{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b762064d",
   "metadata": {},
   "source": [
    "# Pathway visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a6180c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Determination of memory status is not supported on this \n",
      " platform, measuring for memoryleaks will never fail\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import cobra\n",
    "from BFAIR.mfa.INCA import INCA_reimport\n",
    "from BFAIR.mfa.utils import (\n",
    "    calculate_split_ratio,\n",
    "    plot_split_ratio,\n",
    "    get_observable_fluxes,\n",
    "    percent_observable_fluxes,\n",
    "    get_flux_precision,\n",
    ")\n",
    "from BFAIR.mfa.visualization import (\n",
    "    reshape_fluxes_escher,\n",
    "    sampled_fluxes_minrange,\n",
    "    show_reactions,\n",
    "    plot_sampled_reaction_fluxes,\n",
    "    plot_all_subsystem_fluxes,\n",
    "    get_subsytem_reactions,\n",
    "    show_subsystems,\n",
    "    plot_subsystem_fluxes,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296fbade",
   "metadata": {},
   "source": [
    "The following steps are detailed in the notebooks `MFA_compatibility` and `MFA_feasibility_and_sampling`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9f86795",
   "metadata": {},
   "outputs": [],
   "source": [
    "fittedFluxes = pd.read_pickle(\"data/MFA_sampling/preprocessed_fittedFluxes.obj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd131b35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Academic license - for non-commercial use only - expires 2021-07-30\n",
      "Using license file /Users/matmat/gurobi.lic\n"
     ]
    }
   ],
   "source": [
    "model = cobra.io.load_json_model(\"data/MFA_sampling/preprocessed_model.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6db1d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "relaxed_sampled_fluxes = pd.read_pickle(\"data/MFA_sampling/relaxed_sampled_fluxes.obj\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc8ee9f",
   "metadata": {},
   "source": [
    "## Investigate the sampled fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2975ca1e",
   "metadata": {},
   "source": [
    "A few plots of the distribution of points per reaction to visually inspect how gaussian the sample distributions are. In rare cases, bimodal distributions can be found"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e85d9d",
   "metadata": {},
   "source": [
    "#### Sampled value distributions per reaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce94549",
   "metadata": {},
   "source": [
    "First, print all the subsystems included in the metabolic model that is being used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3c05e5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Extracellular exchange\n",
      "1: Intracellular demand\n",
      "2: Biomass and maintenance functions\n",
      "3: Transport, Inner Membrane\n",
      "4: Transport, Outer Membrane Porin\n",
      "5: Transport, Outer Membrane\n",
      "6: Nucleotide Salvage Pathway\n",
      "7: Glycerophospholipid Metabolism\n",
      "8: Alternate Carbon Metabolism\n",
      "9: Cofactor and Prosthetic Group Biosynthesis\n",
      "10: Cell Envelope Biosynthesis\n",
      "11: Methylglyoxal Metabolism\n",
      "12: Arginine and Proline Metabolism\n",
      "13: Membrane Lipid Metabolism\n",
      "14: Pyruvate Metabolism\n",
      "15: Tyrosine, Tryptophan, and Phenylalanine Metabolism\n",
      "16: Murein Recycling\n",
      "17: Valine, Leucine, and Isoleucine Metabolism\n",
      "18: Nitrogen Metabolism\n",
      "19: Lipopolysaccharide Biosynthesis / Recycling\n",
      "20: Unassigned\n",
      "21: Citric Acid Cycle\n",
      "22: Inorganic Ion Transport and Metabolism\n",
      "23: Methionine Metabolism\n",
      "24: Purine and Pyrimidine Biosynthesis\n",
      "25: Alanine and Aspartate Metabolism\n",
      "26: tRNA Charging\n",
      "27: Cysteine Metabolism\n",
      "28: Threonine and Lysine Metabolism\n",
      "29: Histidine Metabolism\n",
      "30: Oxidative Phosphorylation\n",
      "31: Glycine and Serine Metabolism\n",
      "32: Pentose Phosphate Pathway\n",
      "33: Glycolysis/Gluconeogenesis\n",
      "34: Folate Metabolism\n",
      "35: Glutamate Metabolism\n",
      "36: Glyoxylate Metabolism\n",
      "37: Anaplerotic Reactions\n",
      "38: Murein Biosynthesis\n"
     ]
    }
   ],
   "source": [
    "show_subsystems(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e104a40",
   "metadata": {},
   "source": [
    "Let's also have a look at the reaction in one of the subsystems. Here we use the Pyruvate Metabolism as an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d77ff54",
   "metadata": {},
   "outputs": [],
   "source": [
    "reactions, _ = get_subsytem_reactions(model, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68a96e09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: ACALD\n",
      "1: ACKr\n",
      "2: ACS\n",
      "3: ALCD2x\n",
      "4: FHL\n",
      "5: LDH_D\n",
      "6: OAADC\n",
      "7: PFL\n",
      "8: POR5\n",
      "9: PTAr\n",
      "10: PTAr_reverse\n",
      "11: ACKr_reverse\n",
      "12: ACS_reverse\n"
     ]
    }
   ],
   "source": [
    "show_reactions(reactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b93592",
   "metadata": {},
   "source": [
    "Here are some plotting methods to better understand what we just created. These plots include a check for normal distributions; if the sampled fluxes for a reaction are normal distributed, we see a green histogram with a kernel density distribution in red. If they are not then the colors are inverted. Here are some examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b52297",
   "metadata": {},
   "source": [
    "Reaction with normally distributed sampled fluxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87e69206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc516aa24c0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sampled_reaction_fluxes(relaxed_sampled_fluxes, reactions, reaction_id=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5cf06f",
   "metadata": {},
   "source": [
    "No normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad66a0af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc5164601c0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sampled_reaction_fluxes(relaxed_sampled_fluxes, reactions, reaction_id=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897d0f5c",
