMm\r\n",
      "LEFT FUMARATE + H2O\r\n",
      "RIGHT (S)-MALATE\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 1.28452229613e-07\r\n",
      "k_REVERSE 2.99241367259e-08\r\n",
      "//\r\n",
      "REACTION\t MDHm\r\n",
      "LEFT (S)-MALATE + NAD+\r\n",
      "RIGHT OXALOACETATE + NADH\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "k_FORWARD 6.02465353109e-14\r\n",
      "k_REVERSE 8.16137826051e-09\r\n",
      "//\r\n",
      "REACTION\t GAPD\r\n",
      "LEFT D-GLYCERALDEHYDE-3-PHOSPHATE + ORTHOPHOSPHATE + NAD+\r\n",
      "RIGHT 3-Phospho-D-glyceroyl_phosphate + NADH\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENESIS, GLYCOLYSIS\r\n",
      "k_FORWARD 2.08196126641e-09\r\n",
      "k_REVERSE 5.26524284909e-09\r\n",
      "//\r\n",
      "REACTION\t PGK\r\n",
      "LEFT 3-Phospho-D-glyceroyl_phosphate + ADP\r\n",
      "RIGHT 3-PHOSPHO-D-GLYCERATE + ATP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENESIS, GLYCOLYSIS, CALVIN CYCLE\r\n",
      "k_FORWARD 2.13029627082e-09\r\n",
      "k_REVERSE 7.987666474e-11\r\n",
      "//\r\n",
      "REACTION\t TPI\r\n",
      "LEFT Glycerone_phosphate\r\n",
      "RIGHT D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY CALVIN CYCLE, GLYCOLYSIS\r\n",
      "k_FORWARD 0.000909644994161\r\n",
      "k_REVERSE 0.0199949618125\r\n",
      "//\r\n",
      "REACTION\t MDH\r\n",
      "LEFT (S)-MALATE + NAD+\r\n",
      "RIGHT OXALOACETATE + NADH\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t 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",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t PPCK\r\n",
      "LEFT oxaloacetate + ATP\r\n",
      "RIGHT phosphoenolpyruvate + ADP + CO2\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY GLUCONEOGENSESIS\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t FBA\r\n",
      "LEFT D-FRUCTOSE_1,6-BISPHOSPHATE\r\n",
      "RIGHT Glycerone_phosphate + D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENSESIS, GLYCOLYSIS, CALVIN CYCLE\r\n",
      "k_FORWARD 8.11191324392e-08\r\n",
      "k_REVERSE 0.000616957167254\r\n",
      "//\r\n",
      "REACTION\t FBP\r\n",
      "LEFT D-FRUCTOSE_6-PHOSPHATE + Orthophosphate\r\n",
      "RIGHT H2O + D-FRUCTOSE_1,6-BISPHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY GLUCONEOGENESIS, CALVIN CYCLE\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t TKT2\r\n",
      "LEFT D-FRUCTOSE_6-PHOSPHATE + D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "RIGHT D-ERYTHROSE-4-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY CALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t RPE\r\n",
      "LEFT D-XYLULOSE-5-PHOSPHATE\r\n",
      "RIGHT D-RIBULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY CALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t Xylulokinase\r\n",
      "LEFT D-XYLULOSE + ATP\r\n",
      "RIGHT D-XYLULOSE-5-PHOSPHATE + ADP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY CALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t PYK_org\r\n",
      "LEFT ADP + PHOSPHOENOLPYRUVATE\r\n",
      "RIGHT PYRUVATE + ATP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY GLYCOLYSIS, PYRUVATE METABOLISM, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t PYK\r\n",
      "LEFT ADP + PHOSPHOENOLPYRUVATE\r\n",
      "RIGHT PYRUVATE + ATP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLYCOLYSIS, PYRUVATE METABOLISM, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.000368091614665\r\n",
      "k_REVERSE 2.95814395877e-09\r\n",
      "//\r\n",
      "REACTION\t RPI\r\n",
      "LEFT D-RIBOSE-5-PHOSPHATE\r\n",
      "RIGHT D-RIBULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY CALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t TKT1\r\n",
      "LEFT SEDOHEPTULOSE_7-PHOSPHATE + D-GLYCERALDEHYDE-3-PHOSPHATE\r\n",
      "RIGHT D-RIBOSE-5-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY CALVIN CYCLE, PENTOSE PHOSPHATE PATHWAY, RUBISCO SHUNT\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t TALA\r\n",
      "LEFT D-GLYCERALDEHYDE-3-PHOSPHATE + SEDOHEPTULOSE_7-PHOSPHATE\r\n",
      "RIGHT D-FRUCTOSE_6-PHOSPHATE + D-ERYTHROSE-4-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY RUBISCO SHUNT, PENTOSE PHOSPHATE PATHWAY\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t PGM\r\n",
      "LEFT 3-PHOSPHO-D-GLYCERATE\r\n",
      "RIGHT 2-PHOSPHO-D-GLYCERATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENESIS, RUBISCO SHUNT, GLYCOLYSIS I\r\n",
      "k_FORWARD 0.0011932320055\r\n",
      "k_REVERSE 0.0128837015343\r\n",
      "//\r\n",
      "REACTION\t ENO\r\n",
      "LEFT 2-PHOSPHO-D-GLYCERATE\r\n",
      "RIGHT PHOSPHOENOLPYRUVATE + H2O\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENESIS, RUBISCO SHUNT, GLYCOLYSIS I\r\n",
      "k_FORWARD 0.0791920214198\r\n",
      "k_REVERSE 0.0199883948156\r\n",
      "//\r\n",
      "REACTION\t 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",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY PENTOSE PHOSPHATE PATHWAY\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t 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",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY PENTOSE PHOSPHATE PATHWAY\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t HEX1\r\n",
      "LEFT BETA-D-GLUCOSE + ATP\r\n",
      "RIGHT BETA-D-GLUCOSE-6-PHOSPHATE + ADP\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENESIS, (GLYCOLYSIS-YEAST), GLYCOLYSIS\r\n",
      "NREGULATION ATP 2.80e-03 10\r\n",
      "k_FORWARD 9.20996093059e-10\r\n",
      "k_REVERSE 1.00184422035e-13\r\n",
      "//\r\n",
      "REACTION\t PGI\r\n",
      "LEFT BETA-D-GLUCOSE-6-PHOSPHATE\r\n",
      "RIGHT D-FRUCTOSE_6-PHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLUCONEOGENESIS, (GLYCOLYSIS-YEAST), GLYCOLYSIS\r\n",
      "k_FORWARD 9.94481490048e-08\r\n",
      "k_REVERSE 3.98304496959e-08\r\n",
      "//\r\n",
      "REACTION\t G6PDH2r\r\n",
      "LEFT BETA-D-GLUCOSE-6-PHOSPHATE + NADP+\r\n",
      "RIGHT D-Glucono-1,5-lactone_6-phosphate + NADPH\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY GLUCONEOGENESIS\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t PFK\r\n",
      "LEFT D-FRUCTOSE_6-PHOSPHATE + ATP\r\n",
      "RIGHT ADP + D-FRUCTOSE_1,6-BISPHOSPHATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY GLYCOLYSIS\r\n",
      "NREGULATION ATP 2.80e-03 10\r\n",
      "COMMNENT From Equilibrator\r\n",
      "k_FORWARD 1.45898263083e-09\r\n",
      "k_REVERSE 1.68336626358e-13\r\n",
      "//\r\n",
      "REACTION\t PYRt2m\r\n",
      "LEFT PYRUVATE\r\n",
      "RIGHT PYRUVATE\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "DGZERO -5.94\r\n",
      "DGZERO-UNITS KJ/MOL\r\n",
      "k_FORWARD 0.219582252566\r\n",
      "k_REVERSE 0.0199938495337\r\n",
      "//\r\n",
      "REACTION\t PDHm\r\n",
      "LEFT COA + NAD+ + PYRUVATE\r\n",
      "RIGHT ACETYL-COA + CO2 + NADH\r\n",
      "LEFT_COMPARTMENT MITOCHONDRIA\r\n",
      "RIGHT_COMPARTMENT MITOCHONDRIA\r\n",
      "ENZYME_LEVEL 1.0\r\n",
      "PATHWAY ACETYL COA BIOSYNTHESIS\r\n",
      "NREGULATION ACETYL-COA 1.00e-03 20\r\n",
      "k_FORWARD 5.97022735957e-10\r\n",
      "k_REVERSE 2.66631445841e-14\r\n",
      "//\r\n",
      "REACTION\t ICL\r\n",
      "LEFT isocitrate\r\n",
      "RIGHT glyoxylate + succinate\r\n",
      "LEFT_COMPARTMENT GLYOXYSOME\r\n",
      "RIGHT_COMPARTMENT GLYOXYSOME\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY glyoxylate shunt\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t MAS\r\n",
      "LEFT acetyl-CoA + glyoxylate + H2O\r\n",
      "RIGHT (S)-MALATE + COA\r\n",
      "LEFT_COMPARTMENT GLYOXYSOME\r\n",
      "RIGHT_COMPARTMENT GLYOXYSOME\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY glyoxylate shunt\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t PYRDC\r\n",
      "LEFT pyruvate\r\n",
      "RIGHT acetaldehyde + CO2\r\n",
      "LEFT_COMPARTMENT CYTOSOL\r\n",
      "RIGHT_COMPARTMENT CYTOSOL\r\n",
      "ENZYME_LEVEL 0.0\r\n",
      "PATHWAY fermentation\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t 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 0.0\r\n",
      "PATHWAY fermentation\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t 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 0.0\r\n",
      "PATHWAY fermentation\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n",
      "REACTION\t 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 0.0\r\n",
      "PATHWAY fermentation\r\n",
      "k_FORWARD 0.0\r\n",
      "k_REVERSE 0.0\r\n",
      "//\r\n"
     ]
    }
   ],
   "source": [
    "cat neurospora_pentose_phos.glycolysis.tca.2_rate.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turning on Regulation and Setting Enzyme Levels\n",
    "To use regulation in the simulation, this flag must be set in the input (*.in) file:\n",
    "\n",
    "USE_REGULATION 1\n",
    "\n",
    "If USE_REGULATION is set to 0, then the regulation information in the .dat file will be ignored.\n",
    "\n",
    "Reactions can be turned on (1.0) or off (0.0) by setting the ENZYME_LEVEL feature in the .dat file for a reaction:\n",
    "\n",
    "USE_ENZYME_LEVELS 1.0 \n",
    "\n",
    "or\n",
    "\n",
    "USE_ENZYME_LEVELS 0.0\n",
    "\n",
    "**In addition, USE_ACTIVITY 1\" must be set in the input (*.in):**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RXN_FILE neurospora_pentose_phos.glycolysis.tca.2_rate.dat\r\n",
      "INIT_FILE neurospora_pentose_phos.glycolysis.tca.2_reg.rstrt\r\n",
      "LOG_FILE neurospora_pentose_phos.glycolysis.tca.2_reg_rate.log\r\n",
      "OUT_FILE neurospora_pentose_phos.glycolysis.tca.2_reg_rate.out\r\n",
      "USE_DEQ 1\r\n",
      "NO_ROUND_FROM_DEQ 1\r\n",
      "ODE_T_FINAL 10000000\r\n",
      "ODE_T_FINAL 10\r\n",
      "ODE_T_FINAL 0.01\r\n",
      "ODE_T_FINAL 1000.0\r\n",
      "DELTA_CONCS_CHOICE 10\r\n",
      "ODE_RXN_VIEW_FREQ 1000\r\n",
      "ODE_RXN_VIEW_FREQ 1\r\n",
      "DERIV_THRESH  5.0e-17\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"
     ]
    }
   ],
   "source": [
    "cat neurospora_pentose_phos.glycolysis.tca_reg_rate.in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models and Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model With Regulation\n",
    "### Run Deterministic Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#args = (\"boltzmann\", \"neurospora_pentose_phos.glycolysis.tca_reg_rate.in\")\n",
    "#Or just:\n",
    "#args = \"bin/bar -c somefile.xml -d text.txt -r aString -f anotherString\".split()\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 kinetic rate law simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(S)-MALATE: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",
       "(S)-MALATE:MITOCHONDRIA                      0.000000e+00\n",
       "2-OXOGLUTARATE:MITOCHONDRIA                  0.000000e+00\n",
       "ACETYL-COA:MITOCHONDRIA                      0.000000e+00\n",
       "ADP:MITOCHONDRIA                             0.000000e+00\n",
       "ATP:MITOCHONDRIA                             0.000000e+00\n",
       "PYRUVATE:CYTOSOL                             0.000000e+00\n",
       "CITRATE:MITOCHONDRIA                         0.000000e+00\n",
       "COA:MITOCHONDRIA                             0.000000e+00\n",
       "FUMARATE:MITOCHONDRIA                        0.000000e+00\n",
       "ISOCITRATE: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",
       "OXALOACETATE:MITOCHONDRIA                    0.000000e+00\n",
       "PYRUVATE:MITOCHONDRIA                        0.000000e+00\n",
       "REDOX1:MITOCHONDRIA                          0.000000e+00\n",
       "REDOX2:MITOCHONDRIA                          0.000000e+00\n",
       "CO2:MITOCHONDRIA                             0.000000e+00\n",
       "PHOSPHOENOLPYRUVATE:CYTOSOL                  0.000000e+00\n",
       "OXALOACETATE:CYTOSOL                         0.000000e+00\n",
       "ORTHOPHOSPHATE:CYTOSOL                       0.000000e+00\n",
       "3-PHOSPHO-D-GLYCERATE:CYTOSOL                0.000000e+00\n",
       "3-PHOSPHO-D-GLYCEROYL_PHOSPHATE: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",
       "ADP:CYTOSOL                                  0.000000e+00\n",
       "ATP:CYTOSOL                                  0.000000e+00\n",
       "BETA-D-GLUCOSE:CYTOSOL                       0.000000e+00\n",
       "BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL           0.000000e+00\n",
       "CO2:CYTOSOL                                  0.000000e+00\n",
       "D-ERYTHROSE-4-PHOSPHATE:CYTOSOL              0.000000e+00\n",
       "D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL          0.000000e+00\n",
       "SUCCINATE:MITOCHONDRIA                       0.000000e+00\n",
       "D-FRUCTOSE_6-PHOSPHATE:CYTOSOL               0.000000e+00\n",
       "D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL         0.000000e+00\n",
       "D-RIBOSE-5-PHOSPHATE:CYTOSOL                 0.000000e+00\n",
       "D-RIBULOSE-5-PHOSPHATE: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",
       "GLYCERONE_PHOSPHATE:CYTOSOL                  0.000000e+00\n",
       "LACTATE:CYTOSOL                              0.000000e+00\n",
       "NAD+:CYTOSOL                                 0.000000e+00\n",
       "NADH:CYTOSOL                                 0.000000e+00\n",
       "NADP+:CYTOSOL                                0.000000e+00\n",
       "NADPH:CYTOSOL                                0.000000e+00\n",
       "D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL    0.000000e+00\n",
