community_model_Integrated_SNM_13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cobra\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Community modeling\n",
    "\n",
    "In this notebook we will implement a method to create community models of two or more species specific metabolic models using cobrapy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth:  0.2823653925322476\n",
      "Growth:  2.558694612613393\n"
     ]
    }
   ],
   "source": [
    "model_DP = cobra.io.read_sbml_model(\"models/consistent_DP_SNM.xml\")\n",
    "model_SA = cobra.io.read_sbml_model(\"models/consistent_iYS854_SNM.xml\")\n",
    "print(\"Growth: \", model_DP.slim_optimize())\n",
    "print(\"Growth: \", model_SA.slim_optimize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [],
   "source": [
    "Des = pd.read_csv(\"Descriptive_name.csv\", sep =\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "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>Compound</th>\n",
       "      <th>BiGG</th>\n",
       "      <th>ModelSeed</th>\n",
       "      <th>KEGG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alanine</td>\n",
       "      <td>ala__L</td>\n",
       "      <td>cpd00035</td>\n",
       "      <td>C00041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arginine</td>\n",
       "      <td>arg__L</td>\n",
       "      <td>cpd00051</td>\n",
       "      <td>C00062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cysteine</td>\n",
       "      <td>cys__L</td>\n",
       "      <td>cpd00084</td>\n",
       "      <td>C00097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Glutamic acid</td>\n",
       "      <td>glu__L</td>\n",
       "      <td>cpd00023</td>\n",
       "      <td>C00025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glycine</td>\n",
       "      <td>gly</td>\n",
       "      <td>cpd00033</td>\n",
       "      <td>C00037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Histidine</td>\n",
       "      <td>his__L</td>\n",
       "      <td>cpd00119</td>\n",
       "      <td>C00135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Leucine</td>\n",
       "      <td>leu__L</td>\n",
       "      <td>cpd00107</td>\n",
       "      <td>C00123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lysine</td>\n",
       "      <td>lys__L</td>\n",
       "      <td>cpd00039</td>\n",
       "      <td>C00047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ornithine</td>\n",
       "      <td>orn</td>\n",
       "      <td>cpd00064</td>\n",
       "      <td>C00077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Phenylalanine</td>\n",
       "      <td>phe__L</td>\n",
       "      <td>cpd00066</td>\n",
       "      <td>C00079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Proline</td>\n",
       "      <td>pro__L</td>\n",
       "      <td>cpd00129</td>\n",
       "      <td>C00148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Serine</td>\n",
       "      <td>ser__L</td>\n",
       "      <td>cpd00054</td>\n",
       "      <td>C00065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Threonine</td>\n",
       "      <td>thr__L</td>\n",
       "      <td>cpd00161</td>\n",
       "      <td>C00188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tryptophane</td>\n",
       "      <td>trp__L</td>\n",
       "      <td>cpd00065</td>\n",
       "      <td>C00078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Valine</td>\n",
       "      <td>val__L</td>\n",
       "      <td>cpd00156</td>\n",
       "      <td>C00183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>citric acid</td>\n",
       "      <td>cit</td>\n",
       "      <td>cpd00137</td>\n",
       "      <td>C00158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Fumaric acid</td>\n",
       "      <td>fum</td>\n",
       "      <td>cpd00106</td>\n",
       "      <td>C00122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>maleic acid</td>\n",
       "      <td>male</td>\n",
       "      <td>cpd00996</td>\n",
       "      <td>C01384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pyruvic acid</td>\n",
       "      <td>pyr</td>\n",
       "      <td>cpd00020</td>\n",
       "      <td>C00022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>succinic acid</td>\n",
       "      <td>succ</td>\n",
       "      <td>cpd00036</td>\n",
       "      <td>C00042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>glucose</td>\n",
       "      <td>glc__D</td>\n",
       "      <td>cpd00027</td>\n",
       "      <td>C00031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Urea</td>\n",
       "      <td>urea</td>\n",
       "      <td>cpd00073</td>\n",
       "      <td>C00086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sodium</td>\n",
       "      <td>na1</td>\n",
       "      <td>cpd00971</td>\n",
       "      <td>C01330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Chloride</td>\n",
       "      <td>cl</td>\n",
       "      <td>cpd00099</td>\n",
       "      <td>C00698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Potassium</td>\n",
       "      <td>k</td>\n",
       "      <td>cpd00205</td>\n",
       "      <td>C00238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Phosphate</td>\n",
       "      <td>pi</td>\n",
       "      <td>cpd00009</td>\n",
       "      <td>C00009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Magnesium</td>\n",
       "      <td>mg2</td>\n",
       "      <td>cpd00254</td>\n",
       "      <td>C00305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Sulphate</td>\n",
       "      <td>so4</td>\n",
       "      <td>cpd00048</td>\n",
       "      <td>C00059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2,2'-Bipyridine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cpd25442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Calcium</td>\n",
       "      <td>ca2</td>\n",
       "      <td>cpd00063</td>\n",
       "      <td>C00076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ZnCl2</td>\n",
