{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb270ec2-29da-4100-9485-39da5cce1663",
   "metadata": {},
   "source": [
    "# Inspecting predicted fluxes\n",
    "\n",
    "In this tutorial we will load and inspect the fluxes predicted by the _Bacillus subtilis_ ME-model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37fa4bb-3c8f-4a62-923c-6c1747ebb652",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d048f35f-6a3b-4642-86cd-49e4c4e4a187",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-12T06:27:35.182100Z",
     "start_time": "2022-12-12T06:27:35.157355Z"
    }
   },
   "outputs": [],
   "source": [
    "from coralme.builder.main import MEBuilder\n",
    "from coralme.util.flux_analysis import flux_based_reactions\n",
    "import coralme\n",
    "import pandas\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051e8ee6-15ba-4017-8fc0-5f54da314cd8",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a83a0a-a0b6-4b74-b0b8-9331a34162d7",
   "metadata": {},
   "source": [
    "Load the ME-model coming out of the Troubleshooter as a JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0d86d92-b978-402c-b16a-439986ea2904",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-12T06:30:28.060280Z",
     "start_time": "2022-12-12T06:30:01.706785Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding Metabolites into the ME-model...                                    : 100.0%|██████████|  4630/ 4630 [00:00<00:00]\n",
      "Adding ProcessData into the ME-model...                                    : 100.0%|██████████|  4752/ 4752 [00:00<00:00]\n",
      "Adding Reactions into the ME-model...                                      : 100.0%|██████████|  7758/ 7758 [00:18<00:00]\n",
      "Updating ME-model Reactions...                                             : 100.0%|██████████|  6369/ 6369 [00:25<00:00]\n"
     ]
    }
   ],
   "source": [
    "me = coralme.io.json.load_json_me_model(\"./bsubtilis/MEModel-step3-bsubtilis-TS.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66554f57-e2e9-4c76-a0e4-f16f8d151874",
   "metadata": {},
   "source": [
    "## Solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cad55474-5c9a-46ea-9305-79ea2d44df66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MINOS and quad MINOS solvers are a courtesy of Prof Michael A. Saunders. Please cite Ma, D., Yang, L., Fleming, R. et al. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. Sci Rep 7, 40863 (2017). https://doi.org/10.1038/srep40863\n",
      "\n",
      "Iteration\t Solution to check\tSolver Status\n",
      "---------\t------------------\t-------------\n",
      "        1\t1.4050280687025918\tNot feasible\n",
      "        2\t0.7025140343512959\tNot feasible\n",
      "        3\t0.3512570171756479\tNot feasible\n",
      "        4\t0.1756285085878240\tNot feasible\n",
      "        5\t0.0878142542939120\tOptimal\n",
      "        6\t0.1317213814408680\tNot feasible\n",
      "        7\t0.1097678178673900\tNot feasible\n",
      "        8\t0.0987910360806510\tOptimal\n",
      "        9\t0.1042794269740205\tNot feasible\n",
      "       10\t0.1015352315273357\tNot feasible\n",
      "       11\t0.1001631338039934\tOptimal\n",
      "       12\t0.1008491826656645\tOptimal\n",
      "       13\t0.1011922070965001\tOptimal\n",
      "       14\t0.1013637193119179\tNot feasible\n",
      "       15\t0.1012779632042090\tOptimal\n",
      "       16\t0.1013208412580635\tNot feasible\n",
      "       17\t0.1012994022311363\tNot feasible\n",
      "       18\t0.1012886827176726\tNot feasible\n",
      "       19\t0.1012833229609408\tNot feasible\n",
      "       20\t0.1012806430825749\tNot feasible\n",
      "       21\t0.1012793031433920\tOptimal\n",
      "       22\t0.1012799731129835\tNot feasible\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "me.optimize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a9d48f-28b2-4f2d-8bce-be2c29bf8a5e",
   "metadata": {},
   "source": [
    "## Inspecting fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d977c276-1711-41fc-87fd-3862d0cb8989",
   "metadata": {},
   "source": [
    "### Predicted fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d1ab603-99bb-4711-aa42-7f6c4aebea59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biomass_dilution</th>\n",
       "      <td>1.012793e-01</td>\n",
       "      <td>-5.648980e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BSU15140-MONOMER_to_generic_16Sm4Cm1402</th>\n",
       "      <td>1.506864e-12</td>\n",
       "      <td>-1.792344e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA_BSU_rRNA_1_to_generic_16s_rRNAs</th>\n",
       "      <td>1.465206e-06</td>\n",