       "SUCCINYL-COA:MITOCHONDRIA                    0.000000e+00\n",
       "2-PHOSPHO-D-GLYCERATE:CYTOSOL                5.551115e-17\n",
       "Name: 999.31, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085fd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10905eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read boltzmann derivatives\n",
    "ode_derivatives = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_reg_rate.ode_dconcs',header=0,index_col=0)\n",
    "derivatives = np.abs(ode_derivatives.iloc[-1,:])\n",
    "display(derivatives.sort_values())\n",
    "plt.figure();ode_derivatives.plot(legend=False,logx=True,logy=False,figsize=(10, 10))\n",
    "\n",
    "plt.yscale('symlog')\n",
    "#plt.legend(bbox_to_anchor=(1.35, 1.05),bbox_transform=plt.gcf().transFigure)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward and Reverse Rates from Deterministic Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114c2fe48>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c03a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c2f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read boltzmann ODE reaction odds\n",
    "ode_likelihoods_timeseries = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_reg_rate.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",
    "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_likelihoods.iloc[:,rev_column_idx].plot(legend=True,logx=True,figsize=(12, 12))\n",
    "ode_fwd_likelihoods_timeseries.plot(legend=False,logx=True,figsize=(12, 12))\n",
    "#plt.yscale('symlog')\n",
    "#plt.legend(bbox_to_anchor=(1.02, 0.9),bbox_transform=plt.gcf().transFigure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "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>3.167759</td>\n",
       "      <td>3.099478e-01</td>\n",
       "      <td>2.857811</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACONTm</th>\n",
       "      <td>3.172947</td>\n",
       "      <td>3.151333e-01</td>\n",
       "      <td>2.857814</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICDHxm</th>\n",
       "      <td>2.943089</td>\n",
       "      <td>8.531388e-02</td>\n",
       "      <td>2.857775</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKGDm</th>\n",
       "      <td>2.943124</td>\n",
       "      <td>8.532100e-02</td>\n",
       "      <td>2.857803</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCOASm</th>\n",
       "      <td>3.172931</td>\n",
       "      <td>3.151645e-01</td>\n",
       "      <td>2.857766</td>\n",
       "      <td>0.057143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCD1m</th>\n",
       "      <td>3.172834</td>\n",
       "      <td>3.151739e-01</td>\n",
       "      <td>2.857660</td>\n",
       "      <td>0.057141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMm</th>\n",
       "      <td>3.172531</td>\n",
       "      <td>3.152049e-01</td>\n",
       "      <td>2.857326</td>\n",
       "      <td>0.057135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDHm</th>\n",
       "      <td>3.167089</td>\n",
       "      <td>3.108204e-01</td>\n",
       "      <td>2.856269</td>\n",
       "      <td>0.057113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <td>2.857815</td>\n",
       "      <td>7.730477e-07</td>\n",
       "      <td>2.857814</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <td>3.165479</td>\n",
       "      <td>3.076658e-01</td>\n",
       "      <td>2.857813</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPI</th>\n",
       "      <td>1.428909</td>\n",
       "      <td>1.511411e-06</td>\n",
       "      <td>1.428907</td>\n",
       "      <td>0.028572</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>1.428908</td>\n",
       "      <td>1.511408e-06</td>\n",
       "      <td>1.428906</td>\n",
       "      <td>0.028572</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>2.857812</td>\n",
       "      <td>3.583479e-05</td>\n",
       "      <td>2.857776</td>\n",
       "      <td>0.057144</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>2.995261</td>\n",
       "      <td>1.374769e-01</td>\n",
       "      <td>2.857784</td>\n",
       "      <td>0.057144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <td>2.857838</td>\n",
       "      <td>7.274040e-05</td>\n",
       "      <td>2.857765</td>\n",
       "      <td>0.057143</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>1.428907</td>\n",
       "      <td>1.389075e-11</td>\n",
       "      <td>1.428907</td>\n",
       "      <td>0.028572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <td>1.943454</td>\n",
       "      <td>5.145463e-01</td>\n",
       "      <td>1.428908</td>\n",
       "      <td>0.028572</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>1.428907</td>\n",
       "      <td>1.389073e-11</td>\n",
       "      <td>1.428907</td>\n",
       "      <td>0.028572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRt2m</th>\n",
       "      <td>2.857773</td>\n",
       "      <td>8.044882e-06</td>\n",
       "      <td>2.857765</td>\n",
       "      <td>0.057143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDHm</th>\n",
       "      <td>2.859040</td>\n",
       "      <td>3.883754e-05</td>\n",
       "      <td>2.859001</td>\n",
       "      <td>0.057168</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                   3.167759  3.099478e-01  2.857811           0.057144\n",
       "ACONTm                3.172947  3.151333e-01  2.857814           0.057144\n",
       "ICDHxm                2.943089  8.531388e-02  2.857775           0.057144\n",
       "AKGDm                 2.943124  8.532100e-02  2.857803           0.057144\n",
       "SUCOASm               3.172931  3.151645e-01  2.857766           0.057143\n",
       "SUCD1m                3.172834  3.151739e-01  2.857660           0.057141\n",
       "FUMm                  3.172531  3.152049e-01  2.857326           0.057135\n",
       "MDHm                  3.167089  3.108204e-01  2.856269           0.057113\n",
       "GAPD                  2.857815  7.730477e-07  2.857814           0.057144\n",
       "PGK                   3.165479  3.076658e-01  2.857813           0.057144\n",
       "TPI                   1.428909  1.511411e-06  1.428907           0.028572\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                   1.428908  1.511408e-06  1.428906           0.028572\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                   2.857812  3.583479e-05  2.857776           0.057144\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                   2.995261  1.374769e-01  2.857784           0.057144\n",
       "ENO                   2.857838  7.274040e-05  2.857765           0.057143\n",
       "GND                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "PGL                   0.000000  0.000000e+00  0.000000           0.000000\n",
       "HEX1                  1.428907  1.389075e-11  1.428907           0.028572\n",
       "PGI                   1.943454  5.145463e-01  1.428908           0.028572\n",
       "G6PDH2r               0.000000  0.000000e+00  0.000000           0.000000\n",
       "PFK                   1.428907  1.389073e-11  1.428907           0.028572\n",
       "PYRt2m                2.857773  8.044882e-06  2.857765           0.057143\n",
       "PDHm                  2.859040  3.883754e-05  2.859001           0.057168\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"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ode_likelihoods_steadystate = pd.DataFrame(data = ode_fwd_likelihoods_timeseries.iloc[-20:].mean(), \n",
    "                                           index = ode_fwd_likelihoods_timeseries.columns,\n",
    "                                          columns =['Forward'])\n",
    "ode_likelihoods_steadystate['Reverse'] = ode_rev_likelihoods_timeseries.iloc[-20:].mean()\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",
    "ode_likelihoods_steadystate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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5-phosphate\",\"label_x\":6123.488561328125,\"label_y\":4120.912818359375,\"node_is_primary\":true},\"681144\":{\"node_type\":\"multimarker\",\"x\":6032.225824309936,\"y\":4258.430853910517},\"681145\":{\"node_type\":\"midmarker\",\"x\":6022.863561328125,\"y\":4276.104224609375},\"681146\":{\"node_type\":\"multimarker\",\"x\":6013.501298346314,\"y\":4293.777595308233},\"681147\":{\"node_type\":\"metabolite\",\"x\":5935.892369921875,\"y\":4414.575416015625,\"bigg_id\":\"xu5p__D_c\",\"name\":\"D-Xylulose 5-phosphate\",\"label_x\":5889.451451953125,\"label_y\":4467.9081796875,\"node_is_primary\":true},\"681148\":{\"node_type\":\"multimarker\",\"x\":6171.074452745969,\"y\":4297.5634096560125},\"681149\":{\"node_type\":\"midmarker\",\"x\":6161.477519783975,\"y\":4280.01636210598},\"681150\":{\"node_type\":\"multimarker\",\"x\":6151.8805868219815,\"y\":4262.469314555947},\"681151\":{\"node_type\":\"metabolite\",\"x\":6238.496936481335,\"y\":4412.780211729521,\"bigg_id\":\"r5p_c\",\"name\":\"Alpha-D-Ribose 5-phosphate\",\"label_x\":6268.496936481335,\"label_y\":4422.780211729521,\"node_is_primary\":true},\"681152\":{\"node_type\":\"multimarker\",\"x\":6240.909745978103,\"y\":4594.991455666422},\"681153\":{\"node_type\":\"midmarker\",\"x\":6241.502942788885,\"y\":4614.982656669455},\"681154\":{\"node_type\":\"multimarker\",\"x\":6242.096139599662,\"y\":4634.973857672485},\"681157\":{\"node_type\":\"metabolite\",\"x\":5942.131403716573,\"y\":4744.989314680073,\"bigg_id\":\"s7p_c\",\"name\":\"Sedoheptulose 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": 59,
     "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.compact_Glycolysis_TCA_PPP.json\",reaction_data=reaction_data)\n",
    "b.display_in_notebook(menu='zoom')\n",
    "#b.display_in_browser(menu='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Metabolite Levels from Deterministic Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1147b2f28>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109077780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148b8198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read boltzmann stochastic reaction likelihoods\n",
    "ode_metabolites = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_reg_rate.ode_concs',header=0,index_col=0)\n",
    "\n",
    "plt.figure();ode_metabolites.plot(legend=False,logx=True,logy=True,figsize=(10, 10))\n",
    "plt.legend(bbox_to_anchor=(1.35, 0.95),bbox_transform=plt.gcf().transFigure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>CV</th>\n",
       "      <th>Ratio</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(S)-MALATE:CYTOSOL</th>\n",
       "      <td>1.700000e-03</td>\n",
       "      <td>2.911441e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.023764e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-PHOSPHO-D-GLYCERATE:CYTOSOL</th>\n",
       "      <td>1.198391e-09</td>\n",
       "      <td>1.502514e-40</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>7.216879e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-PHOSPHO-D-GLYCERATE:CYTOSOL</th>\n",
       "      <td>9.246002e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>5.568073e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL</th>\n",
       "      <td>1.915081e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>1.153289e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6-PHOSPHO-D-GLUCONATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETALDEHYDE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP:CYTOSOL</th>\n",
       "      <td>5.600000e-04</td>\n",
       "      <td>2.209576e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.372399e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP:CYTOSOL</th>\n",
       "      <td>9.600000e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.781255e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETA-D-GLUCOSE:CYTOSOL</th>\n",
       "      <td>2.000000e-03</td>\n",
       "      <td>3.959560e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.204428e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>9.827226e-04</td>\n",
       "      <td>5.036467e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.918094e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2:CYTOSOL</th>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>1.933379e-36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-ERYTHROSE-4-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>5.149502e-08</td>\n",
       "      <td>8.951418e-40</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.101103e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL</th>\n",
       "      <td>5.848601e-04</td>\n",
       "      <td>8.462622e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.522110e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-FRUCTOSE_6-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>1.262518e-03</td>\n",
       "      <td>3.920300e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.603061e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>3.700402e-15</td>\n",
       "      <td>1.770225e-46</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>2.228434e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-RIBOSE-5-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>3.189369e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.920683e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-RIBULOSE-5-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.890365e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.740619e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-XYLULOSE:CYTOSOL</th>\n",