       "      <td>zn2</td>\n",
       "      <td>cpd00034</td>\n",
       "      <td>C00038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>MnCl2</td>\n",
       "      <td>mn2</td>\n",
       "      <td>cpd00030</td>\n",
       "      <td>C00034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>H3BO3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cpd09225</td>\n",
       "      <td>C12486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CoCl2</td>\n",
       "      <td>cobalt2</td>\n",
       "      <td>cpd00149</td>\n",
       "      <td>C00175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CuCl2</td>\n",
       "      <td>cu2</td>\n",
       "      <td>cpd00058</td>\n",
       "      <td>C00070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NiCl2</td>\n",
       "      <td>ni2</td>\n",
       "      <td>cpd00244</td>\n",
       "      <td>C00291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MoNa2O4</td>\n",
       "      <td>mobd</td>\n",
       "      <td>cpd11574</td>\n",
       "      <td>C06232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Cyanocobalamine</td>\n",
       "      <td>adocbl</td>\n",
       "      <td>cpd01826</td>\n",
       "      <td>C02823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>p-Aminobenzoic acid</td>\n",
       "      <td>4abz</td>\n",
       "      <td>cpd00443</td>\n",
       "      <td>C00568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Biotin</td>\n",
       "      <td>btn</td>\n",
       "      <td>cpd00104</td>\n",
       "      <td>C00120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Nicotinic acid</td>\n",
       "      <td>nac</td>\n",
       "      <td>cpd00218</td>\n",
       "      <td>C00253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Ca-D-Pantothenic acid</td>\n",
       "      <td>pnto__R</td>\n",
       "      <td>cpd00644</td>\n",
       "      <td>C00864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Pyridoxamine-2HCl</td>\n",
       "      <td>pydam</td>\n",
       "      <td>cpd00419</td>\n",
       "      <td>C00534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Thiamine-dichloride</td>\n",
       "      <td>thm</td>\n",
       "      <td>cpd00305</td>\n",
       "      <td>C00378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>ribflv</td>\n",
       "      <td>cpd00220</td>\n",
       "      <td>C00255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Oxygen</td>\n",
       "      <td>o2</td>\n",
       "      <td>cpd00007</td>\n",
       "      <td>C00007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Fe3+</td>\n",
       "      <td>fe2</td>\n",
       "      <td>cpd10516</td>\n",
       "      <td>C14819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Water</td>\n",
       "      <td>h2o</td>\n",
       "      <td>cpd00001</td>\n",
       "      <td>C00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Co2</td>\n",
       "      <td>co2</td>\n",
       "      <td>cpd00011</td>\n",
       "      <td>C00011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Compound     BiGG ModelSeed    KEGG\n",
       "0                 Alanine   ala__L  cpd00035  C00041\n",
       "1                Arginine   arg__L  cpd00051  C00062\n",
       "2                Cysteine   cys__L  cpd00084  C00097\n",
       "3           Glutamic acid   glu__L  cpd00023  C00025\n",
       "4                 Glycine      gly  cpd00033  C00037\n",
       "5               Histidine   his__L  cpd00119  C00135\n",
       "6                 Leucine   leu__L  cpd00107  C00123\n",
       "7                  Lysine   lys__L  cpd00039  C00047\n",
       "8               Ornithine      orn  cpd00064  C00077\n",
       "9           Phenylalanine   phe__L  cpd00066  C00079\n",
       "10                Proline   pro__L  cpd00129  C00148\n",
       "11                 Serine   ser__L  cpd00054  C00065\n",
       "12              Threonine   thr__L  cpd00161  C00188\n",
       "13            Tryptophane   trp__L  cpd00065  C00078\n",
       "14                 Valine   val__L  cpd00156  C00183\n",
       "15            citric acid      cit  cpd00137  C00158\n",
       "16           Fumaric acid      fum  cpd00106  C00122\n",
       "17            maleic acid     male  cpd00996  C01384\n",
       "18           pyruvic acid      pyr  cpd00020  C00022\n",
       "19          succinic acid     succ  cpd00036  C00042\n",
       "20                glucose   glc__D  cpd00027  C00031\n",
       "21                   Urea     urea  cpd00073  C00086\n",
       "22                 Sodium      na1  cpd00971  C01330\n",
       "23               Chloride       cl  cpd00099  C00698\n",
       "24              Potassium        k  cpd00205  C00238\n",
       "25              Phosphate       pi  cpd00009  C00009\n",
       "26              Magnesium      mg2  cpd00254  C00305\n",
       "27               Sulphate      so4  cpd00048  C00059\n",
       "28        2,2'-Bipyridine      NaN  cpd25442     NaN\n",
       "29                Calcium      ca2  cpd00063  C00076\n",
       "30                  ZnCl2      zn2  cpd00034  C00038\n",
       "31                  MnCl2      mn2  cpd00030  C00034\n",
       "32                  H3BO3      NaN  cpd09225  C12486\n",
       "33                  CoCl2  cobalt2  cpd00149  C00175\n",
       "34                  CuCl2      cu2  cpd00058  C00070\n",
       "35                  NiCl2      ni2  cpd00244  C00291\n",
       "36                MoNa2O4     mobd  cpd11574  C06232\n",
       "37        Cyanocobalamine   adocbl  cpd01826  C02823\n",
       "38    p-Aminobenzoic acid     4abz  cpd00443  C00568\n",
       "39                 Biotin      btn  cpd00104  C00120\n",
       "40         Nicotinic acid      nac  cpd00218  C00253\n",
       "41  Ca-D-Pantothenic acid  pnto__R  cpd00644  C00864\n",
       "42      Pyridoxamine-2HCl    pydam  cpd00419  C00534\n",
       "43    Thiamine-dichloride      thm  cpd00305  C00378\n",