   "metadata": {},
   "source": [
    "We can also produce a summary plot for all the sampled reactions in a selected subsystem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c475cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x900 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_all_subsystem_fluxes(relaxed_sampled_fluxes, reactions, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bedeb2c8",
   "metadata": {},
   "source": [
    "#### Box plots for several reactions of interest to visualize the distribution of points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9f1157",
   "metadata": {},
   "source": [
    "This is a different way to have a look at the distributions of sampled values for a selected subsystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3adfce41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Extracellular exchange\n",
      "1: Intracellular demand\n",
      "2: Biomass and maintenance functions\n",
      "3: Transport, Inner Membrane\n",
      "4: Transport, Outer Membrane Porin\n",
      "5: Transport, Outer Membrane\n",
      "6: Nucleotide Salvage Pathway\n",
      "7: Glycerophospholipid Metabolism\n",
      "8: Alternate Carbon Metabolism\n",
      "9: Cofactor and Prosthetic Group Biosynthesis\n",
      "10: Cell Envelope Biosynthesis\n",
      "11: Methylglyoxal Metabolism\n",
      "12: Arginine and Proline Metabolism\n",
      "13: Membrane Lipid Metabolism\n",
      "14: Pyruvate Metabolism\n",
      "15: Tyrosine, Tryptophan, and Phenylalanine Metabolism\n",
      "16: Murein Recycling\n",
      "17: Valine, Leucine, and Isoleucine Metabolism\n",
      "18: Nitrogen Metabolism\n",
      "19: Lipopolysaccharide Biosynthesis / Recycling\n",
      "20: Unassigned\n",
      "21: Citric Acid Cycle\n",
      "22: Inorganic Ion Transport and Metabolism\n",
      "23: Methionine Metabolism\n",
      "24: Purine and Pyrimidine Biosynthesis\n",
      "25: Alanine and Aspartate Metabolism\n",
      "26: tRNA Charging\n",
      "27: Cysteine Metabolism\n",
      "28: Threonine and Lysine Metabolism\n",
      "29: Histidine Metabolism\n",
      "30: Oxidative Phosphorylation\n",
      "31: Glycine and Serine Metabolism\n",
      "32: Pentose Phosphate Pathway\n",
      "33: Glycolysis/Gluconeogenesis\n",
      "34: Folate Metabolism\n",
      "35: Glutamate Metabolism\n",
      "36: Glyoxylate Metabolism\n",
      "37: Anaplerotic Reactions\n",
      "38: Murein Biosynthesis\n"
     ]
    }
   ],
   "source": [
    "show_subsystems(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52bd076b",
   "metadata": {},
   "source": [
    "We can exclude some reactions if their sampled fluxes are very small in order not to crowd the plot. Here we exclude every reaction whose max value does not exceed 1 and whose min value does not go below -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf1102f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_relaxed_sampled_fluxes = sampled_fluxes_minrange(relaxed_sampled_fluxes, min_val=-1, max_val=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98b6127f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EX_co2_e</th>\n",
       "      <th>EX_glc__D_e</th>\n",
       "      <th>EX_h_e</th>\n",
       "      <th>EX_h2o_e</th>\n",
       "      <th>EX_ac_e</th>\n",
       "      <th>EX_lac__D_e</th>\n",
       "      <th>EX_nh4_e</th>\n",
       "      <th>EX_o2_e</th>\n",
       "      <th>EX_etoh_e</th>\n",
       "      <th>ABUTt2pp</th>\n",
       "      <th>...</th>\n",
       "      <th>ACONTa_reverse</th>\n",
       "      <th>FUM_reverse</th>\n",
       "      <th>GAPD_reverse</th>\n",
       "      <th>ICDHyr_reverse</th>\n",
       "      <th>PGM_reverse</th>\n",
       "      <th>PTAr_reverse</th>\n",
       "      <th>ACONTb_reverse</th>\n",
       "      <th>PGK_reverse</th>\n",
       "      <th>ACKr_reverse</th>\n",
       "      <th>ACS_reverse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.543237</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.953104</td>\n",
       "      <td>28.789643</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.215748</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>3.237696</td>\n",
       "      <td>465.714553</td>\n",
       "      <td>...</td>\n",
       "      <td>16.367512</td>\n",
       "      <td>8.367700</td>\n",
       "      <td>4.072338</td>\n",
       "      <td>2.371525</td>\n",
       "      <td>22.201158</td>\n",
       "      <td>4.986488</td>\n",
       "      <td>16.870859</td>\n",
       "      <td>23.245243</td>\n",
       "      <td>26.410573</td>\n",
       "      <td>14.852405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.942223</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.554119</td>\n",
       "      <td>28.789580</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.816636</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>3.636745</td>\n",
       "      <td>427.225266</td>\n",
       "      <td>...</td>\n",
       "      <td>22.920790</td>\n",
       "      <td>13.203244</td>\n",
       "      <td>9.455891</td>\n",
       "      <td>9.111397</td>\n",
       "      <td>21.735601</td>\n",
       "      <td>4.842415</td>\n",
       "      <td>25.150167</td>\n",
       "      <td>23.181246</td>\n",
       "      <td>13.659831</td>\n",
       "      <td>20.950423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.011655</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.484687</td>\n",
       "      <td>28.789676</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.747395</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>3.706081</td>\n",
       "      <td>559.450009</td>\n",
       "      <td>...</td>\n",
       "      <td>15.204975</td>\n",
       "      <td>9.995851</td>\n",
       "      <td>4.136965</td>\n",
       "      <td>11.608163</td>\n",
       "      <td>22.235026</td>\n",
       "      <td>12.156031</td>\n",
       "      <td>17.390690</td>\n",
       "      <td>23.249894</td>\n",
       "      <td>21.592494</td>\n",
       "      <td>14.942728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.202258</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>11.294084</td>\n",
       "      <td>28.789536</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.556513</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>2.896824</td>\n",
       "      <td>616.914400</td>\n",