       "      <td>8.800000e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.299484e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-XYLULOSE-5-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.320598e-06</td>\n",
       "      <td>8.136115e-38</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.397497e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHANOL:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLYCERONE_PHOSPHATE:CYTOSOL</th>\n",
       "      <td>5.215589e-08</td>\n",
       "      <td>8.837994e-40</td>\n",
       "      <td>1.000001</td>\n",
       "      <td>3.140901e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LACTATE:CYTOSOL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAD+:CYTOSOL</th>\n",
       "      <td>2.600000e-03</td>\n",
       "      <td>3.045815e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.565757e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADH:CYTOSOL</th>\n",
       "      <td>8.300000e-05</td>\n",
       "      <td>9.317489e-36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.998377e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADP+:CYTOSOL</th>\n",
       "      <td>2.100000e-06</td>\n",
       "      <td>8.990787e-38</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.264650e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADPH:CYTOSOL</th>\n",
       "      <td>1.200000e-04</td>\n",
       "      <td>1.611149e-36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.226569e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTHOPHOSPHATE:CYTOSOL</th>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>6.335296e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.204428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OXALOACETATE:CYTOSOL</th>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.979780e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHOSPHOENOLPYRUVATE:CYTOSOL</th>\n",
       "      <td>8.239156e-13</td>\n",
       "      <td>1.302610e-44</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>4.961736e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRUVATE:CYTOSOL</th>\n",
       "      <td>4.321398e-10</td>\n",
       "      <td>6.510478e-42</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>2.602407e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDOHEPTULOSE_7-PHOSPHATE:CYTOSOL</th>\n",
       "      <td>3.035266e-04</td>\n",
       "      <td>1.019155e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.827880e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-MALATE:GLYOXYSOME</th>\n",
       "      <td>1.700000e-03</td>\n",
       "      <td>2.911441e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.023764e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETYL-COA:GLYOXYSOME</th>\n",
       "      <td>2.863050e-03</td>\n",
       "      <td>6.914933e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.724169e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COA:GLYOXYSOME</th>\n",
       "      <td>1.400000e-03</td>\n",
       "      <td>3.181789e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.430997e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLYOXYLATE:GLYOXYSOME</th>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>4.720163e-38</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISOCITRATE:GLYOXYSOME</th>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.979780e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCINATE:GLYOXYSOME</th>\n",
       "      <td>5.700000e-04</td>\n",
       "      <td>2.170811e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.432620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(S)-MALATE:MITOCHONDRIA</th>\n",
       "      <td>1.459541e-03</td>\n",
       "      <td>3.051990e-34</td>\n",
       "      <td>1.000187</td>\n",
       "      <td>8.789561e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-OXOGLUTARATE:MITOCHONDRIA</th>\n",
       "      <td>5.568240e-10</td>\n",
       "      <td>2.021060e-41</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>3.353273e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACETYL-COA:MITOCHONDRIA</th>\n",
       "      <td>6.333631e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999343</td>\n",
       "      <td>3.814202e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP:MITOCHONDRIA</th>\n",
       "      <td>5.600000e-04</td>\n",
       "      <td>2.209576e-35</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.372399e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP:MITOCHONDRIA</th>\n",
       "      <td>9.600000e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.781255e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRATE:MITOCHONDRIA</th>\n",
       "      <td>7.979793e-04</td>\n",
       "      <td>6.202479e-35</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>4.805544e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2:MITOCHONDRIA</th>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>1.933379e-36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COA:MITOCHONDRIA</th>\n",
       "      <td>1.400000e-03</td>\n",
       "      <td>3.181789e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.430997e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMARATE:MITOCHONDRIA</th>\n",
       "      <td>1.078705e-03</td>\n",
       "      <td>4.588326e-35</td>\n",
       "      <td>1.000047</td>\n",
       "      <td>6.496113e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISOCITRATE:MITOCHONDRIA</th>\n",
       "      <td>1.720674e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>1.036214e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAD+:MITOCHONDRIA</th>\n",
       "      <td>2.600000e-03</td>\n",
       "      <td>3.045815e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.565757e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADH:MITOCHONDRIA</th>\n",
       "      <td>8.300000e-05</td>\n",
       "      <td>9.317489e-36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.998377e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADP+:MITOCHONDRIA</th>\n",
       "      <td>2.100000e-06</td>\n",
       "      <td>8.990787e-38</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.264650e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADPH:MITOCHONDRIA</th>\n",
       "      <td>1.200000e-04</td>\n",
       "      <td>1.611149e-36</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.226569e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTHOPHOSPHATE:MITOCHONDRIA</th>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>6.335296e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.204428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OXALOACETATE:MITOCHONDRIA</th>\n",
       "      <td>1.049036e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000641</td>\n",
       "      <td>6.317443e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRUVATE:MITOCHONDRIA</th>\n",
       "      <td>1.848941e-13</td>\n",
       "      <td>3.627890e-45</td>\n",
       "      <td>0.999759</td>\n",
       "      <td>1.113458e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDOX1:MITOCHONDRIA</th>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.979780e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDOX2:MITOCHONDRIA</th>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.979780e-34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.022141e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCINATE:MITOCHONDRIA</th>\n",
       "      <td>3.422563e-03</td>\n",
       "      <td>5.206046e-34</td>\n",
       "      <td>1.000003</td>\n",
       "      <td>2.061116e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCINYL-COA:MITOCHONDRIA</th>\n",
       "      <td>2.915206e-02</td>\n",
       "      <td>4.346380e-34</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>1.755578e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Mean            CV  \\\n",
       "(S)-MALATE:CYTOSOL                         1.700000e-03  2.911441e-35   \n",
       "2-PHOSPHO-D-GLYCERATE:CYTOSOL              1.198391e-09  1.502514e-40   \n",
       "3-PHOSPHO-D-GLYCERATE:CYTOSOL              9.246002e-08  0.000000e+00   \n",
       "3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL    1.915081e-07  0.000000e+00   \n",
       "6-PHOSPHO-D-GLUCONATE:CYTOSOL              0.000000e+00           NaN   \n",
       "ACETALDEHYDE:CYTOSOL                       0.000000e+00           NaN   \n",
       "ACETATE:CYTOSOL                            0.000000e+00           NaN   \n",
       "ADP:CYTOSOL                                5.600000e-04  2.209576e-35   \n",
       "ATP:CYTOSOL                                9.600000e-03  0.000000e+00   \n",
       "BETA-D-GLUCOSE:CYTOSOL                     2.000000e-03  3.959560e-34   \n",
       "BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL         9.827226e-04  5.036467e-35   \n",
       "CO2:CYTOSOL                                1.000000e-04  1.933379e-36   \n",
       "D-ERYTHROSE-4-PHOSPHATE:CYTOSOL            5.149502e-08  8.951418e-40   \n",
       "D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL        5.848601e-04  8.462622e-35   \n",
       "D-FRUCTOSE_6-PHOSPHATE:CYTOSOL             1.262518e-03  3.920300e-35   \n",
       "D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL  0.000000e+00           NaN   \n",
       "D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL       3.700402e-15  1.770225e-46   \n",
       "D-RIBOSE-5-PHOSPHATE:CYTOSOL               3.189369e-07  0.000000e+00   \n",
       "D-RIBULOSE-5-PHOSPHATE:CYTOSOL             2.890365e-07  0.000000e+00   \n",
       "D-XYLULOSE:CYTOSOL                         8.800000e-03  0.000000e+00   \n",
       "D-XYLULOSE-5-PHOSPHATE:CYTOSOL             2.320598e-06  8.136115e-38   \n",
       "ETHANOL:CYTOSOL                            0.000000e+00           NaN   \n",
       "GLYCERONE_PHOSPHATE:CYTOSOL                5.215589e-08  8.837994e-40   \n",
       "LACTATE:CYTOSOL                            0.000000e+00           NaN   \n",
       "NAD+:CYTOSOL                               2.600000e-03  3.045815e-34   \n",
       "NADH:CYTOSOL                               8.300000e-05  9.317489e-36   \n",
       "NADP+:CYTOSOL                              2.100000e-06  8.990787e-38   \n",
       "NADPH:CYTOSOL                              1.200000e-04  1.611149e-36   \n",
       "ORTHOPHOSPHATE:CYTOSOL                     2.000000e-02  6.335296e-34   \n",
       "OXALOACETATE:CYTOSOL                       1.000000e-03  1.979780e-34   \n",
       "PHOSPHOENOLPYRUVATE:CYTOSOL                8.239156e-13  1.302610e-44   \n",
       "PYRUVATE:CYTOSOL                           4.321398e-10  6.510478e-42   \n",
       "SEDOHEPTULOSE_7-PHOSPHATE:CYTOSOL          3.035266e-04  1.019155e-35   \n",
       "(S)-MALATE:GLYOXYSOME                      1.700000e-03  2.911441e-35   \n",
       "ACETYL-COA:GLYOXYSOME                      2.863050e-03  6.914933e-35   \n",
       "COA:GLYOXYSOME                             1.400000e-03  3.181789e-34   \n",
       "GLYOXYLATE:GLYOXYSOME                      1.000000e-06  4.720163e-38   \n",
       "ISOCITRATE:GLYOXYSOME                      1.000000e-03  1.979780e-34   \n",
       "SUCCINATE:GLYOXYSOME                       5.700000e-04  2.170811e-35   \n",
       "(S)-MALATE:MITOCHONDRIA                    1.459541e-03  3.051990e-34   \n",
       "2-OXOGLUTARATE:MITOCHONDRIA                5.568240e-10  2.021060e-41   \n",
       "ACETYL-COA:MITOCHONDRIA                    6.333631e-07  0.000000e+00   \n",
       "ADP:MITOCHONDRIA                           5.600000e-04  2.209576e-35   \n",
       "ATP:MITOCHONDRIA                           9.600000e-03  0.000000e+00   \n",
       "CITRATE:MITOCHONDRIA                       7.979793e-04  6.202479e-35   \n",
       "CO2:MITOCHONDRIA                           1.000000e-04  1.933379e-36   \n",
       "COA:MITOCHONDRIA                           1.400000e-03  3.181789e-34   \n",
       "FUMARATE:MITOCHONDRIA                      1.078705e-03  4.588326e-35   \n",
       "ISOCITRATE:MITOCHONDRIA                    1.720674e-05  0.000000e+00   \n",
       "NAD+:MITOCHONDRIA                          2.600000e-03  3.045815e-34   \n",
       "NADH:MITOCHONDRIA                          8.300000e-05  9.317489e-36   \n",
       "NADP+:MITOCHONDRIA                         2.100000e-06  8.990787e-38   \n",
       "NADPH:MITOCHONDRIA                         1.200000e-04  1.611149e-36   \n",
       "ORTHOPHOSPHATE:MITOCHONDRIA                2.000000e-02  6.335296e-34   \n",
       "OXALOACETATE:MITOCHONDRIA                  1.049036e-07  0.000000e+00   \n",
       "PYRUVATE:MITOCHONDRIA                      1.848941e-13  3.627890e-45   \n",
       "REDOX1:MITOCHONDRIA                        1.000000e-03  1.979780e-34   \n",
       "REDOX2:MITOCHONDRIA                        1.000000e-03  1.979780e-34   \n",
       "SUCCINATE:MITOCHONDRIA                     3.422563e-03  5.206046e-34   \n",
       "SUCCINYL-COA:MITOCHONDRIA                  2.915206e-02  4.346380e-34   \n",
       "\n",
       "                                              Ratio        Counts  \n",
       "(S)-MALATE:CYTOSOL                         1.000000  1.023764e+06  \n",
       "2-PHOSPHO-D-GLYCERATE:CYTOSOL              0.999983  7.216879e-01  \n",
       "3-PHOSPHO-D-GLYCERATE:CYTOSOL              0.999983  5.568073e+01  \n",
       "3-PHOSPHO-D-GLYCEROYL_PHOSPHATE:CYTOSOL    0.999986  1.153289e+02  \n",