       "44             Riboflavin   ribflv  cpd00220  C00255\n",
       "45                 Oxygen       o2  cpd00007  C00007\n",
       "46                   Fe3+      fe2  cpd10516  C14819\n",
       "47                  Water      h2o  cpd00001  C00001\n",
       "48                    Co2      co2  cpd00011  C00011"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snm3 = pd.read_csv(\"SNM3.csv\", sep =\";\")\n",
    "snm3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [],
   "source": [
    "BIOMASS_DP = \"Growth\" \n",
    "BIOMASS_SA = \"BIOMASS_iYS_wild_type\"\n",
    "models = [model_DP.copy(), model_SA.copy()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Constructing of a compartment community model\n",
    "We will costruct a community model as following:\n",
    "* We will name each non-exchange reaction and associated metabolites or compartments by: id + model_id, where model_id is the identifier of the model. Thus cobra will consider them as seperate systems.\n",
    "* We will add the exchange metabolites and reactions unchanged as the envirionment as they should be shared by both models! We define the set of all exchange reactions as the union of the single set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modify_reaction_id(rec, id):\n",
    "    # Mark reaktion by organism id, copy all attributes\n",
    "    new_reaction = cobra.Reaction(rec.id + id)\n",
    "    new_reaction.name = rec.name \n",
    "    new_reaction.subsystem = rec.subsystem\n",
    "    new_reaction.lower_bound = rec.lower_bound\n",
    "    new_reaction.upper_bound = rec.upper_bound\n",
    "    # Create new metabolites for reaction\n",
    "    metabolites = rec.metabolites\n",
    "    new_mets = dict()\n",
    "    for met, val in metabolites.items(): \n",
    "        # Reaction should be copied, but assign metabilites to organism by id\n",
    "        comp = met.compartment \n",
    "        if comp != \"e\":\n",
    "            # If it is not an exchanged metabolite, assign id\n",
    "            new_met = cobra.Metabolite(met.id + id, formula=met.formula, name=met.name, compartment=comp + id)\n",
    "        else:\n",
    "            # If it is an exchange metabolite, then do not assign id\n",
    "            new_met = cobra.Metabolite(met.id, formula=met.formula, name=met.name, compartment=comp)\n",
    "        new_mets[new_met] = val # Same stoichimetry\n",
    "    new_reaction.add_metabolites(new_mets)\n",
    "    new_reaction.gene_reaction_rule = rec.gene_reaction_rule\n",
    "    try:\n",
    "        # This does not work for every reactions...\n",
    "        new_reaction.genes = rec.genes\n",
    "    except:\n",
    "        pass\n",
    "    return new_reaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_community_model(models, biomass_functions, weights = \"auto\", medium=\"auto\"):\n",
    "    \"\"\" This methods builds a community model given a list of single models. \n",
    "\n",
    "    It does this by renaming all metabolites/reactions with species specific ids  \"\"\"\n",
    "    ids = [model.id for model in models]\n",
    "    model_id = \"community_\" +\"__\".join([model.id for model in models])\n",
    "    model = cobra.Model(\"Community model\")\n",
    "    for model_i in models:\n",
    "        id = \"_\" + model_i.id\n",
    "        reactions = model_i.reactions.copy()\n",
    "        new_reactions = []\n",
    "        for rec in reactions:\n",
    "            if \"EX_\" in rec.id:\n",
    "                # Add exchange reactions unchanged -> Shared by organisms!\n",
    "                if not rec in model.exchanges:\n",
    "                    model.add_reactions([rec])\n",
    "            else:\n",
    "                # Modify reactions that they are recognized as different.\n",
    "                new_reaction = modify_reaction_id(rec,id)\n",
    "                new_reactions.append(new_reaction)\n",
    "        model.add_reactions(new_reactions)\n",
    "    # Compute weights: Such that all models have same growth on their medium!\n",
    "    if weights == \"auto\":\n",
    "        biomass = []\n",
    "        for model_i in models:\n",
    "            biomass.append(model_i.slim_optimize())\n",
    "        max_biomass = np.max(biomass)\n",
    "        weights = [max_biomass/g for g in biomass]\n",
    "    \n",
    "    # Set objective as sum of growth\n",
    "    objective = []\n",
    "    for i, growth in enumerate(biomass_functions):\n",
    "        v_i = weights[i] * model.reactions.get_by_id(growth + \"_\" + ids[i]).flux_expression\n",
    "        objective.append(v_i)\n",
    "    model.objective = sum(objective)\n",
    "    if medium==\"auto\":\n",
    "        # Set medium to union of model mediums\n",
    "        medium = dict()\n",
    "        for m in models:\n",
    "            for key ,val in m.medium.items():\n",
    "                medium[key] = val\n",
    "        model.medium = medium \n",
    "    else:\n",
    "        model.medium = medium\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigate SNM3 medium\n",
    "\n",
    "The medium should induce competition!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "There are no boundary reactions in this model. Therefore specific types of boundary reactions such as 'exchanges', 'demands' or 'sinks' cannot be identified.\n"
     ]
    }
   ],
   "source": [
    "model = create_community_model(models, [BIOMASS_DP, BIOMASS_SA], weights=[1.,1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth: 5.025290140091366\n",
      "DP growth: 0.02411460929043585\n"
     ]
    }
   ],
   "source": [
    "opt = model.optimize()\n",
    "print(\"SA growth: \" + str(model.reactions.get_by_id(BIOMASS_SA + \"_\" + model_SA.id).flux))\n",
    "print(\"DP growth: \" + str(model.reactions.get_by_id(BIOMASS_DP + \"_\" + model_DP.id).flux))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Objective</h3><p>1.0 Growth_DP_83VPs_KB5 + 1.0 BIOMASS_iYS_wild_type_iYS854 = 5.049404749381802</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metabolite</th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Flux</th>\n",