       "      <td>...</td>\n",
       "      <td>21.907680</td>\n",
       "      <td>5.826934</td>\n",
       "      <td>3.935700</td>\n",
       "      <td>3.257740</td>\n",
       "      <td>21.459601</td>\n",
       "      <td>2.889205</td>\n",
       "      <td>21.762006</td>\n",
       "      <td>23.143307</td>\n",
       "      <td>18.602788</td>\n",
       "      <td>21.085737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.325906</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>11.170436</td>\n",
       "      <td>28.789660</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.433111</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>3.020349</td>\n",
       "      <td>178.689373</td>\n",
       "      <td>...</td>\n",
       "      <td>11.184041</td>\n",
       "      <td>7.140398</td>\n",
       "      <td>5.356585</td>\n",
       "      <td>6.182290</td>\n",
       "      <td>21.997845</td>\n",
       "      <td>3.832498</td>\n",
       "      <td>10.953312</td>\n",
       "      <td>23.217287</td>\n",
       "      <td>9.294117</td>\n",
       "      <td>9.636500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>14.612671</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>9.883671</td>\n",
       "      <td>28.789480</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.145987</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>4.307293</td>\n",
       "      <td>27.590698</td>\n",
       "      <td>...</td>\n",
       "      <td>27.182617</td>\n",
       "      <td>27.755076</td>\n",
       "      <td>22.768438</td>\n",
       "      <td>26.011132</td>\n",
       "      <td>29.612248</td>\n",
       "      <td>26.707553</td>\n",
       "      <td>27.086390</td>\n",
       "      <td>24.264196</td>\n",
       "      <td>26.035236</td>\n",
       "      <td>1.250572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>14.589870</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>9.906472</td>\n",
       "      <td>28.789480</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.168787</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>4.284492</td>\n",
       "      <td>3.380870</td>\n",
       "      <td>...</td>\n",
       "      <td>26.251317</td>\n",
       "      <td>27.346056</td>\n",
       "      <td>22.392033</td>\n",
       "      <td>25.431437</td>\n",
       "      <td>29.521050</td>\n",
       "      <td>25.816474</td>\n",
       "      <td>26.211918</td>\n",
       "      <td>24.251656</td>\n",
       "      <td>24.429541</td>\n",
       "      <td>1.430741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>14.589661</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>9.906680</td>\n",
       "      <td>28.789493</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.169023</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>4.284270</td>\n",
       "      <td>19.452563</td>\n",
       "      <td>...</td>\n",
       "      <td>25.911706</td>\n",
       "      <td>26.529107</td>\n",
       "      <td>21.936295</td>\n",
       "      <td>24.823842</td>\n",
       "      <td>29.639972</td>\n",
       "      <td>26.295611</td>\n",
       "      <td>25.981870</td>\n",
       "      <td>24.268005</td>\n",
       "      <td>25.094538</td>\n",
       "      <td>0.893650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>14.584237</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>9.912105</td>\n",
       "      <td>28.789489</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.174440</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>4.278850</td>\n",
       "      <td>27.401631</td>\n",
       "      <td>...</td>\n",
       "      <td>26.178008</td>\n",
       "      <td>26.791359</td>\n",
       "      <td>21.737854</td>\n",
       "      <td>24.645933</td>\n",
       "      <td>29.583094</td>\n",
       "      <td>26.034856</td>\n",
       "      <td>26.227460</td>\n",
       "      <td>24.260185</td>\n",
       "      <td>25.131312</td>\n",
       "      <td>1.352205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>14.010088</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.486254</td>\n",
       "      <td>28.789586</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.748782</td>\n",
       "      <td>-7.560577</td>\n",
       "      <td>-8.810505</td>\n",
       "      <td>3.704604</td>\n",
       "      <td>167.406398</td>\n",
       "      <td>...</td>\n",
       "      <td>23.423028</td>\n",
       "      <td>22.058764</td>\n",
       "      <td>15.957888</td>\n",
       "      <td>17.500774</td>\n",
       "      <td>25.802773</td>\n",
       "      <td>16.166442</td>\n",
       "      <td>22.425759</td>\n",
       "      <td>23.740430</td>\n",
       "      <td>15.536149</td>\n",
       "      <td>13.119900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 157 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     EX_co2_e  EX_glc__D_e     EX_h_e   EX_h2o_e  EX_ac_e  EX_lac__D_e  \\\n",
       "0   13.543237        -10.0  10.953104  28.789643      2.3     2.215748   \n",
       "1   13.942223        -10.0  10.554119  28.789580      2.3     1.816636   \n",
       "2   14.011655        -10.0  10.484687  28.789676      2.3     1.747395   \n",
       "3   13.202258        -10.0  11.294084  28.789536      2.3     2.556513   \n",
       "4   13.325906        -10.0  11.170436  28.789660      2.3     2.433111   \n",
       "..        ...          ...        ...        ...      ...          ...   \n",
       "95  14.612671        -10.0   9.883671  28.789480      2.3     1.145987   \n",
       "96  14.589870        -10.0   9.906472  28.789480      2.3     1.168787   \n",
       "97  14.589661        -10.0   9.906680  28.789493      2.3     1.169023   \n",
       "98  14.584237        -10.0   9.912105  28.789489      2.3     1.174440   \n",
       "99  14.010088        -10.0  10.486254  28.789586      2.3     1.748782   \n",
       "\n",
       "    EX_nh4_e   EX_o2_e  EX_etoh_e    ABUTt2pp  ...  ACONTa_reverse  \\\n",
       "0  -7.560577 -8.810505   3.237696  465.714553  ...       16.367512   \n",
       "1  -7.560577 -8.810505   3.636745  427.225266  ...       22.920790   \n",
       "2  -7.560577 -8.810505   3.706081  559.450009  ...       15.204975   \n",
       "3  -7.560577 -8.810505   2.896824  616.914400  ...       21.907680   \n",
       "4  -7.560577 -8.810505   3.020349  178.689373  ...       11.184041   \n",
       "..       ...       ...        ...         ...  ...             ...   \n",