       "      <td>-1.448997e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA_BSU_rRNA_16_to_generic_16s_rRNAs</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-6.326530e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA_BSU_rRNA_19_to_generic_16s_rRNAs</th>\n",
       "      <td>5.674343e-07</td>\n",
       "      <td>-1.849227e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translocation_BSU25290_Plasma_Membrane</th>\n",
       "      <td>3.013728e-12</td>\n",
       "      <td>-4.779545e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translocation_BSU27650_Plasma_Membrane</th>\n",
       "      <td>1.354157e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translocation_BSU33630_Plasma_Membrane</th>\n",
       "      <td>1.370728e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translocation_BSU35300_Plasma_Membrane</th>\n",
       "      <td>1.354988e-06</td>\n",
       "      <td>3.520845e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translocation_BSU41040_Plasma_Membrane</th>\n",
       "      <td>3.277003e-08</td>\n",
       "      <td>9.992851e-34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7758 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               fluxes  reduced_costs\n",
       "biomass_dilution                         1.012793e-01  -5.648980e-01\n",
       "BSU15140-MONOMER_to_generic_16Sm4Cm1402  1.506864e-12  -1.792344e-33\n",
       "RNA_BSU_rRNA_1_to_generic_16s_rRNAs      1.465206e-06  -1.448997e-32\n",
       "RNA_BSU_rRNA_16_to_generic_16s_rRNAs     0.000000e+00  -6.326530e-02\n",
       "RNA_BSU_rRNA_19_to_generic_16s_rRNAs     5.674343e-07  -1.849227e-32\n",
       "...                                               ...            ...\n",
       "translocation_BSU25290_Plasma_Membrane   3.013728e-12  -4.779545e-34\n",
       "translocation_BSU27650_Plasma_Membrane   1.354157e-06   0.000000e+00\n",
       "translocation_BSU33630_Plasma_Membrane   1.370728e-06   0.000000e+00\n",
       "translocation_BSU35300_Plasma_Membrane   1.354988e-06   3.520845e-34\n",
       "translocation_BSU41040_Plasma_Membrane   3.277003e-08   9.992851e-34\n",
       "\n",
       "[7758 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxes = me.solution.to_frame()\n",
    "fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495c1b76-30fc-4bca-bb63-77d343e3defc",
   "metadata": {},
   "source": [
    "### Biomass production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5984f8fb-a193-4aff-b6d1-5a9c5d85a5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biomass_dilution</th>\n",
       "      <td>0.101279</td>\n",
       "      <td>-5.648980e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein_biomass_to_biomass</th>\n",
       "      <td>0.034953</td>\n",
       "      <td>-2.375644e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA_biomass_to_biomass</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>-1.199677e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tRNA_biomass_to_biomass</th>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rRNA_biomass_to_biomass</th>\n",
       "      <td>0.005100</td>\n",
       "      <td>-7.874817e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncRNA_biomass_to_biomass</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.391429e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tmRNA_biomass_to_biomass</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.391429e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA_biomass_to_biomass</th>\n",
       "      <td>0.006003</td>\n",
       "      <td>7.391429e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lipid_biomass_to_biomass</th>\n",
       "      <td>0.023282</td>\n",
       "      <td>7.391429e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constituent_biomass_to_biomass</th>\n",
       "      <td>0.011441</td>\n",
       "      <td>7.391429e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prosthetic_group_biomass_to_biomass</th>\n",
       "      <td>0.000023</td>\n",
       "      <td>-5.556140e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peptidoglycan_biomass_to_biomass</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.391429e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biomass_constituent_demand</th>\n",
       "      <td>0.101279</td>\n",
       "      <td>1.554562e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       fluxes  reduced_costs\n",
       "biomass_dilution                     0.101279  -5.648980e-01\n",
       "protein_biomass_to_biomass           0.034953  -2.375644e-34\n",
       "mRNA_biomass_to_biomass              0.000116  -1.199677e-34\n",
       "tRNA_biomass_to_biomass              0.000700   0.000000e+00\n",
       "rRNA_biomass_to_biomass              0.005100  -7.874817e-35\n",