       "      <th>C-Number</th>\n",
       "      <th>C-Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26dap__M_e</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4abz_e</td>\n",
       "      <td>EX_4abz_e</td>\n",
       "      <td>1.562E-05</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ala__L_e</td>\n",
       "      <td>EX_ala__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arg__L_e</td>\n",
       "      <td>EX_arg__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>13.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ca2_e</td>\n",
       "      <td>EX_ca2_e</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cl_e</td>\n",
       "      <td>EX_cl_e</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cobalt2_e</td>\n",
       "      <td>EX_cobalt2_e</td>\n",
       "      <td>0.0008525</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cu2_e</td>\n",
       "      <td>EX_cu2_e</td>\n",
       "      <td>0.0007906</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cys__L_e</td>\n",
       "      <td>EX_cys__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fe2_e</td>\n",
       "      <td>EX_fe2_e</td>\n",
       "      <td>0.07596</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fum_e</td>\n",
       "      <td>EX_fum_e</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glc__D_e</td>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>13.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glu__L_e</td>\n",
       "      <td>EX_glu__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>11.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gly_e</td>\n",
       "      <td>EX_gly_e</td>\n",
       "      <td>0.03517</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h2o_e</td>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>his__L_e</td>\n",
       "      <td>EX_his__L_e</td>\n",
       "      <td>0.305</td>\n",
       "      <td>6</td>\n",
       "      <td>0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ile__L_e</td>\n",
       "      <td>EX_ile__L_e</td>\n",
       "      <td>1.633</td>\n",
       "      <td>6</td>\n",
       "      <td>2.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>k_e</td>\n",
       "      <td>EX_k_e</td>\n",
       "      <td>0.9344</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>leu__L_e</td>\n",
       "      <td>EX_leu__L_e</td>\n",
       "      <td>1.589</td>\n",
       "      <td>6</td>\n",
       "      <td>2.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lys__L_e</td>\n",
       "      <td>EX_lys__L_e</td>\n",
       "      <td>1.82</td>\n",
       "      <td>6</td>\n",
       "      <td>2.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>met__L_e</td>\n",
       "      <td>EX_met__L_e</td>\n",
       "      <td>0.4105</td>\n",
       "      <td>5</td>\n",
       "      <td>0.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mg2_e</td>\n",
       "      <td>EX_mg2_e</td>\n",
       "      <td>0.04354</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mn2_e</td>\n",
       "      <td>EX_mn2_e</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mobd_e</td>\n",
       "      <td>EX_mobd_e</td>\n",
       "      <td>3.518E-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na1_e</td>\n",
       "      <td>EX_na1_e</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nac_e</td>\n",
       "      <td>EX_nac_e</td>\n",
       "      <td>0.0007119</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ni2_e</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>0.000856</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>o2_e</td>\n",
       "      <td>EX_o2_e</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>orn_e</td>\n",
       "      <td>EX_orn_e</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>11.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>phe__L_e</td>\n",
       "      <td>EX_phe__L_e</td>\n",
       "      <td>0.9653</td>\n",
       "      <td>9</td>\n",
       "      <td>1.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pi_e</td>\n",
       "      <td>EX_pi_e</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pro__L_e</td>\n",
       "      <td>EX_pro__L_e</td>\n",
       "      <td>0.005162</td>\n",
       "      <td>5</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pyr_e</td>\n",
       "      <td>EX_pyr_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ribflv_e</td>\n",
       "      <td>EX_ribflv_e</td>\n",
       "      <td>0.00151</td>\n",
       "      <td>17</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ser__L_e</td>\n",
       "      <td>EX_ser__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>so4_e</td>\n",
       "      <td>EX_so4_e</td>\n",
       "      <td>0.02126</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>thm_e</td>\n",
       "      <td>EX_thm_e</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>12</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>thr__L_e</td>\n",
       "      <td>EX_thr__L_e</td>\n",
       "      <td>0.005924</td>\n",
       "      <td>4</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trp__L_e</td>\n",
       "      <td>EX_trp__L_e</td>\n",
       "      <td>0.07842</td>\n",
       "      <td>11</td>\n",
       "      <td>0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>urea_e</td>\n",
       "      <td>EX_urea_e</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>val__L_e</td>\n",
       "      <td>EX_val__L_e</td>\n",
       "      <td>2.023</td>\n",
       "      <td>5</td>\n",
       "      <td>2.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>zn2_e</td>\n",
       "      <td>EX_zn2_e</td>\n",
       "      <td>0.002396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2fe1s_c_iYS854</td>\n",
       "      <td>SK_2fe1s_c_iYS854</td>\n",