       "95 -7.560577 -8.810505   4.307293   27.590698  ...       27.182617   \n",
       "96 -7.560577 -8.810505   4.284492    3.380870  ...       26.251317   \n",
       "97 -7.560577 -8.810505   4.284270   19.452563  ...       25.911706   \n",
       "98 -7.560577 -8.810505   4.278850   27.401631  ...       26.178008   \n",
       "99 -7.560577 -8.810505   3.704604  167.406398  ...       23.423028   \n",
       "\n",
       "    FUM_reverse  GAPD_reverse  ICDHyr_reverse  PGM_reverse  PTAr_reverse  \\\n",
       "0      8.367700      4.072338        2.371525    22.201158      4.986488   \n",
       "1     13.203244      9.455891        9.111397    21.735601      4.842415   \n",
       "2      9.995851      4.136965       11.608163    22.235026     12.156031   \n",
       "3      5.826934      3.935700        3.257740    21.459601      2.889205   \n",
       "4      7.140398      5.356585        6.182290    21.997845      3.832498   \n",
       "..          ...           ...             ...          ...           ...   \n",
       "95    27.755076     22.768438       26.011132    29.612248     26.707553   \n",
       "96    27.346056     22.392033       25.431437    29.521050     25.816474   \n",
       "97    26.529107     21.936295       24.823842    29.639972     26.295611   \n",
       "98    26.791359     21.737854       24.645933    29.583094     26.034856   \n",
       "99    22.058764     15.957888       17.500774    25.802773     16.166442   \n",
       "\n",
       "    ACONTb_reverse  PGK_reverse  ACKr_reverse  ACS_reverse  \n",
       "0        16.870859    23.245243     26.410573    14.852405  \n",
       "1        25.150167    23.181246     13.659831    20.950423  \n",
       "2        17.390690    23.249894     21.592494    14.942728  \n",
       "3        21.762006    23.143307     18.602788    21.085737  \n",
       "4        10.953312    23.217287      9.294117     9.636500  \n",
       "..             ...          ...           ...          ...  \n",
       "95       27.086390    24.264196     26.035236     1.250572  \n",
       "96       26.211918    24.251656     24.429541     1.430741  \n",
       "97       25.981870    24.268005     25.094538     0.893650  \n",
       "98       26.227460    24.260185     25.131312     1.352205  \n",
       "99       22.425759    23.740430     15.536149    13.119900  \n",
       "\n",
       "[100 rows x 157 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_relaxed_sampled_fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0391ffef",
   "metadata": {},
   "source": [
    "Here is the pyruvate metabolism as an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b519957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc535bb6c40>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_subsystem_fluxes(model, reduced_relaxed_sampled_fluxes, subsystem_id=14, no_zero_cols=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d50955a",
   "metadata": {},
   "source": [
    "#### Calculating the flux splits at key branch points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3e808a",
   "metadata": {},
   "source": [
    "One important piece of information that can be extracted from calculated fluxes are the flux splits at branching points (e.g., glycolysis vs. PPP, TCA vs. acetate secretion, glyoxylate shunt vs. full TCA, etc.). This will provide information about which of the optional pathways branching off of a given intermediate will carry a higher flux that potentially interferes with a mapped out production strategy. Escher Maps as seen above can be used in order to identify the reactions leading to- and branching off these intermediates. Here we present the following splits as examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682bba22",
   "metadata": {},
   "source": [
    "- Glycolysis/PPP: `EX_glc__D` compared to `PGI` and `G6PDH2r`\n",
    "- Glyoxylate shunt/full TCA: `ACONTb` compared to `ICDHyr` and `ICL`\n",
    "- TCA/acetate secretion: (`ACALD`, `PFL`, `PDH`) compared to `PTAr` and `CS`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b445c0",
   "metadata": {},
   "source": [
    "These fluxes can either be single fluxes, selected by their IDs (see the first two examples) or a combination of multiple fluxes (see last example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42af10ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EX_glc__D_e/G6PDH2r</th>\n",
       "      <td>0.474611</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_glc__D_e/PGI</th>\n",
       "      <td>0.435667</td>\n",
       "      <td>0.068342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Mean     Stdev\n",
       "EX_glc__D_e/G6PDH2r  0.474611  0.000004\n",
       "EX_glc__D_e/PGI      0.435667  0.068342"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_split_ratio(relaxed_sampled_fluxes, 'EX_glc__D_e', 'G6PDH2r', 'PGI', branch_point_name=\"Glycolysis/PPP\")\n",
    "calculate_split_ratio(relaxed_sampled_fluxes, 'EX_glc__D_e', 'G6PDH2r', 'PGI', branch_point_name=\"Glycolysis/PPP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f65c17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACONTb/ICDHyr</th>\n",
       "      <td>5.325006e-01</td>\n",
       "      <td>2.710899e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACONTb/ICL</th>\n",
       "      <td>7.186864e-09</td>\n",
       "      <td>2.245143e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Mean         Stdev\n",
       "ACONTb/ICDHyr  5.325006e-01  2.710899e-01\n",
       "ACONTb/ICL     7.186864e-09  2.245143e-08"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_split_ratio(relaxed_sampled_fluxes, 'ACONTb', 'ICDHyr', 'ICL', branch_point_name=\"Glyoxylate shunt/full TCA\")\n",
    "calculate_split_ratio(relaxed_sampled_fluxes, 'ACONTb', 'ICDHyr', 'ICL', branch_point_name=\"Glyoxylate shunt/full TCA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc7ad597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Glycolysis/PTAr</th>\n",
       "      <td>1.634627</td>\n",
       "      <td>1.035903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycolysis/CS</th>\n",
       "      <td>0.120737</td>\n",
       "      <td>0.012378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Mean     Stdev\n",
       "Glycolysis/PTAr  1.634627  1.035903\n",
       "Glycolysis/CS    0.120737  0.012378"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glycolysis = abs(relaxed_sampled_fluxes['ACALD']) + abs(relaxed_sampled_fluxes['PFL']) + abs(relaxed_sampled_fluxes['PDH'])\n",