       "6-PHOSPHO-D-GLUCONATE:CYTOSOL                   NaN  0.000000e+00  \n",
       "ACETALDEHYDE:CYTOSOL                            NaN  0.000000e+00  \n",
       "ACETATE:CYTOSOL                                 NaN  0.000000e+00  \n",
       "ADP:CYTOSOL                                1.000000  3.372399e+05  \n",
       "ATP:CYTOSOL                                1.000000  5.781255e+06  \n",
       "BETA-D-GLUCOSE:CYTOSOL                     1.000000  1.204428e+06  \n",
       "BETA-D-GLUCOSE-6-PHOSPHATE:CYTOSOL         1.000000  5.918094e+05  \n",
       "CO2:CYTOSOL                                1.000000  6.022141e+04  \n",
       "D-ERYTHROSE-4-PHOSPHATE:CYTOSOL            1.000000  3.101103e+01  \n",
       "D-FRUCTOSE_1,6-BISPHOSPHATE:CYTOSOL        1.000000  3.522110e+05  \n",
       "D-FRUCTOSE_6-PHOSPHATE:CYTOSOL             1.000000  7.603061e+05  \n",
       "D-GLUCONO-1,5-LACTONE_6-PHOSPHATE:CYTOSOL       NaN  0.000000e+00  \n",
       "D-GLYCERALDEHYDE-3-PHOSPHATE:CYTOSOL       0.999995  2.228434e-06  \n",
       "D-RIBOSE-5-PHOSPHATE:CYTOSOL               1.000000  1.920683e+02  \n",
       "D-RIBULOSE-5-PHOSPHATE:CYTOSOL             1.000000  1.740619e+02  \n",
       "D-XYLULOSE:CYTOSOL                         1.000000  5.299484e+06  \n",
       "D-XYLULOSE-5-PHOSPHATE:CYTOSOL             1.000000  1.397497e+03  \n",
       "ETHANOL:CYTOSOL                                 NaN  0.000000e+00  \n",
       "GLYCERONE_PHOSPHATE:CYTOSOL                1.000001  3.140901e+01  \n",
       "LACTATE:CYTOSOL                                 NaN  0.000000e+00  \n",
       "NAD+:CYTOSOL                               1.000000  1.565757e+06  \n",
       "NADH:CYTOSOL                               1.000000  4.998377e+04  \n",
       "NADP+:CYTOSOL                              1.000000  1.264650e+03  \n",
       "NADPH:CYTOSOL                              1.000000  7.226569e+04  \n",
       "ORTHOPHOSPHATE:CYTOSOL                     1.000000  1.204428e+07  \n",
       "OXALOACETATE:CYTOSOL                       1.000000  6.022141e+05  \n",
       "PHOSPHOENOLPYRUVATE:CYTOSOL                0.999983  4.961736e-04  \n",
       "PYRUVATE:CYTOSOL                           0.999983  2.602407e-01  \n",
       "SEDOHEPTULOSE_7-PHOSPHATE:CYTOSOL          1.000000  1.827880e+05  \n",
       "(S)-MALATE:GLYOXYSOME                      1.000000  1.023764e+06  \n",
       "ACETYL-COA:GLYOXYSOME                      1.000000  1.724169e+06  \n",
       "COA:GLYOXYSOME                             1.000000  8.430997e+05  \n",
       "GLYOXYLATE:GLYOXYSOME                      1.000000  6.022141e+02  \n",
       "ISOCITRATE:GLYOXYSOME                      1.000000  6.022141e+05  \n",
       "SUCCINATE:GLYOXYSOME                       1.000000  3.432620e+05  \n",
       "(S)-MALATE:MITOCHONDRIA                    1.000187  8.789561e+05  \n",
       "2-OXOGLUTARATE:MITOCHONDRIA                0.999985  3.353273e-01  \n",
       "ACETYL-COA:MITOCHONDRIA                    0.999343  3.814202e+02  \n",
       "ADP:MITOCHONDRIA                           1.000000  3.372399e+05  \n",
       "ATP:MITOCHONDRIA                           1.000000  5.781255e+06  \n",
       "CITRATE:MITOCHONDRIA                       0.999983  4.805544e+05  \n",
       "CO2:MITOCHONDRIA                           1.000000  6.022141e+04  \n",
       "COA:MITOCHONDRIA                           1.000000  8.430997e+05  \n",
       "FUMARATE:MITOCHONDRIA                      1.000047  6.496113e+05  \n",
       "ISOCITRATE:MITOCHONDRIA                    0.999983  1.036214e+04  \n",
       "NAD+:MITOCHONDRIA                          1.000000  1.565757e+06  \n",
       "NADH:MITOCHONDRIA                          1.000000  4.998377e+04  \n",
       "NADP+:MITOCHONDRIA                         1.000000  1.264650e+03  \n",
       "NADPH:MITOCHONDRIA                         1.000000  7.226569e+04  \n",
       "ORTHOPHOSPHATE:MITOCHONDRIA                1.000000  1.204428e+07  \n",
       "OXALOACETATE:MITOCHONDRIA                  1.000641  6.317443e+01  \n",
       "PYRUVATE:MITOCHONDRIA                      0.999759  1.113458e-04  \n",
       "REDOX1:MITOCHONDRIA                        1.000000  6.022141e+05  \n",
       "REDOX2:MITOCHONDRIA                        1.000000  6.022141e+05  \n",
       "SUCCINATE:MITOCHONDRIA                     1.000003  2.061116e+06  \n",
       "SUCCINYL-COA:MITOCHONDRIA                  0.999989  1.755578e+07  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ode_metabolites_steadystate = pd.DataFrame()\n",
    "ode_metabolites_steadystate = \\\n",
    "  pd.DataFrame(data = ode_metabolites.iloc[-20:,:].mean(axis=0),columns=['Mean'])\n",
    "ode_metabolites_steadystate['CV'] = \\\n",
    "  pd.DataFrame(data = ode_metabolites.iloc[-20:,:].var(axis=0)/ode_metabolites.iloc[-20:,:].mean(axis=0),columns=['ODE'])\n",
    "ode_metabolites_steadystate['Ratio'] = \\\n",
    "  pd.DataFrame(data =(ode_metabolites.iloc[-1]/ode_metabolites.iloc[0]))\n",
    "ode_metabolites_steadystate['Counts'] = ode_metabolites_steadystate['Mean']*Concentration2Count\n",
    "display(ode_metabolites_steadystate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reaction Rates from Kinetic Rate Law\n",
    "Check to see that the rates calculated from the usual mass action rate law using rate constants is the same as the rates calculated above from thermodynamic odds.\n",
    "\n",
    "Since the rate constants were determined from the net reaction probabilities determined from the maximum entropy distribution modified by including regulation, the flux values from the kinetic rate law calculated below should be compared to those net reaction probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "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>LEFT</th>\n",
       "      <th>RIGHT</th>\n",
       "      <th>LEFT_COMPARTMENT</th>\n",
       "      <th>RIGHT_COMPARTMENT</th>\n",
       "      <th>ENZYME_LEVEL</th>\n",
       "      <th>k_FORWARD</th>\n",
       "      <th>k_REVERSE</th>\n",
       "      <th>NREGULATION</th>\n",
       "      <th>Fwd Rate</th>\n",
       "      <th>Rev Rate</th>\n",
       "      <th>Net Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ME1m</th>\n",
       "      <td>(S)-MALATE + NAD+</td>\n",
       "      <td>pyruvate + NADH + CO2</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME2m</th>\n",
       "      <td>(S)-MALATE + NADP+</td>\n",
       "      <td>PYRUVATE + NADPH + CO2</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSm</th>\n",
       "      <td>OXALOACETATE + ACETYL-COA + H2O</td>\n",
       "      <td>CITRATE + COA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.465382e-06</td>\n",
       "      <td>6.506103e-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083502</td>\n",
       "      <td>2.635982e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACONTm</th>\n",
       "      <td>CITRATE</td>\n",
       "      <td>ISOCITRATE</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.736230e-07</td>\n",
       "      <td>2.537439e-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083435</td>\n",
       "      <td>2.629331e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICDHxm</th>\n",
       "      <td>ISOCITRATE + NAD+</td>\n",
       "      <td>2-OXOGLUTARATE + NADH + CO2</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.850469e-12</td>\n",
       "      <td>5.280809e-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062472</td>\n",
       "      <td>5.330270e-03</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKGDm</th>\n",
       "      <td>2-OXOGLUTARATE + COA + NAD+</td>\n",
       "      <td>SUCCINYL-COA + CO2 + NADH</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.955165e-13</td>\n",
       "      <td>5.564560e-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.086548</td>\n",
       "      <td>2.940566e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCOASm</th>\n",
       "      <td>SUCCINYL-COA + ADP + Orthophosphate</td>\n",
       "      <td>SUCCINATE + ATP + COA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.170119e-21</td>\n",
       "      <td>2.617611e-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083439</td>\n",
       "      <td>2.629715e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCD1m</th>\n",
       "      <td>SUCCINATE + redox1</td>\n",
       "      <td>FUMARATE + redox2</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.722371e-14</td>\n",
       "      <td>6.722371e-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083440</td>\n",
       "      <td>2.629826e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMm</th>\n",
       "      <td>FUMARATE + H2O</td>\n",
       "      <td>(S)-MALATE</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.284522e-07</td>\n",
       "      <td>2.992414e-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083444</td>\n",
       "      <td>2.630200e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDHm</th>\n",
       "      <td>(S)-MALATE + NAD+</td>\n",
       "      <td>OXALOACETATE + NADH</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.024654e-14</td>\n",
       "      <td>8.161378e-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082913</td>\n",
       "      <td>2.577115e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <td>D-GLYCERALDEHYDE-3-PHOSPHATE + ORTHOPHOSPHATE ...</td>\n",
       "      <td>3-Phospho-D-glyceroyl_phosphate + NADH</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.081961e-09</td>\n",
       "      <td>5.265243e-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087494</td>\n",
       "      <td>3.035187e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <td>3-Phospho-D-glyceroyl_phosphate + ADP</td>\n",
       "      <td>3-PHOSPHO-D-GLYCERATE + ATP</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.130296e-09</td>\n",
       "      <td>7.987666e-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082855</td>\n",
       "      <td>2.571266e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPI</th>\n",
       "      <td>Glycerone_phosphate</td>\n",
       "      <td>D-GLYCERALDEHYDE-3-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.096450e-04</td>\n",
       "      <td>1.999496e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>4.455746e-08</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDH</th>\n",
       "      <td>(S)-MALATE + NAD+</td>\n",
       "      <td>OXALOACETATE + NADH</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEP_Carboxylase</th>\n",
       "      <td>oxaloacetate + orthophosphate</td>\n",
       "      <td>phosphoenolpyruvate + CO2 + H2O</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPCK</th>\n",
       "      <td>oxaloacetate + ATP</td>\n",
       "      <td>phosphoenolpyruvate + ADP + CO2</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBA</th>\n",
       "      <td>D-FRUCTOSE_1,6-BISPHOSPHATE</td>\n",
       "      <td>Glycerone_phosphate + D-GLYCERALDEHYDE-3-PHOSP...</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.111913e-08</td>\n",
       "      <td>6.169572e-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>4.318263e-08</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBP</th>\n",
       "      <td>D-FRUCTOSE_6-PHOSPHATE + Orthophosphate</td>\n",
       "      <td>H2O + D-FRUCTOSE_1,6-BISPHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKT2</th>\n",
       "      <td>D-FRUCTOSE_6-PHOSPHATE + D-GLYCERALDEHYDE-3-PH...</td>\n",
       "      <td>D-ERYTHROSE-4-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPE</th>\n",
       "      <td>D-XYLULOSE-5-PHOSPHATE</td>\n",
       "      <td>D-RIBULOSE-5-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylulokinase</th>\n",
       "      <td>D-XYLULOSE + ATP</td>\n",
       "      <td>D-XYLULOSE-5-PHOSPHATE + ADP</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK_org</th>\n",
       "      <td>ADP + PHOSPHOENOLPYRUVATE</td>\n",
       "      <td>PYRUVATE + ATP</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK</th>\n",
       "      <td>ADP + PHOSPHOENOLPYRUVATE</td>\n",
       "      <td>PYRUVATE + ATP</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.680916e-04</td>\n",
       "      <td>2.958144e-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061593</td>\n",
       "      <td>4.450580e-03</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>D-RIBOSE-5-PHOSPHATE</td>\n",
       "      <td>D-RIBULOSE-5-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKT1</th>\n",
       "      <td>SEDOHEPTULOSE_7-PHOSPHATE + D-GLYCERALDEHYDE-3...</td>\n",
       "      <td>D-RIBOSE-5-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TALA</th>\n",
       "      <td>D-GLYCERALDEHYDE-3-PHOSPHATE + SEDOHEPTULOSE_7...</td>\n",
       "      <td>D-FRUCTOSE_6-PHOSPHATE + D-ERYTHROSE-4-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGM</th>\n",
       "      <td>3-PHOSPHO-D-GLYCERATE</td>\n",
       "      <td>2-PHOSPHO-D-GLYCERATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.193232e-03</td>\n",
       "      <td>1.288370e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066440</td>\n",
       "      <td>9.298012e-03</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <td>2-PHOSPHO-D-GLYCERATE</td>\n",
       "      <td>PHOSPHOENOLPYRUVATE + H2O</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.919202e-02</td>\n",
       "      <td>1.998839e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057152</td>\n",
       "      <td>9.917713e-06</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GND</th>\n",