       "      <td>0.001372</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>apoACP_c_iYS854</td>\n",
       "      <td>SK_apoACP_c_iYS854</td>\n",
       "      <td>0.3867</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mql8_c_DP_83VPs_KB5</td>\n",
       "      <td>Sink_mql8_DP_83VPs_KB5</td>\n",
       "      <td>2.335E-06</td>\n",
       "      <td>51</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metabolite</th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Flux</th>\n",
       "      <th>C-Number</th>\n",
       "      <th>C-Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5drib_c_iYS854</td>\n",
       "      <td>DM_5drib_c_iYS854</td>\n",
       "      <td>-0.004523</td>\n",
       "      <td>5</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>amob_c_iYS854</td>\n",
       "      <td>DM_amob_c_iYS854</td>\n",
       "      <td>-1.005E-05</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>co2_e</td>\n",
       "      <td>EX_co2_e</td>\n",
       "      <td>-102.2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>etoh_e</td>\n",
       "      <td>EX_etoh_e</td>\n",
       "      <td>-52.04</td>\n",
       "      <td>2</td>\n",
       "      <td>38.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>for_e</td>\n",
       "      <td>EX_for_e</td>\n",
       "      <td>-21.58</td>\n",
       "      <td>1</td>\n",
       "      <td>8.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glyclt_e</td>\n",
       "      <td>EX_glyclt_e</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h2s_e</td>\n",
       "      <td>EX_h2s_e</td>\n",
       "      <td>-9.499</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nh4_e</td>\n",
       "      <td>EX_nh4_e</td>\n",
       "      <td>-68.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ptrc_e</td>\n",
       "      <td>EX_ptrc_e</td>\n",
       "      <td>-9.801</td>\n",
       "      <td>4</td>\n",
       "      <td>14.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ahcys_c_iYS854</td>\n",
       "      <td>SK_ahcys_c_iYS854</td>\n",
       "      <td>-0.02482</td>\n",
       "      <td>14</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<cobra.summary.model_summary.ModelSummary at 0x7f9424229820>"
      ]
     },
     "execution_count": 579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EX_glc__D_e\n",
      "SA growth: 3.141874771171119\n",
      "DP growth: 0.0\n",
      "EX_h2o_e\n",
      "SA growth: 4.980558865988176\n",
      "DP growth: 0.0\n",
      "EX_leu__L_e\n",
      "SA growth: 5.025846357878463\n",
      "DP growth: 0.011903231142710524\n",
      "EX_4abz_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_ala__L_e\n",
      "SA growth: 5.025290140091338\n",
      "DP growth: 0.02411460929106113\n",
      "EX_cl_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pi_e\n",
      "SA growth: 4.9805588659881685\n",
      "DP growth: 0.0\n",
      "EX_ribflv_e\n",
      "SA growth: 5.025302583727924\n",
      "DP growth: 0.023841417826474753\n",
      "EX_gly_e\n",
      "SA growth: 5.025302269110168\n",
      "DP growth: 0.023848325042465035\n",
      "EX_thr__L_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_arg__L_e\n",
      "SA growth: 4.975077227267806\n",
      "DP growth: 0.0\n",
      "EX_lys__L_e\n",
      "SA growth: 4.889421896019226\n",
      "DP growth: 0.0\n",
      "EX_orn_e\n",
      "SA growth: 5.001538473679777\n",
      "DP growth: 0.0\n",
      "EX_k_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pro__L_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_ca2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_mg2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_mn2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_cobalt2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_zn2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_succ_e\n",
      "SA growth: 5.025290140091337\n",
      "DP growth: 0.02411460929108955\n",
      "EX_co2_e\n",
      "SA growth: 5.025290140091337\n",
      "DP growth: 0.02411460929108955\n",
      "EX_cu2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_o2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_glu__L_e\n",
      "SA growth: 4.621989166353422\n",
      "DP growth: 0.0\n",
      "EX_fe2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_fum_e\n",
      "SA growth: 5.025290140091377\n",
      "DP growth: 0.024114609290208477\n",
      "EX_his__L_e\n",
      "SA growth: 5.025906363219405\n",
      "DP growth: 0.010585855240488535\n",
      "EX_ile__L_e\n",
      "SA growth: 4.8955880439219674\n",
      "DP growth: 0.0\n",
      "EX_met__L_e\n",
      "SA growth: 5.025318032885619\n",
      "DP growth: 0.0235022422174751\n",
      "EX_so4_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_trp__L_e\n",
      "SA growth: 5.025633315723155\n",
      "DP growth: 0.01658042482516464\n",
      "EX_val__L_e\n",
      "SA growth: 5.025719036814095\n",
      "DP growth: 0.0146984773560348\n",
      "EX_thm_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_cys__L_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_phe__L_e\n",
      "SA growth: 4.870366995579907\n",
      "DP growth: 0.0\n",
      "EX_na1_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_ni2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_nac_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_26dap__M_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_mobd_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.41003733678287796\n",
      "EX_pyr_e\n",
      "SA growth: 4.980558865988173\n",
      "DP growth: 0.0\n",
      "EX_ser__L_e\n",
      "SA growth: 4.642529141647281\n",
      "DP growth: 0.0\n",
      "EX_urea_e\n",
      "SA growth: 5.022518081371391\n",
      "DP growth: 0.0\n"
     ]
    }
   ],
   "source": [
    "x1 =[]\n",
    "y1 =[]\n",
    "z1 =[]\n",
    "for met, f in model.medium.items():\n",
    "    with model:\n",
    "        medium = model.medium \n",