    "glycolysis.name = 'Glycolysis'\n",
    "plot_split_ratio(relaxed_sampled_fluxes, glycolysis, 'PTAr', 'CS', branch_point_name=\"TCA/acetate secretion\")\n",
    "calculate_split_ratio(relaxed_sampled_fluxes, glycolysis, 'PTAr', 'CS', branch_point_name=\"TCA/acetate secretion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c27d52",
   "metadata": {},
   "source": [
    "#### Calculating the flux resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "292335d1",
   "metadata": {},
   "source": [
    "First we want to calculate which fluxes qualify as \"observable fluxes\", i.e. flux value at least 4 times the confidence interval and 0 not included in the confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a25517d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EX_cm_e</th>\n",
       "      <th>EX_cmp_e</th>\n",
       "      <th>EX_co2_e</th>\n",
       "      <th>EX_cobalt2_e</th>\n",
       "      <th>DM_4crsol_c</th>\n",
       "      <th>DM_5drib_c</th>\n",
       "      <th>DM_aacald_c</th>\n",
       "      <th>DM_amob_c</th>\n",
       "      <th>DM_mththf_c</th>\n",
       "      <th>EX_colipa_e</th>\n",
       "      <th>...</th>\n",
       "      <th>ACONTa_reverse</th>\n",
       "      <th>FUM_reverse</th>\n",
       "      <th>GAPD_reverse</th>\n",
       "      <th>ICDHyr_reverse</th>\n",
       "      <th>PGM_reverse</th>\n",
       "      <th>PTAr_reverse</th>\n",
       "      <th>ACONTb_reverse</th>\n",
       "      <th>PGK_reverse</th>\n",
       "      <th>ACKr_reverse</th>\n",
       "      <th>ACS_reverse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.543237</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>1.101114e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>16.367512</td>\n",
       "      <td>8.367700</td>\n",
       "      <td>4.072338</td>\n",
       "      <td>2.371525</td>\n",
       "      <td>22.201158</td>\n",
       "      <td>4.986488</td>\n",
       "      <td>16.870859</td>\n",
       "      <td>23.245243</td>\n",
       "      <td>26.410573</td>\n",
       "      <td>14.852405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.942223</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>4.395397e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>22.920790</td>\n",
       "      <td>13.203244</td>\n",
       "      <td>9.455891</td>\n",
       "      <td>9.111397</td>\n",
       "      <td>21.735601</td>\n",
       "      <td>4.842415</td>\n",
       "      <td>25.150167</td>\n",
       "      <td>23.181246</td>\n",
       "      <td>13.659831</td>\n",
       "      <td>20.950423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.011655</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>3.478425e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>15.204975</td>\n",
       "      <td>9.995851</td>\n",
       "      <td>4.136965</td>\n",
       "      <td>11.608163</td>\n",
       "      <td>22.235026</td>\n",
       "      <td>12.156031</td>\n",
       "      <td>17.390690</td>\n",
       "      <td>23.249894</td>\n",
       "      <td>21.592494</td>\n",
       "      <td>14.942728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.202258</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>-5.376288e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>21.907680</td>\n",
       "      <td>5.826934</td>\n",
       "      <td>3.935700</td>\n",
       "      <td>3.257740</td>\n",
       "      <td>21.459601</td>\n",
       "      <td>2.889205</td>\n",
       "      <td>21.762006</td>\n",
       "      <td>23.143307</td>\n",
       "      <td>18.602788</td>\n",
       "      <td>21.085737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.325906</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>1.317730e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>11.184041</td>\n",
       "      <td>7.140398</td>\n",
       "      <td>5.356585</td>\n",
       "      <td>6.182290</td>\n",
       "      <td>21.997845</td>\n",
       "      <td>3.832498</td>\n",
       "      <td>10.953312</td>\n",
       "      <td>23.217287</td>\n",
       "      <td>9.294117</td>\n",
       "      <td>9.636500</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.612671</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>1.797798e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>27.182617</td>\n",
       "      <td>27.755076</td>\n",
       "      <td>22.768438</td>\n",
       "      <td>26.011132</td>\n",
       "      <td>29.612248</td>\n",
       "      <td>26.707553</td>\n",
       "      <td>27.086390</td>\n",
       "      <td>24.264196</td>\n",
       "      <td>26.035236</td>\n",
       "      <td>1.250572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.589870</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>2.048300e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>26.251317</td>\n",
       "      <td>27.346056</td>\n",
       "      <td>22.392033</td>\n",
       "      <td>25.431437</td>\n",
       "      <td>29.521050</td>\n",
       "      <td>25.816474</td>\n",
       "      <td>26.211918</td>\n",
       "      <td>24.251656</td>\n",
       "      <td>24.429541</td>\n",
       "      <td>1.430741</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.589661</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>1.702788e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>25.911706</td>\n",
       "      <td>26.529107</td>\n",
       "      <td>21.936295</td>\n",
       "      <td>24.823842</td>\n",
       "      <td>29.639972</td>\n",
       "      <td>26.295611</td>\n",
       "      <td>25.981870</td>\n",
       "      <td>24.268005</td>\n",
       "      <td>25.094538</td>\n",
       "      <td>0.893650</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.584237</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>1.669795e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>26.178008</td>\n",
       "      <td>26.791359</td>\n",
       "      <td>21.737854</td>\n",
       "      <td>24.645933</td>\n",
       "      <td>29.583094</td>\n",
       "      <td>26.034856</td>\n",
       "      <td>26.227460</td>\n",
       "      <td>24.260185</td>\n",
       "      <td>25.131312</td>\n",
       "      <td>1.352205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.010088</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>4.444221e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>23.423028</td>\n",