       "ncRNA_biomass_to_biomass             0.000000   7.391429e-35\n",
       "tmRNA_biomass_to_biomass             0.000000   7.391429e-35\n",
       "DNA_biomass_to_biomass               0.006003   7.391429e-35\n",
       "lipid_biomass_to_biomass             0.023282   7.391429e-35\n",
       "constituent_biomass_to_biomass       0.011441   7.391429e-35\n",
       "prosthetic_group_biomass_to_biomass  0.000023  -5.556140e-35\n",
       "peptidoglycan_biomass_to_biomass     0.000000   7.391429e-35\n",
       "biomass_constituent_demand           0.101279   1.554562e-02"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxes[fluxes.index.str.contains(\"biomass\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd37655b-6303-4354-b448-0f12ca7d7cae",
   "metadata": {},
   "source": [
    "### Transcription rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5630678c-e894-4bbc-b9a3-e71fc1bff8ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>transcription_TU8J2-1243_from_BSU25200-MONOMER</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-6.246011e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transcription_TU8J2-569_from_BSU25200-MONOMER</th>\n",
       "      <td>2.160400e-08</td>\n",
       "      <td>7.605084e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transcription_TU8J2-1577_from_BSU25200-MONOMER</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-6.986124e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transcription_TU8J2-257_from_BSU25200-MONOMER</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.127560e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transcription_TU8J2-677_from_BSU25200-MONOMER</th>\n",
       "      <td>1.736594e-08</td>\n",
       "      <td>-1.447121e-32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      fluxes  reduced_costs\n",
       "transcription_TU8J2-1243_from_BSU25200-MONOMER  0.000000e+00  -6.246011e-33\n",
       "transcription_TU8J2-569_from_BSU25200-MONOMER   2.160400e-08   7.605084e-32\n",
       "transcription_TU8J2-1577_from_BSU25200-MONOMER  0.000000e+00  -6.986124e-32\n",
       "transcription_TU8J2-257_from_BSU25200-MONOMER   0.000000e+00  -1.127560e+03\n",
       "transcription_TU8J2-677_from_BSU25200-MONOMER   1.736594e-08  -1.447121e-32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxes[fluxes.index.str.contains(\"^transcription_\")].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ceaf816-6d4d-4b41-acc2-c669cded8c25",
   "metadata": {},
   "source": [
    "### Translation rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eaf9a7f8-72fa-4be3-b01d-ad208c45d4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>translation_BSU00090</th>\n",
       "      <td>1.419685e-08</td>\n",
       "      <td>-1.712708e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translation_BSU00110</th>\n",
       "      <td>6.498762e-13</td>\n",
       "      <td>-6.375952e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translation_BSU00120</th>\n",
       "      <td>6.498762e-13</td>\n",
       "      <td>-6.911721e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translation_BSU00130</th>\n",
       "      <td>2.293786e-08</td>\n",
       "      <td>2.102613e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>translation_BSU00140</th>\n",
       "      <td>5.327400e-11</td>\n",
       "      <td>9.486318e-33</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                            fluxes  reduced_costs\n",
       "translation_BSU00090  1.419685e-08  -1.712708e-32\n",
       "translation_BSU00110  6.498762e-13  -6.375952e-33\n",
       "translation_BSU00120  6.498762e-13  -6.911721e-33\n",
       "translation_BSU00130  2.293786e-08   2.102613e-32\n",
       "translation_BSU00140  5.327400e-11   9.486318e-33"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxes[fluxes.index.str.contains(\"^translation_\")].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95fd2fb-2498-4666-9ad3-e4ec7fcf05a5",
   "metadata": {},
   "source": [
    "## Biomass profile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3130e406-e228-4b14-963a-9af417d01496",
   "metadata": {},
   "source": [
    "We can calculate the predicted biomass composition in this condition by using _flux_based_reactions_ which outputs the mass balance of a metabolite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3910701-4958-477d-bc6a-87c1d5a9dbcc",
   "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>lb</th>\n",
       "      <th>ub</th>\n",
       "      <th>rxn_flux</th>\n",
       "      <th>met_flux</th>\n",
       "      <th>reaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biomass_dilution</th>\n",
       "      <td>mu</td>\n",
       "      <td>mu</td>\n",
       "      <td>0.101279</td>\n",
