community_model_Integrated_COMPM_13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 71,
   "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": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "Des = pd.read_csv(\"Descriptive_name.csv\", sep =\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snm3 = pd.read_csv(\"SNM3.csv\", sep =\";\")\n",
    "snm3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 75,
   "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": 76,
   "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 COMPM\n",
    "\n",
    "The medium should induce competition!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "compm_SA = json.loads(open(\"compm_SA.json\").read())\n",
    "compm_DP = json.loads(open(\"compm_DP.json\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "compm = dict()\n",
    "for key, val in compm_SA.items():\n",
    "    compm[key] = val \n",
    "for key, val in compm_DP.items():\n",
    "    compm[key] = val "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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.], medium=compm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.medium = compm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth: 2.5586946126136905\n",
      "DP growth: -1.0224043833773067e-12\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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Objective</h3><p>1.0 Growth_DP_83VPs_KB5 + 1.0 BIOMASS_iYS_wild_type_iYS854 = 2.558694612612668</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>arg__L_e</td>\n",
       "      <td>EX_arg__L_e</td>\n",
       "      <td>3.96</td>\n",
       "      <td>6</td>\n",
       "      <td>19.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cl_e</td>\n",
       "      <td>EX_cl_e</td>\n",
       "      <td>0.01267</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>0.4193</td>\n",
       "      <td>3</td>\n",
       "      <td>1.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fe2_e</td>\n",
       "      <td>EX_fe2_e</td>\n",
       "      <td>0.03776</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glc__D_e</td>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>7.913</td>\n",
       "      <td>6</td>\n",
       "      <td>39.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glu__L_e</td>\n",
       "      <td>EX_glu__L_e</td>\n",
       "      <td>3.548</td>\n",
       "      <td>5</td>\n",
       "      <td>14.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>his__L_e</td>\n",
       "      <td>EX_his__L_e</td>\n",
       "      <td>0.1542</td>\n",
       "      <td>6</td>\n",
       "      <td>0.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ile__L_e</td>\n",
       "      <td>EX_ile__L_e</td>\n",
       "      <td>0.07944</td>\n",
       "      <td>6</td>\n",
       "      <td>0.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>k_e</td>\n",
       "      <td>EX_k_e</td>\n",
       "      <td>0.4751</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>0.8035</td>\n",
       "      <td>6</td>\n",
       "      <td>4.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lys__L_e</td>\n",
       "      <td>EX_lys__L_e</td>\n",
       "      <td>0.9223</td>\n",
       "      <td>6</td>\n",
       "      <td>4.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>met__L_e</td>\n",
       "      <td>EX_met__L_e</td>\n",
       "      <td>0.04209</td>\n",
       "      <td>5</td>\n",
       "      <td>0.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mg2_e</td>\n",
       "      <td>EX_mg2_e</td>\n",
       "      <td>0.02112</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>0.001684</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>1.791E-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>5.091</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>0.0002162</td>\n",
       "      <td>6</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>phe__L_e</td>\n",
       "      <td>EX_phe__L_e</td>\n",
       "      <td>0.4876</td>\n",
       "      <td>9</td>\n",
       "      <td>3.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ribflv_e</td>\n",
       "      <td>EX_ribflv_e</td>\n",
       "      <td>0.0007638</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>2.521</td>\n",
       "      <td>3</td>\n",
       "      <td>6.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>so4_e</td>\n",
       "      <td>EX_so4_e</td>\n",
       "      <td>0.01056</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>0.001141</td>\n",
       "      <td>12</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>0.03925</td>\n",
       "      <td>11</td>\n",
       "      <td>0.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>val__L_e</td>\n",
       "      <td>EX_val__L_e</td>\n",
       "      <td>0.9404</td>\n",
       "      <td>5</td>\n",
       "      <td>3.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>zn2_e</td>\n",
       "      <td>EX_zn2_e</td>\n",
       "      <td>0.000829</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>0.0006985</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>0.1969</td>\n",
       "      <td>0</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>-0.002303</td>\n",
       "      <td>5</td>\n",
       "      <td>0.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>amob_c_iYS854</td>\n",
       "      <td>DM_amob_c_iYS854</td>\n",
       "      <td>-5.117E-06</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ac_e</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>2</td>\n",
       "      <td>13.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>co2_e</td>\n",
       "      <td>EX_co2_e</td>\n",
       "      <td>-20.02</td>\n",
       "      <td>1</td>\n",
       "      <td>66.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>etoh_e</td>\n",