       "      <td>22.058764</td>\n",
       "      <td>15.957888</td>\n",
       "      <td>17.500774</td>\n",
       "      <td>25.802773</td>\n",
       "      <td>16.166442</td>\n",
       "      <td>22.425759</td>\n",
       "      <td>23.740430</td>\n",
       "      <td>15.536149</td>\n",
       "      <td>13.119900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2593 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    EX_cm_e  EX_cmp_e   EX_co2_e  EX_cobalt2_e  DM_4crsol_c  DM_5drib_c  \\\n",
       "0       0.0       0.0  13.543237     -0.000017     0.000156    0.000158   \n",
       "1       0.0       0.0  13.942223     -0.000017     0.000156    0.000157   \n",
       "2       0.0       0.0  14.011655     -0.000017     0.000156    0.000157   \n",
       "3       0.0       0.0  13.202258     -0.000018     0.000156    0.000158   \n",
       "4       0.0       0.0  13.325906     -0.000017     0.000156    0.000157   \n",
       "..      ...       ...        ...           ...          ...         ...   \n",
       "95      0.0       0.0  14.612671     -0.000017     0.000156    0.000157   \n",
       "96      0.0       0.0  14.589870     -0.000017     0.000156    0.000157   \n",
       "97      0.0       0.0  14.589661     -0.000017     0.000156    0.000157   \n",
       "98      0.0       0.0  14.584237     -0.000017     0.000156    0.000158   \n",
       "99      0.0       0.0  14.010088     -0.000017     0.000156    0.000158   \n",
       "\n",
       "    DM_aacald_c  DM_amob_c  DM_mththf_c   EX_colipa_e  ...  ACONTa_reverse  \\\n",
       "0           0.0   0.000001     0.000314  1.101114e-12  ...       16.367512   \n",
       "1           0.0   0.000001     0.000314  4.395397e-13  ...       22.920790   \n",
       "2           0.0   0.000001     0.000314  3.478425e-12  ...       15.204975   \n",
       "3           0.0   0.000001     0.000314 -5.376288e-13  ...       21.907680   \n",
       "4           0.0   0.000001     0.000314  1.317730e-12  ...       11.184041   \n",
       "..          ...        ...          ...           ...  ...             ...   \n",
       "95          0.0   0.000001     0.000314  1.797798e-12  ...       27.182617   \n",
       "96          0.0   0.000001     0.000314  2.048300e-12  ...       26.251317   \n",
       "97          0.0   0.000001     0.000314  1.702788e-12  ...       25.911706   \n",
       "98          0.0   0.000001     0.000314  1.669795e-12  ...       26.178008   \n",
       "99          0.0   0.000001     0.000314  4.444221e-12  ...       23.423028   \n",
       "\n",
       "    FUM_reverse  GAPD_reverse  ICDHyr_reverse  PGM_reverse  PTAr_reverse  \\\n",
       "0      8.367700      4.072338        2.371525    22.201158      4.986488   \n",
       "1     13.203244      9.455891        9.111397    21.735601      4.842415   \n",
       "2      9.995851      4.136965       11.608163    22.235026     12.156031   \n",
       "3      5.826934      3.935700        3.257740    21.459601      2.889205   \n",
       "4      7.140398      5.356585        6.182290    21.997845      3.832498   \n",
       "..          ...           ...             ...          ...           ...   \n",
       "95    27.755076     22.768438       26.011132    29.612248     26.707553   \n",
       "96    27.346056     22.392033       25.431437    29.521050     25.816474   \n",
       "97    26.529107     21.936295       24.823842    29.639972     26.295611   \n",
       "98    26.791359     21.737854       24.645933    29.583094     26.034856   \n",
       "99    22.058764     15.957888       17.500774    25.802773     16.166442   \n",
       "\n",
       "    ACONTb_reverse  PGK_reverse  ACKr_reverse  ACS_reverse  \n",
       "0        16.870859    23.245243     26.410573    14.852405  \n",
       "1        25.150167    23.181246     13.659831    20.950423  \n",
       "2        17.390690    23.249894     21.592494    14.942728  \n",
       "3        21.762006    23.143307     18.602788    21.085737  \n",
       "4        10.953312    23.217287      9.294117     9.636500  \n",
       "..             ...          ...           ...          ...  \n",
       "95       27.086390    24.264196     26.035236     1.250572  \n",
       "96       26.211918    24.251656     24.429541     1.430741  \n",
       "97       25.981870    24.268005     25.094538     0.893650  \n",
       "98       26.227460    24.260185     25.131312     1.352205  \n",
       "99       22.425759    23.740430     15.536149    13.119900  \n",
       "\n",
       "[100 rows x 2593 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relaxed_sampled_fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8a18f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable_fluxes = get_observable_fluxes(fittedFluxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be1dda6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>simulation_id</th>\n",
       "      <th>simulation_dateAndTime</th>\n",
       "      <th>rxn_id</th>\n",
       "      <th>flux</th>\n",
       "      <th>flux_stdev</th>\n",
       "      <th>flux_lb</th>\n",
       "      <th>flux_ub</th>\n",
       "      <th>flux_units</th>\n",
       "      <th>fit_alf</th>\n",
       "      <th>fit_chi2s</th>\n",
       "      <th>fit_cor</th>\n",
       "      <th>fit_cov</th>\n",
       "      <th>free</th>\n",
       "      <th>used_</th>\n",
       "      <th>comment_</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>26dap_DASH_MSYN</td>\n",
       "      <td>0.229504</td>\n",
       "      <td>0.002608</td>\n",
       "      <td>0.224392</td>\n",
       "      <td>0.234616</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ALATA_L</td>\n",
       "      <td>0.343552</td>\n",
       "      <td>0.003904</td>\n",
       "      <td>0.335900</td>\n",
       "      <td>0.351204</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ArgSYN</td>\n",
       "      <td>0.197824</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.193418</td>\n",
       "      <td>0.202230</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ASNN</td>\n",
       "      <td>0.161216</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.157625</td>\n",
       "      <td>0.164807</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ASPTA</td>\n",
       "      <td>1.279872</td>\n",
       "      <td>0.014544</td>\n",
       "      <td>1.251366</td>\n",