    "        medium[met] = 0\n",
    "        model.medium = medium\n",
    "        model.optimize()\n",
    "        print(met)\n",
    "        print(\"SA growth: \" + str(model.reactions.get_by_id(BIOMASS_SA + \"_\" + model_SA.id).flux))\n",
    "        print(\"DP growth: \" + str(model.reactions.get_by_id(BIOMASS_DP + \"_\" + model_DP.id).flux))\n",
    "        for j in range(len(Des)):\n",
    "            ide = \"EX_\" + str(Des.iloc[j][1]) + \"_e\"\n",
    "            if ide == met:\n",
    "                x1.append(str(Des.iloc[j][0]))\n",
    "                y1.append(round(model.reactions.get_by_id(BIOMASS_SA + \"_\" + model_SA.id).flux,3))\n",
    "                z1.append(round(model.reactions.get_by_id(BIOMASS_DP + \"_\" + model_DP.id).flux,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "There are no boundary reactions in this model. Therefore specific types of boundary reactions such as 'exchanges', 'demands' or 'sinks' cannot be identified.\n"
     ]
    }
   ],
   "source": [
    "model = create_community_model(models, [BIOMASS_DP, BIOMASS_SA], weights=[10.,1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth: 3.482229886466535\n",
      "DP growth: 0.2859373319534555\n"
     ]
    }
   ],
   "source": [
    "opt = model.optimize()\n",
    "print(\"SA growth: \" + str(model.reactions.get_by_id(BIOMASS_SA + \"_\" + model_SA.id).flux))\n",
    "print(\"DP growth: \" + str(model.reactions.get_by_id(BIOMASS_DP + \"_\" + model_DP.id).flux))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Objective</h3><p>10.0 Growth_DP_83VPs_KB5 + 1.0 BIOMASS_iYS_wild_type_iYS854 = 6.34160320600109</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metabolite</th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Flux</th>\n",
       "      <th>C-Number</th>\n",
       "      <th>C-Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26dap__M_e</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>0.02769</td>\n",
       "      <td>7</td>\n",
       "      <td>0.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4abz_e</td>\n",
       "      <td>EX_4abz_e</td>\n",
       "      <td>0.0001853</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ala__L_e</td>\n",
       "      <td>EX_ala__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arg__L_e</td>\n",
       "      <td>EX_arg__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>15.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ca2_e</td>\n",
       "      <td>EX_ca2_e</td>\n",
       "      <td>0.01486</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cl_e</td>\n",
       "      <td>EX_cl_e</td>\n",
       "      <td>0.03405</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cobalt2_e</td>\n",
       "      <td>EX_cobalt2_e</td>\n",
       "      <td>0.01011</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cu2_e</td>\n",
       "      <td>EX_cu2_e</td>\n",
       "      <td>0.009374</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cys__L_e</td>\n",
       "      <td>EX_cys__L_e</td>\n",
       "      <td>0.5508</td>\n",
       "      <td>3</td>\n",
       "      <td>0.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fe2_e</td>\n",
       "      <td>EX_fe2_e</td>\n",
       "      <td>0.07272</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fum_e</td>\n",
       "      <td>EX_fum_e</td>\n",
       "      <td>7.868</td>\n",
       "      <td>4</td>\n",
       "      <td>7.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glc__D_e</td>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>15.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glu__L_e</td>\n",
       "      <td>EX_glu__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gly_e</td>\n",
       "      <td>EX_gly_e</td>\n",
       "      <td>0.2855</td>\n",
       "      <td>2</td>\n",
       "      <td>0.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h2o_e</td>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>his__L_e</td>\n",
       "      <td>EX_his__L_e</td>\n",
       "      <td>0.236</td>\n",
       "      <td>6</td>\n",
       "      <td>0.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ile__L_e</td>\n",
       "      <td>EX_ile__L_e</td>\n",
       "      <td>1.207</td>\n",
       "      <td>6</td>\n",
       "      <td>1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>k_e</td>\n",
       "      <td>EX_k_e</td>\n",
       "      <td>0.6619</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>leu__L_e</td>\n",
       "      <td>EX_leu__L_e</td>\n",
       "      <td>1.218</td>\n",
       "      <td>6</td>\n",
       "      <td>1.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lys__L_e</td>\n",
       "      <td>EX_lys__L_e</td>\n",
       "      <td>1.35</td>\n",
       "      <td>6</td>\n",
       "      <td>2.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>met__L_e</td>\n",
       "      <td>EX_met__L_e</td>\n",
       "      <td>0.3246</td>\n",
       "      <td>5</td>\n",
       "      <td>0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mg2_e</td>\n",
       "      <td>EX_mg2_e</td>\n",
       "      <td>0.05325</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mn2_e</td>\n",
       "      <td>EX_mn2_e</td>\n",
       "      <td>0.01313</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mobd_e</td>\n",
       "      <td>EX_mobd_e</td>\n",
       "      <td>2.438E-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>na1_e</td>\n",
       "      <td>EX_na1_e</td>\n",
       "      <td>6.954</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nac_e</td>\n",
       "      <td>EX_nac_e</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>6</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ni2_e</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>0.01015</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>o2_e</td>\n",
       "      <td>EX_o2_e</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>orn_e</td>\n",