       "      <td>EX_etoh_e</td>\n",
       "      <td>-1.595</td>\n",
       "      <td>2</td>\n",
       "      <td>10.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glyclt_e</td>\n",
       "      <td>EX_glyclt_e</td>\n",
       "      <td>-0.001141</td>\n",
       "      <td>2</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h2o_e</td>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>-42.22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ser__D_e</td>\n",
       "      <td>EX_ser__D_e</td>\n",
       "      <td>-0.02169</td>\n",
       "      <td>3</td>\n",
       "      <td>0.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>succ_e</td>\n",
       "      <td>EX_succ_e</td>\n",
       "      <td>-0.02169</td>\n",
       "      <td>4</td>\n",
       "      <td>0.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>urea_e</td>\n",
       "      <td>EX_urea_e</td>\n",
       "      <td>-2.471</td>\n",
       "      <td>1</td>\n",
       "      <td>8.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ahcys_c_iYS854</td>\n",
       "      <td>SK_ahcys_c_iYS854</td>\n",
       "      <td>-0.01264</td>\n",
       "      <td>14</td>\n",
       "      <td>0.59%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<cobra.summary.model_summary.ModelSummary at 0x7fdf8ae26700>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EX_glc__D_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_h2o_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_leu__L_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_4abz_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_ala__L_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_cl_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pi_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_ribflv_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_gly_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_thr__L_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_arg__L_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_lys__L_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_orn_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_k_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pro__L_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_ca2_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\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: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_zn2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_co2_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_cu2_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_o2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_glu__L_e\n",
      "SA growth: 2.558694612613442\n",
      "DP growth: -9.733325256888747e-13\n",
      "EX_fe2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_fum_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_his__L_e\n",
      "SA growth: 2.558694612613442\n",
      "DP growth: -9.733325256888747e-13\n",
      "EX_ile__L_e\n",
      "SA growth: 2.558694612613442\n",
      "DP growth: -9.733325256888747e-13\n",
      "EX_met__L_e\n",
      "SA growth: 2.558694612613442\n",
      "DP growth: -9.733325256888747e-13\n",
      "EX_so4_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_trp__L_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_val__L_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\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: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\n",
      "EX_na1_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_ni2_e\n",
      "SA growth: 2.558694612613397\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: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_mobd_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.2823653925333652\n",
      "EX_pyr_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_ser__L_e\n",
      "SA growth: 2.5586946126085213\n",
      "DP growth: 0.0\n",
      "EX_urea_e\n",
      "SA growth: 2.558694612613441\n",
      "DP growth: -9.729994587814872e-13\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 met == ide:\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": 84,
   "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.], medium=compm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Objective</h3><p>10.0 Growth_DP_83VPs_KB5 + 1.0 BIOMASS_iYS_wild_type_iYS854 = 2.9045218796936076</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>0.00706</td>\n",
       "      <td>7</td>\n",
       "      <td>0.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4abz_e</td>\n",
       "      <td>EX_4abz_e</td>\n",
       "      <td>4.723E-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>0.1381</td>\n",
       "      <td>3</td>\n",
       "      <td>0.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arg__L_e</td>\n",
       "      <td>EX_arg__L_e</td>\n",
       "      <td>3.056</td>\n",
       "      <td>6</td>\n",
       "      <td>15.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ca2_e</td>\n",
       "      <td>EX_ca2_e</td>\n",
       "      <td>0.00379</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>0.01506</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>0.002577</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>0.00239</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>0.3679</td>\n",
       "      <td>3</td>\n",
       "      <td>0.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fe2_e</td>\n",
       "      <td>EX_fe2_e</td>\n",
       "      <td>0.03755</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glc__D_e</td>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>9.345</td>\n",
       "      <td>6</td>\n",
       "      <td>47.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glu__L_e</td>\n",
       "      <td>EX_glu__L_e</td>\n",
       "      <td>0.9169</td>\n",