       "      <td>1.327850</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>DAPDC</td>\n",
       "      <td>0.229504</td>\n",
       "      <td>0.002608</td>\n",
       "      <td>0.224392</td>\n",
       "      <td>0.234616</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>BIOMASS_Ec_iJO1366_core_53p95M</td>\n",
       "      <td>0.704000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.688320</td>\n",
       "      <td>0.719680</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.917000</td>\n",
       "      <td>2.343000</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>EX_glc_e</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>7.007922</td>\n",
       "      <td>7.791993</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>EX_nh4_e</td>\n",
       "      <td>4.901952</td>\n",
       "      <td>0.055704</td>\n",
       "      <td>4.792774</td>\n",
       "      <td>5.011130</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>EX_so4_e</td>\n",
       "      <td>0.164032</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>0.160379</td>\n",
       "      <td>0.167685</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>GLNS</td>\n",
       "      <td>0.475200</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.464616</td>\n",
       "      <td>0.485784</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>34</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>GluSYN</td>\n",
       "      <td>4.596768</td>\n",
       "      <td>0.052236</td>\n",
       "      <td>4.494387</td>\n",
       "      <td>4.699149</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>37</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>HisSYN</td>\n",
       "      <td>0.063360</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.061949</td>\n",
       "      <td>0.064771</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>41</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>IleSYN</td>\n",
       "      <td>0.194304</td>\n",
       "      <td>0.002208</td>\n",
       "      <td>0.189976</td>\n",
       "      <td>0.198632</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>42</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>LeuSYN</td>\n",
       "      <td>0.301312</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>0.294601</td>\n",
       "      <td>0.308023</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>48</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>MetSYN</td>\n",
       "      <td>0.102784</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>0.100495</td>\n",
       "      <td>0.105073</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>51</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>MTHFC</td>\n",
       "      <td>0.063360</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.061949</td>\n",
       "      <td>0.064771</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>52</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>MTHFD</td>\n",
       "      <td>0.102784</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>0.100495</td>\n",
       "      <td>0.105073</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>62</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>PheSYN</td>\n",
       "      <td>0.123904</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.121144</td>\n",
       "      <td>0.126664</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>65</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ProSYN</td>\n",
       "      <td>0.147840</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.144547</td>\n",
       "      <td>0.151133</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>66</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>PRPPS</td>\n",
       "      <td>0.063360</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.061949</td>\n",
       "      <td>0.064771</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>74</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>SERAT</td>\n",
       "      <td>0.164032</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>0.160379</td>\n",
       "      <td>0.167685</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>83</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ThrSYN</td>\n",
       "      <td>0.363968</td>\n",
       "      <td>0.004136</td>\n",
       "      <td>0.355862</td>\n",
       "      <td>0.407275</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>94</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>TrpSYN</td>\n",
       "      <td>0.038016</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.037169</td>\n",
       "      <td>0.038863</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>95</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>TyrSYN</td>\n",
       "      <td>0.092224</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>0.090170</td>\n",
       "      <td>0.094278</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>96</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>ValSYN</td>\n",
       "      <td>0.283008</td>\n",
       "      <td>0.003216</td>\n",
       "      <td>0.276705</td>\n",
       "      <td>0.289311</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>110</td>\n",
       "      <td>WTEColi_113C80_U13C20_01</td>\n",
       "      <td>2021-04-16 18:29:37</td>\n",
       "      <td>CYSS</td>\n",
       "      <td>0.164032</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>0.160379</td>\n",
       "      <td>0.167685</td>\n",
       "      <td>mmol*gDCW-1*hr-1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index             simulation_id simulation_dateAndTime  \\\n",