       "      <td>NADPH + D-RIBULOSE-5-PHOSPHATE + CO2</td>\n",
       "      <td>NADP+ + 6-PHOSPHO-D-GLUCONATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGL</th>\n",
       "      <td>6-PHOSPHO-D-GLUCONATE</td>\n",
       "      <td>D-Glucono-1,5-lactone_6-phosphate + H2O</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEX1</th>\n",
       "      <td>BETA-D-GLUCOSE + ATP</td>\n",
       "      <td>BETA-D-GLUCOSE-6-PHOSPHATE + ADP</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.209961e-10</td>\n",
       "      <td>1.001844e-13</td>\n",
       "      <td>ATP 2.80e-03 10</td>\n",
       "      <td>6412.994026</td>\n",
       "      <td>1.999498e-02</td>\n",
       "      <td>6412.974031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <td>BETA-D-GLUCOSE-6-PHOSPHATE</td>\n",
       "      <td>D-FRUCTOSE_6-PHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.944815e-08</td>\n",
       "      <td>3.983045e-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.058854</td>\n",
       "      <td>3.028333e-02</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6PDH2r</th>\n",
       "      <td>BETA-D-GLUCOSE-6-PHOSPHATE + NADP+</td>\n",
       "      <td>D-Glucono-1,5-lactone_6-phosphate + NADPH</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFK</th>\n",
       "      <td>D-FRUCTOSE_6-PHOSPHATE + ATP</td>\n",
       "      <td>ADP + D-FRUCTOSE_1,6-BISPHOSPHATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.458983e-09</td>\n",
       "      <td>1.683366e-13</td>\n",
       "      <td>ATP 2.80e-03 10</td>\n",
       "      <td>6412.992797</td>\n",
       "      <td>1.999496e-02</td>\n",
       "      <td>6412.972802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRt2m</th>\n",
       "      <td>PYRUVATE</td>\n",
       "      <td>PYRUVATE</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.195823e-01</td>\n",
       "      <td>1.999385e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>2.226232e-06</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDHm</th>\n",
       "      <td>COA + NAD+ + PYRUVATE</td>\n",
       "      <td>ACETYL-COA + CO2 + NADH</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>MITOCHONDRIA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.970227e-10</td>\n",
       "      <td>2.666314e-14</td>\n",
       "      <td>ACETYL-COA 1.00e-03 20</td>\n",
       "      <td>0.087754</td>\n",
       "      <td>3.061223e-02</td>\n",
       "      <td>0.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICL</th>\n",
       "      <td>isocitrate</td>\n",
       "      <td>glyoxylate + succinate</td>\n",
       "      <td>GLYOXYSOME</td>\n",
       "      <td>GLYOXYSOME</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAS</th>\n",
       "      <td>acetyl-CoA + glyoxylate + H2O</td>\n",
       "      <td>(S)-MALATE + COA</td>\n",
       "      <td>GLYOXYSOME</td>\n",
       "      <td>GLYOXYSOME</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRDC</th>\n",
       "      <td>pyruvate</td>\n",
       "      <td>acetaldehyde + CO2</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALDD2y</th>\n",
       "      <td>acetaldehyde + NADP+ + H2O</td>\n",
       "      <td>acetate + NADPH</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALCD2X_copy1</th>\n",
       "      <td>acetaldehyde + NADH</td>\n",
       "      <td>ethanol + NAD+</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LactateDehydrogenase</th>\n",
       "      <td>pyruvate + NADH</td>\n",
       "      <td>lactate + NAD+</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>CYTOSOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   LEFT  \\\n",
       "ME1m                                                  (S)-MALATE + NAD+   \n",
       "ME2m                                                 (S)-MALATE + NADP+   \n",
       "CSm                                     OXALOACETATE + ACETYL-COA + H2O   \n",
       "ACONTm                                                          CITRATE   \n",
       "ICDHxm                                                ISOCITRATE + NAD+   \n",
       "AKGDm                                       2-OXOGLUTARATE + COA + NAD+   \n",
       "SUCOASm                             SUCCINYL-COA + ADP + Orthophosphate   \n",
       "SUCD1m                                               SUCCINATE + redox1   \n",
       "FUMm                                                     FUMARATE + H2O   \n",
       "MDHm                                                  (S)-MALATE + NAD+   \n",
       "GAPD                  D-GLYCERALDEHYDE-3-PHOSPHATE + ORTHOPHOSPHATE ...   \n",
       "PGK                               3-Phospho-D-glyceroyl_phosphate + ADP   \n",
       "TPI                                                 Glycerone_phosphate   \n",
       "MDH                                                   (S)-MALATE + NAD+   \n",
       "PEP_Carboxylase                           oxaloacetate + orthophosphate   \n",
       "PPCK                                                 oxaloacetate + ATP   \n",
       "FBA                                         D-FRUCTOSE_1,6-BISPHOSPHATE   \n",
       "FBP                             D-FRUCTOSE_6-PHOSPHATE + Orthophosphate   \n",
       "TKT2                  D-FRUCTOSE_6-PHOSPHATE + D-GLYCERALDEHYDE-3-PH...   \n",
       "RPE                                              D-XYLULOSE-5-PHOSPHATE   \n",
       "Xylulokinase                                           D-XYLULOSE + ATP   \n",
       "PYK_org                                       ADP + PHOSPHOENOLPYRUVATE   \n",
       "PYK                                           ADP + PHOSPHOENOLPYRUVATE   \n",
       "RPI                                                D-RIBOSE-5-PHOSPHATE   \n",
       "TKT1                  SEDOHEPTULOSE_7-PHOSPHATE + D-GLYCERALDEHYDE-3...   \n",
       "TALA                  D-GLYCERALDEHYDE-3-PHOSPHATE + SEDOHEPTULOSE_7...   \n",
       "PGM                                               3-PHOSPHO-D-GLYCERATE   \n",
       "ENO                                               2-PHOSPHO-D-GLYCERATE   \n",
       "GND                                NADPH + D-RIBULOSE-5-PHOSPHATE + CO2   \n",
       "PGL                                               6-PHOSPHO-D-GLUCONATE   \n",
       "HEX1                                               BETA-D-GLUCOSE + ATP   \n",
       "PGI                                          BETA-D-GLUCOSE-6-PHOSPHATE   \n",
       "G6PDH2r                              BETA-D-GLUCOSE-6-PHOSPHATE + NADP+   \n",
       "PFK                                        D-FRUCTOSE_6-PHOSPHATE + ATP   \n",
       "PYRt2m                                                         PYRUVATE   \n",
       "PDHm                                              COA + NAD+ + PYRUVATE   \n",
       "ICL                                                          isocitrate   \n",
       "MAS                                       acetyl-CoA + glyoxylate + H2O   \n",
       "PYRDC                                                          pyruvate   \n",
       "ALDD2y                                       acetaldehyde + NADP+ + H2O   \n",
       "ALCD2X_copy1                                        acetaldehyde + NADH   \n",
       "LactateDehydrogenase                                    pyruvate + NADH   \n",
       "\n",
       "                                                                  RIGHT  \\\n",
       "ME1m                                              pyruvate + NADH + CO2   \n",
       "ME2m                                             PYRUVATE + NADPH + CO2   \n",
       "CSm                                                       CITRATE + COA   \n",
       "ACONTm                                                       ISOCITRATE   \n",
       "ICDHxm                                      2-OXOGLUTARATE + NADH + CO2   \n",
       "AKGDm                                         SUCCINYL-COA + CO2 + NADH   \n",
       "SUCOASm                                           SUCCINATE + ATP + COA   \n",
       "SUCD1m                                                FUMARATE + redox2   \n",
       "FUMm                                                         (S)-MALATE   \n",
       "MDHm                                                OXALOACETATE + NADH   \n",
       "GAPD                             3-Phospho-D-glyceroyl_phosphate + NADH   \n",
       "PGK                                         3-PHOSPHO-D-GLYCERATE + ATP   \n",
       "TPI                                        D-GLYCERALDEHYDE-3-PHOSPHATE   \n",
       "MDH                                                 OXALOACETATE + NADH   \n",
       "PEP_Carboxylase                         phosphoenolpyruvate + CO2 + H2O   \n",
       "PPCK                                    phosphoenolpyruvate + ADP + CO2   \n",
       "FBA                   Glycerone_phosphate + D-GLYCERALDEHYDE-3-PHOSP...   \n",
       "FBP                                   H2O + D-FRUCTOSE_1,6-BISPHOSPHATE   \n",
       "TKT2                   D-ERYTHROSE-4-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE   \n",
       "RPE                                              D-RIBULOSE-5-PHOSPHATE   \n",
       "Xylulokinase                               D-XYLULOSE-5-PHOSPHATE + ADP   \n",
       "PYK_org                                                  PYRUVATE + ATP   \n",
       "PYK                                                      PYRUVATE + ATP   \n",
       "RPI                                              D-RIBULOSE-5-PHOSPHATE   \n",
       "TKT1                      D-RIBOSE-5-PHOSPHATE + D-XYLULOSE-5-PHOSPHATE   \n",
       "TALA                   D-FRUCTOSE_6-PHOSPHATE + D-ERYTHROSE-4-PHOSPHATE   \n",
       "PGM                                               2-PHOSPHO-D-GLYCERATE   \n",
       "ENO                                           PHOSPHOENOLPYRUVATE + H2O   \n",
       "GND                                       NADP+ + 6-PHOSPHO-D-GLUCONATE   \n",
       "PGL                             D-Glucono-1,5-lactone_6-phosphate + H2O   \n",
       "HEX1                                   BETA-D-GLUCOSE-6-PHOSPHATE + ADP   \n",
       "PGI                                              D-FRUCTOSE_6-PHOSPHATE   \n",
       "G6PDH2r                       D-Glucono-1,5-lactone_6-phosphate + NADPH   \n",
       "PFK                                   ADP + D-FRUCTOSE_1,6-BISPHOSPHATE   \n",
       "PYRt2m                                                         PYRUVATE   \n",
       "PDHm                                            ACETYL-COA + CO2 + NADH   \n",
       "ICL                                              glyoxylate + succinate   \n",
       "MAS                                                    (S)-MALATE + COA   \n",
       "PYRDC                                                acetaldehyde + CO2   \n",
       "ALDD2y                                                  acetate + NADPH   \n",
       "ALCD2X_copy1                                             ethanol + NAD+   \n",
       "LactateDehydrogenase                                     lactate + NAD+   \n",
       "\n",
       "                     LEFT_COMPARTMENT RIGHT_COMPARTMENT ENZYME_LEVEL  \\\n",
       "ME1m                     MITOCHONDRIA      MITOCHONDRIA          0.0   \n",
       "ME2m                     MITOCHONDRIA      MITOCHONDRIA          0.0   \n",
       "CSm                      MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "ACONTm                   MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "ICDHxm                   MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "AKGDm                    MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "SUCOASm                  MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "SUCD1m                   MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "FUMm                     MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "MDHm                     MITOCHONDRIA      MITOCHONDRIA          NaN   \n",
       "GAPD                          CYTOSOL           CYTOSOL          1.0   \n",
       "PGK                           CYTOSOL           CYTOSOL          1.0   \n",
       "TPI                           CYTOSOL           CYTOSOL          1.0   \n",
       "MDH                           CYTOSOL           CYTOSOL          0.0   \n",
       "PEP_Carboxylase               CYTOSOL           CYTOSOL          0.0   \n",
       "PPCK                          CYTOSOL           CYTOSOL          0.0   \n",
       "FBA                           CYTOSOL           CYTOSOL          1.0   \n",
       "FBP                           CYTOSOL           CYTOSOL          0.0   \n",
       "TKT2                          CYTOSOL           CYTOSOL          0.0   \n",
       "RPE                           CYTOSOL           CYTOSOL          0.0   \n",
       "Xylulokinase                  CYTOSOL           CYTOSOL          0.0   \n",
       "PYK_org                       CYTOSOL           CYTOSOL          0.0   \n",
       "PYK                           CYTOSOL           CYTOSOL          1.0   \n",
       "RPI                           CYTOSOL           CYTOSOL          0.0   \n",
       "TKT1                          CYTOSOL           CYTOSOL          0.0   \n",
       "TALA                          CYTOSOL           CYTOSOL          0.0   \n",
       "PGM                           CYTOSOL           CYTOSOL          1.0   \n",
       "ENO                           CYTOSOL           CYTOSOL          1.0   \n",
       "GND                           CYTOSOL           CYTOSOL          0.0   \n",