       "      <td>EX_orn_e</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>phe__L_e</td>\n",
       "      <td>EX_phe__L_e</td>\n",
       "      <td>0.7531</td>\n",
       "      <td>9</td>\n",
       "      <td>1.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pi_e</td>\n",
       "      <td>EX_pi_e</td>\n",
       "      <td>7.756</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pro__L_e</td>\n",
       "      <td>EX_pro__L_e</td>\n",
       "      <td>0.06121</td>\n",
       "      <td>5</td>\n",
       "      <td>0.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pyr_e</td>\n",
       "      <td>EX_pyr_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ribflv_e</td>\n",
       "      <td>EX_ribflv_e</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>17</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ser__L_e</td>\n",
       "      <td>EX_ser__L_e</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>so4_e</td>\n",
       "      <td>EX_so4_e</td>\n",
       "      <td>0.02057</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>thm_e</td>\n",
       "      <td>EX_thm_e</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>thr__L_e</td>\n",
       "      <td>EX_thr__L_e</td>\n",
       "      <td>0.07025</td>\n",
       "      <td>4</td>\n",
       "      <td>0.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trp__L_e</td>\n",
       "      <td>EX_trp__L_e</td>\n",
       "      <td>0.06916</td>\n",
       "      <td>11</td>\n",
       "      <td>0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>urea_e</td>\n",
       "      <td>EX_urea_e</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>val__L_e</td>\n",
       "      <td>EX_val__L_e</td>\n",
       "      <td>1.399</td>\n",
       "      <td>5</td>\n",
       "      <td>1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>zn2_e</td>\n",
       "      <td>EX_zn2_e</td>\n",
       "      <td>0.01024</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2fe1s_c_iYS854</td>\n",
       "      <td>SK_2fe1s_c_iYS854</td>\n",
       "      <td>0.0009506</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>apoACP_c_iYS854</td>\n",
       "      <td>SK_apoACP_c_iYS854</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mql8_c_DP_83VPs_KB5</td>\n",
       "      <td>Sink_mql8_DP_83VPs_KB5</td>\n",
       "      <td>2.769E-05</td>\n",
       "      <td>51</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metabolite</th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Flux</th>\n",
       "      <th>C-Number</th>\n",
       "      <th>C-Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5drib_c_iYS854</td>\n",
       "      <td>DM_5drib_c_iYS854</td>\n",
       "      <td>-0.003134</td>\n",
       "      <td>5</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>amob_c_iYS854</td>\n",
       "      <td>DM_amob_c_iYS854</td>\n",
       "      <td>-6.964E-06</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>co2_e</td>\n",
       "      <td>EX_co2_e</td>\n",
       "      <td>-84.66</td>\n",
       "      <td>1</td>\n",
       "      <td>32.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>etoh_e</td>\n",
       "      <td>EX_etoh_e</td>\n",
       "      <td>-40.91</td>\n",
       "      <td>2</td>\n",
       "      <td>31.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>for_e</td>\n",
       "      <td>EX_for_e</td>\n",
       "      <td>-36.87</td>\n",
       "      <td>1</td>\n",
       "      <td>14.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glyclt_e</td>\n",
       "      <td>EX_glyclt_e</td>\n",
       "      <td>-0.001738</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h2s_e</td>\n",
       "      <td>EX_h2s_e</td>\n",
       "      <td>-0.1781</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nh4_e</td>\n",
       "      <td>EX_nh4_e</td>\n",
       "      <td>-57.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ptrc_e</td>\n",
       "      <td>EX_ptrc_e</td>\n",
       "      <td>-14.43</td>\n",
       "      <td>4</td>\n",
       "      <td>22.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ahcys_c_iYS854</td>\n",
       "      <td>SK_ahcys_c_iYS854</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>14</td>\n",
       "      <td>0.09%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<cobra.summary.model_summary.ModelSummary at 0x7f9497164490>"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EX_glc__D_e\n",
      "SA growth: 3.141874771171121\n",
      "DP growth: 0.0\n",
      "EX_h2o_e\n",
      "SA growth: 3.3238890139531394\n",
      "DP growth: 0.28593733195345966\n",
      "EX_leu__L_e\n",
      "SA growth: 3.4370704937175987\n",
      "DP growth: 0.28593733195345916\n",
      "EX_4abz_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_ala__L_e\n",
      "SA growth: 3.482229886466538\n",
      "DP growth: 0.28593733195345744\n",
      "EX_cl_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pi_e\n",
      "SA growth: 3.360272758197507\n",
      "DP growth: 0.28593733195345816\n",
      "EX_ribflv_e\n",
      "SA growth: 3.4811003335280084\n",
      "DP growth: 0.28593733195345966\n",
      "EX_gly_e\n",
      "SA growth: 3.486375784081645\n",
      "DP growth: 0.2819130199085781\n",
      "EX_thr__L_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_arg__L_e\n",
      "SA growth: 3.382449276495586\n",
      "DP growth: 0.2859373319534591\n",
      "EX_lys__L_e\n",
      "SA growth: 3.307856330722536\n",
      "DP growth: 0.2859373319534582\n",
      "EX_orn_e\n",
      "SA growth: 3.351995190806577\n",
      "DP growth: 0.2859373319534597\n",
      "EX_k_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pro__L_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_ca2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_mg2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_mn2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_cobalt2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_zn2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_succ_e\n",