       "0       0  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "4       4  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "5       5  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "6       6  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "7       7  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "10     10  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "11     11  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "14     14  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "17     17  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "18     18  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "20     20  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "31     33  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "32     34  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "35     37  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "39     41  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "40     42  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "45     48  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "48     51  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "49     52  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "57     62  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "60     65  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "61     66  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "67     74  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "73     83  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "79     94  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "80     95  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "81     96  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "95    110  WTEColi_113C80_U13C20_01    2021-04-16 18:29:37   \n",
       "\n",
       "                            rxn_id      flux  flux_stdev   flux_lb   flux_ub  \\\n",
       "0                  26dap_DASH_MSYN  0.229504    0.002608  0.224392  0.234616   \n",
       "4                          ALATA_L  0.343552    0.003904  0.335900  0.351204   \n",
       "5                           ArgSYN  0.197824    0.002248  0.193418  0.202230   \n",
       "6                             ASNN  0.161216    0.001832  0.157625  0.164807   \n",
       "7                            ASPTA  1.279872    0.014544  1.251366  1.327850   \n",
       "10                           DAPDC  0.229504    0.002608  0.224392  0.234616   \n",
       "11  BIOMASS_Ec_iJO1366_core_53p95M  0.704000    0.008000  0.688320  0.719680   \n",
       "14                         EX_ac_e  2.130000    0.500000  1.917000  2.343000   \n",
       "17                        EX_glc_e  7.400000    0.200000  7.007922  7.791993   \n",
       "18                        EX_nh4_e  4.901952    0.055704  4.792774  5.011130   \n",
       "20                        EX_so4_e  0.164032    0.001864  0.160379  0.167685   \n",
       "31                            GLNS  0.475200    0.005400  0.464616  0.485784   \n",
       "32                          GluSYN  4.596768    0.052236  4.494387  4.699149   \n",
       "35                          HisSYN  0.063360    0.000720  0.061949  0.064771   \n",
       "39                          IleSYN  0.194304    0.002208  0.189976  0.198632   \n",
       "40                          LeuSYN  0.301312    0.003424  0.294601  0.308023   \n",
       "45                          MetSYN  0.102784    0.001168  0.100495  0.105073   \n",
       "48                           MTHFC  0.063360    0.000720  0.061949  0.064771   \n",
       "49                           MTHFD  0.102784    0.001168  0.100495  0.105073   \n",
       "57                          PheSYN  0.123904    0.001408  0.121144  0.126664   \n",
       "60                          ProSYN  0.147840    0.001680  0.144547  0.151133   \n",
       "61                           PRPPS  0.063360    0.000720  0.061949  0.064771   \n",
       "67                           SERAT  0.164032    0.001864  0.160379  0.167685   \n",
       "73                          ThrSYN  0.363968    0.004136  0.355862  0.407275   \n",
       "79                          TrpSYN  0.038016    0.000432  0.037169  0.038863   \n",
       "80                          TyrSYN  0.092224    0.001048  0.090170  0.094278   \n",
       "81                          ValSYN  0.283008    0.003216  0.276705  0.289311   \n",
       "95                            CYSS  0.164032    0.001864  0.160379  0.167685   \n",
       "\n",
       "          flux_units  fit_alf fit_chi2s fit_cor fit_cov   free  used_ comment_  \n",
       "0   mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "4   mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "5   mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "6   mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "7   mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "10  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "11  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "14  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "17  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "18  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "20  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "31  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "32  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "35  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "39  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "40  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "45  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "48  mmol*gDCW-1*hr-1     0.05      None    None    None   True   True     None  \n",
       "49  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "57  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "60  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "61  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "67  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "73  mmol*gDCW-1*hr-1     0.05      None    None    None   True   True     None  \n",
       "79  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "80  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "81  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  \n",
       "95  mmol*gDCW-1*hr-1     0.05      None    None    None  False   True     None  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observable_fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d024fdb",
   "metadata": {},
   "source": [
    "The following amount of fluxes qualifies as being observable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "472aa890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.72 %\n"
     ]
    }
   ],
   "source": [
    "percent_observable_fluxes(fittedFluxes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f5cf8a",
   "metadata": {},
   "source": [
    "The flux precision is defined as the mean of the standard deviations of the fitted Fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c84335b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.031311574686820484"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_flux_precision(fittedFluxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5db011c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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