       "PGL                           CYTOSOL           CYTOSOL          0.0   \n",
       "HEX1                          CYTOSOL           CYTOSOL          1.0   \n",
       "PGI                           CYTOSOL           CYTOSOL          1.0   \n",
       "G6PDH2r                       CYTOSOL           CYTOSOL          0.0   \n",
       "PFK                           CYTOSOL           CYTOSOL          1.0   \n",
       "PYRt2m                        CYTOSOL      MITOCHONDRIA          1.0   \n",
       "PDHm                     MITOCHONDRIA      MITOCHONDRIA          1.0   \n",
       "ICL                        GLYOXYSOME        GLYOXYSOME          0.0   \n",
       "MAS                        GLYOXYSOME        GLYOXYSOME          0.0   \n",
       "PYRDC                         CYTOSOL           CYTOSOL          0.0   \n",
       "ALDD2y                        CYTOSOL           CYTOSOL          0.0   \n",
       "ALCD2X_copy1                  CYTOSOL           CYTOSOL          0.0   \n",
       "LactateDehydrogenase          CYTOSOL           CYTOSOL          0.0   \n",
       "\n",
       "                         k_FORWARD     k_REVERSE             NREGULATION  \\\n",
       "ME1m                  0.000000e+00  0.000000e+00                     NaN   \n",
       "ME2m                  0.000000e+00  0.000000e+00                     NaN   \n",
       "CSm                   3.465382e-06  6.506103e-14                     NaN   \n",
       "ACONTm                1.736230e-07  2.537439e-06                     NaN   \n",
       "ICDHxm                3.850469e-12  5.280809e-12                     NaN   \n",
       "AKGDm                 1.955165e-13  5.564560e-19                     NaN   \n",
       "SUCOASm               1.170119e-21  2.617611e-21                     NaN   \n",
       "SUCD1m                6.722371e-14  6.722371e-14                     NaN   \n",
       "FUMm                  1.284522e-07  2.992414e-08                     NaN   \n",
       "MDHm                  6.024654e-14  8.161378e-09                     NaN   \n",
       "GAPD                  2.081961e-09  5.265243e-09                     NaN   \n",
       "PGK                   2.130296e-09  7.987666e-11                     NaN   \n",
       "TPI                   9.096450e-04  1.999496e-02                     NaN   \n",
       "MDH                   0.000000e+00  0.000000e+00                     NaN   \n",
       "PEP_Carboxylase       0.000000e+00  0.000000e+00                     NaN   \n",
       "PPCK                  0.000000e+00  0.000000e+00                     NaN   \n",
       "FBA                   8.111913e-08  6.169572e-04                     NaN   \n",
       "FBP                   0.000000e+00  0.000000e+00                     NaN   \n",
       "TKT2                  0.000000e+00  0.000000e+00                     NaN   \n",
       "RPE                   0.000000e+00  0.000000e+00                     NaN   \n",
       "Xylulokinase          0.000000e+00  0.000000e+00                     NaN   \n",
       "PYK_org               0.000000e+00  0.000000e+00                     NaN   \n",
       "PYK                   3.680916e-04  2.958144e-09                     NaN   \n",
       "RPI                   0.000000e+00  0.000000e+00                     NaN   \n",
       "TKT1                  0.000000e+00  0.000000e+00                     NaN   \n",
       "TALA                  0.000000e+00  0.000000e+00                     NaN   \n",
       "PGM                   1.193232e-03  1.288370e-02                     NaN   \n",
       "ENO                   7.919202e-02  1.998839e-02                     NaN   \n",
       "GND                   0.000000e+00  0.000000e+00                     NaN   \n",
       "PGL                   0.000000e+00  0.000000e+00                     NaN   \n",
       "HEX1                  9.209961e-10  1.001844e-13         ATP 2.80e-03 10   \n",
       "PGI                   9.944815e-08  3.983045e-08                     NaN   \n",
       "G6PDH2r               0.000000e+00  0.000000e+00                     NaN   \n",
       "PFK                   1.458983e-09  1.683366e-13         ATP 2.80e-03 10   \n",
       "PYRt2m                2.195823e-01  1.999385e-02                     NaN   \n",
       "PDHm                  5.970227e-10  2.666314e-14  ACETYL-COA 1.00e-03 20   \n",
       "ICL                   0.000000e+00  0.000000e+00                     NaN   \n",
       "MAS                   0.000000e+00  0.000000e+00                     NaN   \n",
       "PYRDC                 0.000000e+00  0.000000e+00                     NaN   \n",
       "ALDD2y                0.000000e+00  0.000000e+00                     NaN   \n",
       "ALCD2X_copy1          0.000000e+00  0.000000e+00                     NaN   \n",
       "LactateDehydrogenase  0.000000e+00  0.000000e+00                     NaN   \n",
       "\n",
       "                         Fwd Rate      Rev Rate     Net Rate  \n",
       "ME1m                     0.000000  0.000000e+00     0.000000  \n",
       "ME2m                     0.000000  0.000000e+00     0.000000  \n",
       "CSm                      0.083502  2.635982e-02     0.057142  \n",
       "ACONTm                   0.083435  2.629331e-02     0.057142  \n",
       "ICDHxm                   0.062472  5.330270e-03     0.057142  \n",
       "AKGDm                    0.086548  2.940566e-02     0.057142  \n",
       "SUCOASm                  0.083439  2.629715e-02     0.057142  \n",
       "SUCD1m                   0.083440  2.629826e-02     0.057142  \n",
       "FUMm                     0.083444  2.630200e-02     0.057142  \n",
       "MDHm                     0.082913  2.577115e-02     0.057142  \n",
       "GAPD                     0.087494  3.035187e-02     0.057142  \n",
       "PGK                      0.082855  2.571266e-02     0.057142  \n",
       "TPI                      0.028571  4.455746e-08     0.028571  \n",
       "MDH                      0.000000  0.000000e+00     0.000000  \n",
       "PEP_Carboxylase          0.000000  0.000000e+00     0.000000  \n",
       "PPCK                     0.000000  0.000000e+00     0.000000  \n",
       "FBA                      0.028571  4.318263e-08     0.028571  \n",
       "FBP                      0.000000  0.000000e+00     0.000000  \n",
       "TKT2                     0.000000  0.000000e+00     0.000000  \n",
       "RPE                      0.000000  0.000000e+00     0.000000  \n",
       "Xylulokinase             0.000000  0.000000e+00     0.000000  \n",
       "PYK_org                  0.000000  0.000000e+00     0.000000  \n",
       "PYK                      0.061593  4.450580e-03     0.057142  \n",
       "RPI                      0.000000  0.000000e+00     0.000000  \n",
       "TKT1                     0.000000  0.000000e+00     0.000000  \n",
       "TALA                     0.000000  0.000000e+00     0.000000  \n",
       "PGM                      0.066440  9.298012e-03     0.057142  \n",
       "ENO                      0.057152  9.917713e-06     0.057142  \n",
       "GND                      0.000000  0.000000e+00     0.000000  \n",
       "PGL                      0.000000  0.000000e+00     0.000000  \n",
       "HEX1                  6412.994026  1.999498e-02  6412.974031  \n",
       "PGI                      0.058854  3.028333e-02     0.028571  \n",
       "G6PDH2r                  0.000000  0.000000e+00     0.000000  \n",
       "PFK                   6412.992797  1.999496e-02  6412.972802  \n",
       "PYRt2m                   0.057144  2.226232e-06     0.057142  \n",
       "PDHm                     0.087754  3.061223e-02     0.057142  \n",
       "ICL                      0.000000  0.000000e+00     0.000000  \n",
       "MAS                      0.000000  0.000000e+00     0.000000  \n",
       "PYRDC                    0.000000  0.000000e+00     0.000000  \n",
       "ALDD2y                   0.000000  0.000000e+00     0.000000  \n",
       "ALCD2X_copy1             0.000000  0.000000e+00     0.000000  \n",
       "LactateDehydrogenase     0.000000  0.000000e+00     0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in rates constants:\n",
    "reaction_df = pd.DataFrame()\n",
    "filename = 'neurospora_pentose_phos.glycolysis.tca.2_rate.dat'\n",
    "with open(filename,'r') as f:\n",
    "  for line in f:\n",
    "     #print(line)\n",
    "     if re.match('^REACTION', line):\n",
    "        temp = re.split('\\s',line,1)\n",
    "        rxn_name = temp[1].strip()\n",
    "        if not rxn_name:            \n",
    "          print(\"Error: Reaction name not found:\\n\", line)\n",
    "     elif re.match('^COMMENT',line):\n",
    "        continue\n",
    "     elif re.match(r'//',line):\n",
    "        continue\n",
    "     elif re.match('^#',line):\n",
    "        continue\n",
    "     else:\n",
    "        #print(line)\n",
    "        rxn_pair = re.split('\\s',line,1)\n",
    "        reaction_df.loc[rxn_pair[0],rxn_name] = rxn_pair[1].strip()\n",
    "        \n",
    "     # end if\n",
    "  # end for loop\n",
    "# end with loop\n",
    "reaction_df = reaction_df.T\n",
    "reaction_df['k_FORWARD'] = reaction_df['k_FORWARD'].astype(np.float64)\n",
    "reaction_df['k_REVERSE'] = reaction_df['k_REVERSE'].astype(np.float64)\n",
    "del reaction_df['DGZERO-UNITS']\n",
    "del reaction_df['DGZERO']\n",
    "del reaction_df['COMMNENT']\n",
    "del reaction_df['PATHWAY']\n",
    "\n",
    "# Read in stoichiometric matrix and get reactant and product matrices\n",
    "S = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_reg_rate.mat',header=0, index_col = 0, quoting=2)\n",
    "P = (S>0)\n",
    "R = S < 0\n",
    "del P['forward reaction']\n",
    "del R['forward reaction']\n",
    "\n",
    "P = P.astype(np.float64)\n",
    "R = R.astype(np.float64)\n",
    "\n",
    "# Calculate rates:\n",
    "# v_{f} = exp(log(k_f) + R log C)\n",
    "# v_{r} = exp(log(k_r) + P log C)\n",
    "# v_{net} = v_{f} - v_{r} \n",
    "reactant_concentrations = R.multiply(ode_metabolites_steadystate['Counts'],axis=1)\n",
    "reactant_concentrations[reactant_concentrations == 0] = 1\n",
    "reactant_concentrations_rxns = (reactant_concentrations.T).product()\n",
    "\n",
    "product_concentrations = P.multiply(ode_metabolites_steadystate['Counts'],axis=1)\n",
    "product_concentrations[product_concentrations == 0] = 1\n",
    "product_concentrations_rxns = (product_concentrations.T).product()\n",
    "\n",
    "reaction_df['Fwd Rate'] = reaction_df['k_FORWARD'].multiply(reactant_concentrations_rxns)\n",
    "reaction_df['Rev Rate'] = reaction_df['k_REVERSE'].multiply(product_concentrations_rxns)\n",
    "reaction_df['Net Rate'] = reaction_df['Fwd Rate']-reaction_df['Rev Rate']\n",
    "display(reaction_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Power\n",
    "\n",
    "### Power Characteristics\n",
    "For complex systems such as biological systems, the power is the change in free energy with respect to time. After one hour when the growth rate $\\dot{\\xi} = 1 hr^{-1}$, a full stoichiometric quantity of cells is produced according to Eqn \\ref{biomass1} and \\ref{biomass2}. The growth rate is the change in the extent of the reaction $\\xi$ with time such that the growth rate $\\dot{\\xi} = \\frac{d\\xi}{dt}$. The power consumption of a cell can then be expressed in terms of the reaction affinity $A$ and the extent of the reaction $\\xi$,\n",
    "\\begin{eqnarray}\n",
    "P & = & -\\frac{dG}{dt} \\\\\n",
    "  & = & -\\frac{dG}{d\\xi}\\frac{d\\xi}{dt} \\\\\n",
    "  & = & A\\frac{d\\xi}{dt} \\\\\n",
    "\\label{power3}\n",
    "\\end{eqnarray}\n",
    "Since the system is at steady state, $P_{avg} = A\\cdot \\dot{\\xi}$ as well. This relationship is useful for comparing different chemical processes, as will be seen below. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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",
       "      <th>$\\Delta G$</th>\n",
       "      <th>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CSm</th>\n",
       "      <td>3.167759</td>\n",
       "      <td>3.099478e-01</td>\n",
       "      <td>2.857811</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.858297</td>\n",
       "      <td>3.295126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACONTm</th>\n",
       "      <td>3.172947</td>\n",
       "      <td>3.151333e-01</td>\n",
       "      <td>2.857814</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.862354</td>\n",
       "      <td>3.299805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICDHxm</th>\n",
       "      <td>2.943089</td>\n",
       "      <td>8.531388e-02</td>\n",
       "      <td>2.857775</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.675933</td>\n",
       "      <td>3.084853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKGDm</th>\n",
       "      <td>2.943124</td>\n",
       "      <td>8.532100e-02</td>\n",
       "      <td>2.857803</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.675962</td>\n",
       "      <td>3.084917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCOASm</th>\n",
       "      <td>3.172931</td>\n",
       "      <td>3.151645e-01</td>\n",
       "      <td>2.857766</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>-2.862340</td>\n",
       "      <td>3.299736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCD1m</th>\n",
       "      <td>3.172834</td>\n",
       "      <td>3.151739e-01</td>\n",
       "      <td>2.857660</td>\n",
       "      <td>0.057141</td>\n",
       "      <td>-2.862265</td>\n",