       "      <td>-0.101279</td>\n",
       "      <td>1.0 biomass --&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.034953</td>\n",
       "      <td>0.054615</td>\n",
       "      <td>1.0 protein_biomass + 0.5625 unmodeled_protein...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lipid_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.023282</td>\n",
       "      <td>0.023282</td>\n",
       "      <td>1.0 lipid_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constituent_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.011441</td>\n",
       "      <td>0.011441</td>\n",
       "      <td>1.0 constituent_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>1.0 DNA_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rRNA_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.005100</td>\n",
       "      <td>0.005100</td>\n",
       "      <td>1.0 rRNA_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tRNA_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>1.0 tRNA_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>1.0 mRNA_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prosthetic_group_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>1.0 prosthetic_group_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tmRNA_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0 tmRNA_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncRNA_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0 ncRNA_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peptidoglycan_biomass_to_biomass</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0 peptidoglycan_biomass --&gt; 1.0 biomass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      lb      ub  rxn_flux  met_flux  \\\n",
       "biomass_dilution                      mu      mu  0.101279 -0.101279   \n",
       "protein_biomass_to_biomass           0.0  1000.0  0.034953  0.054615   \n",
       "lipid_biomass_to_biomass             0.0  1000.0  0.023282  0.023282   \n",
       "constituent_biomass_to_biomass       0.0  1000.0  0.011441  0.011441   \n",
       "DNA_biomass_to_biomass               0.0  1000.0  0.006003  0.006003   \n",
       "rRNA_biomass_to_biomass              0.0  1000.0  0.005100  0.005100   \n",
       "tRNA_biomass_to_biomass              0.0  1000.0  0.000700  0.000700   \n",
       "mRNA_biomass_to_biomass              0.0  1000.0  0.000116  0.000116   \n",
       "prosthetic_group_biomass_to_biomass  0.0  1000.0  0.000023  0.000023   \n",
       "tmRNA_biomass_to_biomass             0.0  1000.0  0.000000  0.000000   \n",
       "ncRNA_biomass_to_biomass             0.0  1000.0  0.000000  0.000000   \n",
       "peptidoglycan_biomass_to_biomass     0.0  1000.0  0.000000  0.000000   \n",
       "\n",
       "                                                                              reaction  \n",
       "biomass_dilution                                                      1.0 biomass -->   \n",
       "protein_biomass_to_biomass           1.0 protein_biomass + 0.5625 unmodeled_protein...  \n",
       "lipid_biomass_to_biomass                             1.0 lipid_biomass --> 1.0 biomass  \n",
       "constituent_biomass_to_biomass                 1.0 constituent_biomass --> 1.0 biomass  \n",
       "DNA_biomass_to_biomass                                 1.0 DNA_biomass --> 1.0 biomass  \n",
       "rRNA_biomass_to_biomass                               1.0 rRNA_biomass --> 1.0 biomass  \n",
       "tRNA_biomass_to_biomass                               1.0 tRNA_biomass --> 1.0 biomass  \n",
       "mRNA_biomass_to_biomass                               1.0 mRNA_biomass --> 1.0 biomass  \n",
       "prosthetic_group_biomass_to_biomass       1.0 prosthetic_group_biomass --> 1.0 biomass  \n",
       "tmRNA_biomass_to_biomass                             1.0 tmRNA_biomass --> 1.0 biomass  \n",
       "ncRNA_biomass_to_biomass                             1.0 ncRNA_biomass --> 1.0 biomass  \n",
       "peptidoglycan_biomass_to_biomass             1.0 peptidoglycan_biomass --> 1.0 biomass  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flux_based_reactions(me,\"biomass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70eb8096-3f0c-4dcc-999b-338aa6059143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='met_flux'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = flux_based_reactions(me,\"biomass\")[\"met_flux\"]\n",
    "BiomassComponents = tmp[tmp>0]\n",
    "BiomassComponents.plot.pie()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "coralme-dev",
   "language": "python",
   "name": "coralme-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "toc-autonumbering": true,
  "toc-showcode": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}