      "SA growth: 3.482229886466532\n",
      "DP growth: 0.28593733195345905\n",
      "EX_co2_e\n",
      "SA growth: 3.482229886466532\n",
      "DP growth: 0.28593733195345905\n",
      "EX_cu2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_o2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_glu__L_e\n",
      "SA growth: 2.9620989099883577\n",
      "DP growth: 0.28593733195346926\n",
      "EX_fe2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_fum_e\n",
      "SA growth: 3.482229886466536\n",
      "DP growth: 0.2859373319534493\n",
      "EX_his__L_e\n",
      "SA growth: 3.42294252967248\n",
      "DP growth: 0.28593733195344884\n",
      "EX_ile__L_e\n",
      "SA growth: 3.3111855218780124\n",
      "DP growth: 0.2859373319534545\n",
      "EX_met__L_e\n",
      "SA growth: 3.477809016355047\n",
      "DP growth: 0.28593733195344884\n",
      "EX_so4_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_trp__L_e\n",
      "SA growth: 3.4479074989033505\n",
      "DP growth: 0.2859373319534586\n",
      "EX_val__L_e\n",
      "SA growth: 3.4485739536667572\n",
      "DP growth: 0.285937331953459\n",
      "EX_thm_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_cys__L_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_phe__L_e\n",
      "SA growth: 3.2560867950099546\n",
      "DP growth: 0.28593733195346016\n",
      "EX_na1_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_ni2_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_nac_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_26dap__M_e\n",
      "SA growth: 5.026388539834129\n",
      "DP growth: 0.0\n",
      "EX_mobd_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.4100373367828718\n",
      "EX_pyr_e\n",
      "SA growth: 3.3066673990498217\n",
      "DP growth: 0.28593733195344717\n",
      "EX_ser__L_e\n",
      "SA growth: 3.094586229259937\n",
      "DP growth: 0.28593733195344717\n",
      "EX_urea_e\n",
      "SA growth: 3.3780421299386\n",
      "DP growth: 0.28593733195339494\n"
     ]
    }
   ],
   "source": [
    "x2 =[]\n",
    "y2 =[]\n",
    "z2 =[]\n",
    "for met, f in model.medium.items():\n",
    "    with model:\n",
    "        medium = model.medium \n",
    "        medium[met] = 0\n",
    "        model.medium = medium\n",
    "        model.optimize()\n",
    "        print(met)\n",
    "        print(\"SA growth: \" + str(model.reactions.get_by_id(BIOMASS_SA + \"_\" + model_SA.id).flux))\n",
    "        print(\"DP growth: \" + str(model.reactions.get_by_id(BIOMASS_DP + \"_\" + model_DP.id).flux))\n",
    "        for j in range(len(Des)):\n",
    "            ide = \"EX_\" + str(Des.iloc[j][1]) + \"_e\"\n",
    "            if ide == met:\n",
    "                x2.append(str(Des.iloc[j][0]))\n",
    "                y2.append(round(model.reactions.get_by_id(BIOMASS_SA + \"_\" + model_SA.id).flux,3))\n",
    "                z2.append(round(model.reactions.get_by_id(BIOMASS_DP + \"_\" + model_DP.id).flux,3))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw plot\n",
    "title_font = {'fontname':'Arial', 'size':'12', 'color':'black', 'weight':'normal'} \n",
    "fig = plt.figure(figsize=(10,5), dpi= 160)\n",
    "\n",
    "plt.scatter(x = x1, y = y1, color=\"darkgoldenrod\",s=5, marker=\"^\")\n",
    "plt.scatter(x = x1, y = z1, color=\"green\",s=3, marker=\"o\")\n",
    "plt.scatter(x = x2, y = y2, color=\"darkgoldenrod\",s=40, marker=\"^\")\n",
    "plt.scatter(x = x2, y = z2, color=\"green\",s=20, marker=\"o\")\n",
    "plt.ylim(-0.2,6)     \n",
    "plt.legend([\"$\\it{S.aureus}$ with weight 1:1\" , \"$\\it{D. pigrum}$ with weight 1:1\", \"$\\it{S.aureus}$ with weight 1:10\" , \"$\\it{D. pigrum}$ with weight 1:10\"], loc=8, bbox_to_anchor=(0.5, 0.13))\n",
    "\n",
    "# Decorations    \n",
    "plt.title('The effect of removing each single metabolite from the integrated community on the growth with defined media SNM', **title_font)\n",
    "\n",
    "plt.xticks(x1,rotation=90)\n",
    "plt.grid(linestyle='--', alpha=0.5)\n",
    "plt.ylabel(\"Growth of $\\it{S.aureus}$ and $\\it{D. pigrum}$ in the community\")\n",
    "#fig.tight_layout()\n",
    "\n",
    "plt.savefig(\"Integrated_community_remove_metabolite_growth_effect_media_SNM.pdf\", bbox_inches='tight', dpi=1000) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 586,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.medium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EX_glc__D_e': 10.0,\n",
       " 'EX_h2o_e': 10.0,\n",
       " 'EX_leu__L_e': 10.0,\n",
       " 'EX_4abz_e': 10.0,\n",
       " 'EX_ala__L_e': 10.0,\n",
       " 'EX_cl_e': 10.0,\n",
       " 'EX_pi_e': 10.0,\n",
       " 'EX_ribflv_e': 10.0,\n",
       " 'EX_gly_e': 10.0,\n",
       " 'EX_thr__L_e': 10.0,\n",
       " 'EX_arg__L_e': 10.0,\n",
       " 'EX_lys__L_e': 10.0,\n",
       " 'EX_orn_e': 10.0,\n",
       " 'EX_k_e': 10.0,\n",
       " 'EX_pro__L_e': 10.0,\n",
       " 'EX_ca2_e': 10.0,\n",
       " 'EX_mg2_e': 10.0,\n",
       " 'EX_mn2_e': 10.0,\n",
       " 'EX_cobalt2_e': 10.0,\n",
       " 'EX_zn2_e': 10.0,\n",
       " 'EX_succ_e': 10.0,\n",
       " 'EX_co2_e': 10.0,\n",
       " 'EX_cu2_e': 10.0,\n",
       " 'EX_o2_e': 20.0,\n",
       " 'EX_glu__L_e': 10.0,\n",
       " 'EX_fe2_e': 0.1,\n",
       " 'EX_fum_e': 10.0,\n",
       " 'EX_his__L_e': 10.0,\n",
       " 'EX_ile__L_e': 10.0,\n",
       " 'EX_met__L_e': 10.0,\n",
       " 'EX_so4_e': 10.0,\n",
       " 'EX_trp__L_e': 10.0,\n",
       " 'EX_val__L_e': 10.0,\n",
       " 'EX_thm_e': 10.0,\n",
       " 'EX_cys__L_e': 10.0,\n",
       " 'EX_phe__L_e': 10.0,\n",
       " 'EX_na1_e': 10.0,\n",
       " 'EX_ni2_e': 10.0,\n",
       " 'EX_nac_e': 10.0,\n",
       " 'EX_26dap__M_e': 10.0,\n",
       " 'EX_mobd_e': 10.0,\n",
       " 'EX_pyr_e': 10.0,\n",
       " 'EX_ser__L_e': 10.0,\n",
       " 'EX_urea_e': 10.0}"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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