       "      <td>3.299526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMm</th>\n",
       "      <td>3.172531</td>\n",
       "      <td>3.152049e-01</td>\n",
       "      <td>2.857326</td>\n",
       "      <td>0.057135</td>\n",
       "      <td>-2.862029</td>\n",
       "      <td>3.298868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDHm</th>\n",
       "      <td>3.167089</td>\n",
       "      <td>3.108204e-01</td>\n",
       "      <td>2.856269</td>\n",
       "      <td>0.057113</td>\n",
       "      <td>-2.857773</td>\n",
       "      <td>3.292744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <td>2.857815</td>\n",
       "      <td>7.730477e-07</td>\n",
       "      <td>2.857814</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.603046</td>\n",
       "      <td>3.000868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <td>3.165479</td>\n",
       "      <td>3.076658e-01</td>\n",
       "      <td>2.857813</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.856512</td>\n",
       "      <td>3.293071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPI</th>\n",
       "      <td>1.428909</td>\n",
       "      <td>1.511411e-06</td>\n",
       "      <td>1.428907</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>-0.884768</td>\n",
       "      <td>0.509993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBA</th>\n",
       "      <td>1.428908</td>\n",
       "      <td>1.511408e-06</td>\n",
       "      <td>1.428906</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>-0.884766</td>\n",
       "      <td>0.509992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK</th>\n",
       "      <td>2.857812</td>\n",
       "      <td>3.583479e-05</td>\n",
       "      <td>2.857776</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.603043</td>\n",
       "      <td>3.000825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGM</th>\n",
       "      <td>2.995261</td>\n",
       "      <td>1.374769e-01</td>\n",
       "      <td>2.857784</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>-2.719493</td>\n",
       "      <td>3.135079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <td>2.857838</td>\n",
       "      <td>7.274040e-05</td>\n",
       "      <td>2.857765</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>-2.603066</td>\n",
       "      <td>3.000840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEX1</th>\n",
       "      <td>1.428907</td>\n",
       "      <td>1.389075e-11</td>\n",
       "      <td>1.428907</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>-31.429076</td>\n",
       "      <td>18.116184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <td>1.943454</td>\n",
       "      <td>5.145463e-01</td>\n",
       "      <td>1.428908</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>-1.647184</td>\n",
       "      <td>0.949462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFK</th>\n",
       "      <td>1.428907</td>\n",
       "      <td>1.389073e-11</td>\n",
       "      <td>1.428907</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>-31.429076</td>\n",
       "      <td>18.116184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRt2m</th>\n",
       "      <td>2.857773</td>\n",
       "      <td>8.044882e-06</td>\n",
       "      <td>2.857765</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>-2.603009</td>\n",
       "      <td>3.000774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDHm</th>\n",
       "      <td>2.859040</td>\n",
       "      <td>3.883754e-05</td>\n",
       "      <td>2.859001</td>\n",
       "      <td>0.057168</td>\n",
       "      <td>-2.604108</td>\n",
       "      <td>3.003339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Forward       Reverse   For-Rev  Rxn Probabilities  $\\Delta G$  \\\n",
       "CSm      3.167759  3.099478e-01  2.857811           0.057144   -2.858297   \n",
       "ACONTm   3.172947  3.151333e-01  2.857814           0.057144   -2.862354   \n",
       "ICDHxm   2.943089  8.531388e-02  2.857775           0.057144   -2.675933   \n",
       "AKGDm    2.943124  8.532100e-02  2.857803           0.057144   -2.675962   \n",
       "SUCOASm  3.172931  3.151645e-01  2.857766           0.057143   -2.862340   \n",
       "SUCD1m   3.172834  3.151739e-01  2.857660           0.057141   -2.862265   \n",
       "FUMm     3.172531  3.152049e-01  2.857326           0.057135   -2.862029   \n",
       "MDHm     3.167089  3.108204e-01  2.856269           0.057113   -2.857773   \n",
       "GAPD     2.857815  7.730477e-07  2.857814           0.057144   -2.603046   \n",
       "PGK      3.165479  3.076658e-01  2.857813           0.057144   -2.856512   \n",
       "TPI      1.428909  1.511411e-06  1.428907           0.028572   -0.884768   \n",
       "FBA      1.428908  1.511408e-06  1.428906           0.028572   -0.884766   \n",
       "PYK      2.857812  3.583479e-05  2.857776           0.057144   -2.603043   \n",
       "PGM      2.995261  1.374769e-01  2.857784           0.057144   -2.719493   \n",
       "ENO      2.857838  7.274040e-05  2.857765           0.057143   -2.603066   \n",
       "HEX1     1.428907  1.389075e-11  1.428907           0.028572  -31.429076   \n",
       "PGI      1.943454  5.145463e-01  1.428908           0.028572   -1.647184   \n",
       "PFK      1.428907  1.389073e-11  1.428907           0.028572  -31.429076   \n",
       "PYRt2m   2.857773  8.044882e-06  2.857765           0.057143   -2.603009   \n",
       "PDHm     2.859040  3.883754e-05  2.859001           0.057168   -2.604108   \n",
       "\n",
       "             Power  \n",
       "CSm       3.295126  \n",
       "ACONTm    3.299805  \n",
       "ICDHxm    3.084853  \n",
       "AKGDm     3.084917  \n",
       "SUCOASm   3.299736  \n",
       "SUCD1m    3.299526  \n",
       "FUMm      3.298868  \n",
       "MDHm      3.292744  \n",
       "GAPD      3.000868  \n",
       "PGK       3.293071  \n",
       "TPI       0.509993  \n",
       "FBA       0.509992  \n",
       "PYK       3.000825  \n",
       "PGM       3.135079  \n",
       "ENO       3.000840  \n",
       "HEX1     18.116184  \n",
       "PGI       0.949462  \n",
       "PFK      18.116184  \n",
       "PYRt2m    3.000774  \n",
       "PDHm      3.003339  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_reg_rate.amat',header=0, index_col = 0, quoting=2)\n",
    "R = (S<0)\n",
    "P = S>0\n",
    "del R['forward reaction']\n",
    "del P['forward reaction']\n",
    "del S['forward reaction']\n",
    "#S['H2O:CYTOSOL'] =0\n",
    "#S['H2O:GLYOXYSOME'] =0\n",
    "#S['H2O:MITOCHONDRIA'] =0\n",
    "\n",
    "volume_coeff = S.sum(axis=1)\n",
    "\n",
    "R = R.astype(np.float64)\n",
    "P = P.astype(np.float64)\n",
    "\n",
    "steadyState_counts = ode_metabolites_steadystate['Mean']*Concentration2Count\n",
    "\n",
    "reactant_counts = R.multiply(steadyState_counts,axis=1)\n",
    "reactant_concs =  R.multiply(ode_metabolites_steadystate['Mean'])\n",
    "product_counts = P.multiply(steadyState_counts,axis=1)\n",
    "product_concs = P.multiply(ode_metabolites_steadystate['Mean'])\n",
    "\n",
    "product_counts_incremented = product_counts + P.multiply(1)\n",
    "product_concs_incremented = product_concs + P.multiply(concentration_increment)\n",
    "product_counts = product_counts #+ P.multiply(1)\n",
    "product_concs = product_concs #+ P.multiply(concentration_increment)\n",
    "\n",
    "# Take the product of counts of all reactants for a reaction\n",
    "reactant_counts_rxns = (reactant_counts[reactant_counts != 0].T).product()\n",
    "reactant_concs_rxns = (reactant_concs[reactant_concs != 0].T).product()\n",
    "\n",
    "# Take the product of counts of all products for a reaction\n",
    "product_counts_rxns = (product_counts[product_counts != 0].T).product()\n",
    "product_concs_rxns = (product_concs[product_concs != 0].T).product()\n",
    "product_counts_incremented_rxns = (product_counts_incremented[product_counts_incremented != 0].T).product()\n",
    "product_concs_incremented_rxns = (product_concs_incremented[product_concs_incremented != 0].T).product()\n",
    "#denominator = reactant_counts_rxns.multiply(1-ode_likelihoods_steadystate['Reverse'])\n",
    "\n",
    "K_eq = pd.read_table('neurospora_pentose_phos.glycolysis.tca.2_reg_rate.dg0ke',header=0, index_col = 0, quoting=2)\n",
    "\n",
    "result = pd.DataFrame()\n",
    "result['Boltzmann Ke'] = K_eq['Ke']\n",
    "# Notice that Computed L_fwd uses incremented product counts. That is so we can compare to Boltzmann values\n",
    "# when calculating the activities. That is, these need to use the same formula.\n",
    "result['Computed L_fwd'] = result['Boltzmann Ke'].multiply(reactant_counts_rxns.divide(product_counts_incremented_rxns))\n",
    "\n",
    "idx = np.abs(ode_likelihoods_steadystate['For-Rev']) > 0\n",
    "DeltaG = \"$\\Delta G$\"\n",
    "ode_likelihoods_steadystate[DeltaG] = np.nan\n",
    "ode_likelihoods_steadystate['Power'] = np.nan\n",
    "ode_likelihoods_steadystate[DeltaG][idx] = -RT*np.log(result['Computed L_fwd'][idx])\n",
    "ode_likelihoods_steadystate['Power'][idx] = ode_likelihoods_steadystate['For-Rev'][idx]*np.log(result['Computed L_fwd'][idx])\n",
    "display(ode_likelihoods_steadystate.loc[idx,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "               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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = ode_likelihoods_steadystate.isnull()['Power']\n",
    "ode_likelihoods_steadystate.loc[idx,'Power'] = 0.0\n",
    "reaction_data = ode_likelihoods_steadystate['Power'].to_dict()\n",
    "b = Builder(map_name=\"iMM904.compact_Glycolysis_TCA_PPP.json\",reaction_data=reaction_data)\n",
    "b.display_in_notebook(menu='zoom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the free energy change is negative, each reaction will have a negative resistance. Each reaction is actually a small battery, in a sense.\n",
    "\n",
    "A regulated reaction that has a large favorable free energy change relative to other reactions at steady state, will act as a potentiometer.  That is, one could increase the flux by changing the extent of the regulation. \n",
    "\n",
    "## Calculate Resistance and Conductance\n",
    "G = J/V or flux/$\\Delta G$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  \n",
       "  \n",
       "    <link rel=\"stylesheet\" href=\"//unpkg.com/escher-vis@1.6.0/css/dist/builder.min.css\"/>\n",
       "  \n",
       "\n",
       "\n",
       "\n",
       "  <div style=\"height: 500px; width: 100%;\" id=\"bysbgordhi\"></div>\n",
       "\n",
       "  <script>\n",
       "   /* Closure to make sure data only applies to this map. */\n",
       "   (function() {\n",
       "       /* data from python */\n",
       "       var data = get_data();\n",
       "\n",
       "       function load_lib(url, callback) {\n",
       "           /** Dynamically load a JavaScript file */\n",
       "           var s = document.createElement('script');\n",
       "           s.src = url;\n",
       "           s.async = true;\n",
       "           s.onreadystatechange = s.onload = callback;\n",
       "           s.onerror = function() {\n",
       "               console.warn('Failed to load library ' + url);\n",
       "           };\n",
       "           document.getElementsByTagName('head')[0].appendChild(s);\n",
       "       }\n",
       "\n",
       "       function check_global_and_load(name, url, callback) {\n",
       "           /** Look for the name in the global scope. If it isn't present, then\n",
       "              load the library at url. Returns the object. */\n",
       "           if (window[name] !== undefined) {\n",
       "               callback(window[name]);\n",
       "           } else {\n",
       "               load_lib(url, function() {\n",
       "                   callback(window[name]);\n",
       "               });\n",
       "           }\n",
       "       }\n",
       "\n",
       "       function draw_escher(escher) {\n",
       "           /** Get data and draw */\n",
       "           var load = function(t_map_data, t_model_data, t_options) {\n",
       "               escher.Builder(t_map_data, t_model_data, data.builder_embed_css,\n",
       "                              escher.libs.d3_select('#' + data.id), t_options);\n",
       "           }\n",
       "\n",
       "           // for the static site, use the url attributes for the map and model\n",
       "           // information\n",
       "           if (data.static_site_index !== null) {\n",
       "               escher.static.load_map_model_from_url(data.map_download_url, data.model_download_url,\n",
       "                                                     data.static_site_index, data.options, load);\n",
       "           } else {\n",
       "               load(data.map_data, data.model_data, data.options);\n",
       "           }\n",
       "       }\n",
       "\n",
       "       // Load dependencies and draw\n",
       "       if (typeof define === 'function' && define.amd) {\n",
       "           // require.js is available: use it to load escher\n",
       "           require.config({\n",
       "             paths: {\n",
       "               escher: '//unpkg.com/escher-vis@1.6.0/js/dist/escher.min',\n",
       "             },\n",
       "             // Override urlArgs that adds ?v= to the end of the escher URL and\n",
       "             // messes up unpkg links. Honestly, I'm not sure if this will lead\n",
       "             // to downstream bugs in the notebook, but they really shouldn't be\n",
       "             // doing this cache-busting anyway.\n",
       "             urlArgs: null,\n",
       "           })\n",
       "           require([ 'escher' ], function(escher) {\n",
       "               draw_escher(escher);\n",
       "           })\n",
       "       } else {\n",
       "           // require.js not available: dynamically load escher\n",
       "           check_global_and_load('escher', '//unpkg.com/escher-vis@1.6.0/js/dist/escher.min.js', function(escher) {\n",
       "               draw_escher(escher);\n",
       "           })\n",
       "       }\n",
       "\n",
       "       // Put this at the end so Chrome doesn't cut off the code above.\n",
       "       function get_data() {\n",
       "           /** Return the data passed in by the Python module. */\n",
       "           return {\n",
       "               id: JSON.parse('\"bysbgordhi\"'),\n",
       "               map_download_url: JSON.parse('\"//escher.github.io/1-0-0/5/maps/\"'),\n",
       "               model_download_url: JSON.parse('\"//escher.github.io/1-0-0/5/models/\"'),\n",
       "               options: JSON.parse('{\"fill_screen\": false, \"enable_keys\": false, \"never_ask_before_quit\": true, \"metabolite_data\": null, \"reaction_data\": {\"TPI\": 0.24977923047791648, \"PGM\": 0.38387484300306707, \"PGK\": 0.40321194688092254, \"SUCOASm\": 0.40404119957629575, \"FUMm\": 0.4040595104927272, \"GAPD\": 0.36743364024585545, \"PGI\": 0.4650173377404709, \"ACONTm\": 0.40403641568221604, \"SUCD1m\": 0.4040456869212071, \"AKGDm\": 0.37772774230118306, \"MDHm\": 0.40360807473809224, \"FBA\": 0.24977904375831494, \"CSm\": 0.4034641970100286, \"PYRt2m\": 0.36743485150189426, \"ENO\": 0.36744277895436733, \"PYK\": 0.3674381173983264, \"PFK\": 8.872761316298474, \"HEX1\": 8.87276140487925, \"ICDHxm\": 0.37772733456414953, \"PDHm\": 0.36743094405245735}, \"menu\": \"zoom\", \"ignore_bootstrap\": true, \"scroll_behavior\": \"none\", \"gene_data\": null, \"enable_editing\": false}'),\n",
       "               builder_embed_css: JSON.parse('null'),\n",
       "               map_data: JSON.parse('[{\"map_name\":\"iMM904.Central carbon metabolism\",\"map_id\":\"78086bfdab8ac8a8150cf4cd5dada037\",\"map_description\":\"Yeast central carbon metabolism\\\\nLast Modified Wed Feb 28 2018 07:00:43 GMT-0800 (PST)\",\"homepage\":\"https://escher.github.io\",\"schema\":\"https://escher.github.io/escher/jsonschema/1-0-0#\"},{\"reactions\":{\"667142\":{\"name\":\"Glucose-6-phosphate 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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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.abs(ode_likelihoods_steadystate['For-Rev']) > 0\n",
    "ode_likelihoods_steadystate['Resistance'] = np.nan\n",
    "ode_likelihoods_steadystate['Conductance'] = np.nan\n",
    "ode_likelihoods_steadystate['Conductance'][idx] = ode_likelihoods_steadystate['For-Rev'][idx]/((-1)*np.log(result['Computed L_fwd'][idx]))\n",
    "ode_likelihoods_steadystate['Resistance'][idx] = ((1)*np.log(result['Computed L_fwd'][idx]))/ode_likelihoods_steadystate['For-Rev'][idx]\n",
    "\n",
    "idx = ode_likelihoods_steadystate.notna()['Resistance']\n",
    "#ode_likelihoods_steadystate.loc[idx,'Resistance'] = np.inf\n",
    "reaction_data = ode_likelihoods_steadystate.loc[idx,'Resistance'].to_dict()\n",
    "\n",
    "b = Builder(map_name=\"iMM904.compact_Glycolysis_TCA_PPP.json\",reaction_data=reaction_data)\n",
    "b.display_in_notebook(menu='zoom')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next snippet of code generates the data needed for construction of Figure 4 in the manuscript, which is made using Pathway Tools from SRI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "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></th>\n",
       "      <th>Energy</th>\n",
       "      <th>Power</th>\n",
       "      <th>Resistance</th>\n",
       "      <th>Flux</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reaction</th>\n",
       "      <th>FrameId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CSm</th>\n",
       "      <th>CITSYN-RXN</th>\n",
       "      <td>-1.153025</td>\n",
       "      <td>3.295129</td>\n",
       "      <td>0.403464</td>\n",
       "      <td>2.857812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ACONTm</th>\n",
       "      <th>ACONITATEDEHYDR-RXN</th>\n",
       "      <td>-1.154661</td>\n",
       "      <td>3.299805</td>\n",
       "      <td>0.404036</td>\n",
       "      <td>2.857814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACONITATEHYDR-RXN</th>\n",
       "      <td>-1.154661</td>\n",
       "      <td>3.299805</td>\n",
       "      <td>0.404036</td>\n",
       "      <td>2.857814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICDHxm</th>\n",
       "      <th>ISOCITRATE-DEHYDROGENASE-NAD+-RXN</th>\n",
       "      <td>-1.079460</td>\n",
       "      <td>3.084853</td>\n",
       "      <td>0.377727</td>\n",
       "      <td>2.857775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKGDam</th>\n",
       "      <th>2OXOGLUTARATEDEH-RXN</th>\n",
       "      <td>-1.079472</td>\n",
       "      <td>3.084917</td>\n",
       "      <td>0.377728</td>\n",
       "      <td>2.857803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCOASm</th>\n",
       "      <th>SUCCCOASYN-RXN</th>\n",
       "      <td>-1.154656</td>\n",
       "      <td>3.299736</td>\n",
       "      <td>0.404041</td>\n",
       "      <td>2.857766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCD1m</th>\n",
       "      <th>SUCCINATE-DEHYDROGENASE-UBIQUINONE-RXN</th>\n",
       "      <td>-1.154625</td>\n",
       "      <td>3.299526</td>\n",
       "      <td>0.404046</td>\n",
       "      <td>2.857660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUMm</th>\n",
       "      <th>FUMHYDR-RXN</th>\n",
       "      <td>-1.154530</td>\n",
       "      <td>3.298868</td>\n",
       "      <td>0.404060</td>\n",
       "      <td>2.857326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDHm</th>\n",
       "      <th>MALATE-DEH-RXN</th>\n",
       "      <td>-1.152814</td>\n",
       "      <td>3.292751</td>\n",
       "      <td>0.403608</td>\n",
       "      <td>2.856273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <th>GAPOXNPHOSPHN-RXN</th>\n",
       "      <td>-1.050057</td>\n",
       "      <td>3.000868</td>\n",
       "      <td>0.367434</td>\n",
       "      <td>2.857814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <th>PHOSGLYPHOS-RXN</th>\n",
       "      <td>-1.152304</td>\n",
       "      <td>3.293071</td>\n",
       "      <td>0.403212</td>\n",
       "      <td>2.857813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPI</th>\n",
       "      <th>TRIOSPISOMERIZATION-RXN</th>\n",
       "      <td>-0.356911</td>\n",
       "      <td>0.509993</td>\n",
       "      <td>0.249779</td>\n",
       "      <td>1.428907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBA</th>\n",
       "      <th>F16ALDOLASE-RXN</th>\n",
       "      <td>-0.356911</td>\n",
       "      <td>0.509992</td>\n",
       "      <td>0.249779</td>\n",
       "      <td>1.428906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYK</th>\n",
       "      <th>PEPDEPHOS-RXN</th>\n",
       "      <td>-1.050056</td>\n",
       "      <td>3.000825</td>\n",
       "      <td>0.367438</td>\n",
       "      <td>2.857776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGM</th>\n",
       "      <th>3PGAREARR-RXN</th>\n",
       "      <td>-1.097031</td>\n",
       "      <td>3.135079</td>\n",
       "      <td>0.383875</td>\n",
       "      <td>2.857784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <th>2PGADEHYDRAT-RXN</th>\n",
       "      <td>-1.050065</td>\n",
       "      <td>3.000840</td>\n",
       "      <td>0.367443</td>\n",
       "      <td>2.857765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEX1</th>\n",
       "      <th>GLUCOKIN-RXN</th>\n",
       "      <td>-0.356910</td>\n",
       "      <td>18.116184</td>\n",
       "      <td>8.872761</td>\n",
       "      <td>1.428907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <th>PGLUCISOM-RXN</th>\n",
       "      <td>-0.664467</td>\n",
       "      <td>0.949462</td>\n",
       "      <td>0.465017</td>\n",
       "      <td>1.428908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFK</th>\n",
       "      <th>6PFRUCTPHOS-RXN</th>\n",
       "      <td>-0.356910</td>\n",
       "      <td>1.116184</td>\n",
       "      <td>8.872761</td>\n",
       "      <td>1.428907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PYRt2m</th>\n",
       "      <th>TRANS-RXN2T-215</th>\n",
       "      <td>-1.050043</td>\n",
       "      <td>3.000774</td>\n",
       "      <td>0.367435</td>\n",
       "      <td>2.857765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDHm</th>\n",
       "      <th>PYRUVDEHY-RXN</th>\n",
       "      <td>-1.050485</td>\n",
       "      <td>3.003334</td>\n",
       "      <td>0.367431</td>\n",
       "      <td>2.858998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Energy      Power  \\\n",
       "Reaction FrameId                                                       \n",
       "CSm      CITSYN-RXN                             -1.153025   3.295129   \n",
       "ACONTm   ACONITATEDEHYDR-RXN                    -1.154661   3.299805   \n",
       "         ACONITATEHYDR-RXN                      -1.154661   3.299805   \n",
       "ICDHxm   ISOCITRATE-DEHYDROGENASE-NAD+-RXN      -1.079460   3.084853   \n",
       "AKGDam   2OXOGLUTARATEDEH-RXN                   -1.079472   3.084917   \n",
       "SUCOASm  SUCCCOASYN-RXN                         -1.154656   3.299736   \n",
       "SUCD1m   SUCCINATE-DEHYDROGENASE-UBIQUINONE-RXN -1.154625   3.299526   \n",
       "FUMm     FUMHYDR-RXN                            -1.154530   3.298868   \n",
       "MDHm     MALATE-DEH-RXN                         -1.152814   3.292751   \n",
       "GAPD     GAPOXNPHOSPHN-RXN                      -1.050057   3.000868   \n",
       "PGK      PHOSGLYPHOS-RXN                        -1.152304   3.293071   \n",
       "TPI      TRIOSPISOMERIZATION-RXN                -0.356911   0.509993   \n",
       "FBA      F16ALDOLASE-RXN                        -0.356911   0.509992   \n",
       "PYK      PEPDEPHOS-RXN                          -1.050056   3.000825   \n",
       "PGM      3PGAREARR-RXN                          -1.097031   3.135079   \n",
       "ENO      2PGADEHYDRAT-RXN                       -1.050065   3.000840   \n",
       "HEX1     GLUCOKIN-RXN                           -0.356910  18.116184   \n",
       "PGI      PGLUCISOM-RXN                          -0.664467   0.949462   \n",
       "PFK      6PFRUCTPHOS-RXN                        -0.356910   1.116184   \n",
       "PYRt2m   TRANS-RXN2T-215                        -1.050043   3.000774   \n",
       "PDHm     PYRUVDEHY-RXN                          -1.050485   3.003334   \n",
       "\n",
       "                                                 Resistance      Flux  \n",
       "Reaction FrameId                                                       \n",
       "CSm      CITSYN-RXN                                0.403464  2.857812  \n",
       "ACONTm   ACONITATEDEHYDR-RXN                       0.404036  2.857814  \n",
       "         ACONITATEHYDR-RXN                         0.404036  2.857814  \n",
       "ICDHxm   ISOCITRATE-DEHYDROGENASE-NAD+-RXN         0.377727  2.857775  \n",
       "AKGDam   2OXOGLUTARATEDEH-RXN                      0.377728  2.857803  \n",
       "SUCOASm  SUCCCOASYN-RXN                            0.404041  2.857766  \n",
       "SUCD1m   SUCCINATE-DEHYDROGENASE-UBIQUINONE-RXN    0.404046  2.857660  \n",
       "FUMm     FUMHYDR-RXN                               0.404060  2.857326  \n",
       "MDHm     MALATE-DEH-RXN                            0.403608  2.856273  \n",
       "GAPD     GAPOXNPHOSPHN-RXN                         0.367434  2.857814  \n",
       "PGK      PHOSGLYPHOS-RXN                           0.403212  2.857813  \n",
       "TPI      TRIOSPISOMERIZATION-RXN                   0.249779  1.428907  \n",
       "FBA      F16ALDOLASE-RXN                           0.249779  1.428906  \n",
       "PYK      PEPDEPHOS-RXN                             0.367438  2.857776  \n",
       "PGM      3PGAREARR-RXN                             0.383875  2.857784  \n",
       "ENO      2PGADEHYDRAT-RXN                          0.367443  2.857765  \n",
       "HEX1     GLUCOKIN-RXN                              8.872761  1.428907  \n",
       "PGI      PGLUCISOM-RXN                             0.465017  1.428908  \n",
       "PFK      6PFRUCTPHOS-RXN                           8.872761  1.428907  \n",
       "PYRt2m   TRANS-RXN2T-215                           0.367435  2.857765  \n",
       "PDHm     PYRUVDEHY-RXN                             0.367431  2.858998  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biocyc_frames = pd.read_table('power_energy_characteristics.tab',header=0, index_col = 0, quoting=2)\n",
    "biocyc_frames.set_index(['Reaction'],append=True,inplace=True)\n",
    "biocyc_frames = biocyc_frames.swaplevel(axis=0)\n",
    "display(biocyc_frames)\n",
    "\n",
    "biocyc_frames = biocyc_frames.swaplevel(axis=0)\n",
    "biocyc_frames.to_csv('power_energy_characteristics2.txt', sep='\\t', columns = ['Energy','Power','Resistance','Flux'])"
   ]
  }
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