       "      <td>5</td>\n",
       "      <td>3.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gly_e</td>\n",
       "      <td>EX_gly_e</td>\n",
       "      <td>0.07279</td>\n",
       "      <td>2</td>\n",
       "      <td>0.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>his__L_e</td>\n",
       "      <td>EX_his__L_e</td>\n",
       "      <td>0.1378</td>\n",
       "      <td>6</td>\n",
       "      <td>0.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ile__L_e</td>\n",
       "      <td>EX_ile__L_e</td>\n",
       "      <td>0.07944</td>\n",
       "      <td>6</td>\n",
       "      <td>0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>k_e</td>\n",
       "      <td>EX_k_e</td>\n",
       "      <td>0.4079</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>0.7149</td>\n",
       "      <td>6</td>\n",
       "      <td>3.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lys__L_e</td>\n",
       "      <td>EX_lys__L_e</td>\n",
       "      <td>0.8084</td>\n",
       "      <td>6</td>\n",
       "      <td>4.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>met__L_e</td>\n",
       "      <td>EX_met__L_e</td>\n",
       "      <td>0.04209</td>\n",
       "      <td>5</td>\n",
       "      <td>0.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mg2_e</td>\n",
       "      <td>EX_mg2_e</td>\n",
       "      <td>0.0242</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>0.004196</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>1.523E-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>4.335</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>0.001052</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>0.002588</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>19.9</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>2.086</td>\n",
       "      <td>5</td>\n",
       "      <td>8.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>phe__L_e</td>\n",
       "      <td>EX_phe__L_e</td>\n",
       "      <td>0.4374</td>\n",
       "      <td>9</td>\n",
       "      <td>3.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pi_e</td>\n",
       "      <td>EX_pi_e</td>\n",
       "      <td>0.06231</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>0.01561</td>\n",
       "      <td>5</td>\n",
       "      <td>0.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ribflv_e</td>\n",
       "      <td>EX_ribflv_e</td>\n",
       "      <td>0.0006809</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>2.148</td>\n",
       "      <td>3</td>\n",
       "      <td>5.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>so4_e</td>\n",
       "      <td>EX_so4_e</td>\n",
       "      <td>0.01056</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>0.000986</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>0.01791</td>\n",
       "      <td>4</td>\n",
       "      <td>0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trp__L_e</td>\n",
       "      <td>EX_trp__L_e</td>\n",
       "      <td>0.03739</td>\n",
       "      <td>11</td>\n",
       "      <td>0.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>val__L_e</td>\n",
       "      <td>EX_val__L_e</td>\n",
       "      <td>0.8298</td>\n",
       "      <td>5</td>\n",
       "      <td>3.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>zn2_e</td>\n",
       "      <td>EX_zn2_e</td>\n",
       "      <td>0.003027</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>0.0005939</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>0.1674</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>7.06E-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>-0.001958</td>\n",
       "      <td>5</td>\n",
       "      <td>0.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>amob_c_iYS854</td>\n",
       "      <td>DM_amob_c_iYS854</td>\n",
       "      <td>-4.351E-06</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ac_e</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.3224</td>\n",
       "      <td>2</td>\n",
       "      <td>1.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>co2_e</td>\n",
       "      <td>EX_co2_e</td>\n",
       "      <td>-17.06</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>etoh_e</td>\n",
       "      <td>EX_etoh_e</td>\n",
       "      <td>-1.352</td>\n",
       "      <td>2</td>\n",
       "      <td>7.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fum_e</td>\n",
       "      <td>EX_fum_e</td>\n",
       "      <td>-0.04624</td>\n",
       "      <td>4</td>\n",
       "      <td>0.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>glyclt_e</td>\n",
       "      <td>EX_glyclt_e</td>\n",
       "      <td>-0.001018</td>\n",
       "      <td>2</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h2o_e</td>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>-40.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pyr_e</td>\n",
       "      <td>EX_pyr_e</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>3</td>\n",
       "      <td>7.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ser__D_e</td>\n",
       "      <td>EX_ser__D_e</td>\n",
       "      <td>-4.708</td>\n",
       "      <td>3</td>\n",
       "      <td>37.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ahcys_c_iYS854</td>\n",
       "      <td>SK_ahcys_c_iYS854</td>\n",
       "      <td>-0.01074</td>\n",
       "      <td>14</td>\n",
       "      <td>0.40%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<cobra.summary.model_summary.ModelSummary at 0x7fdf63a23610>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EX_glc__D_e\n",
      "SA growth: 2.4495257486583815\n",
      "DP growth: 0.02076938270447598\n",
      "EX_h2o_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\n",
      "EX_leu__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\n",
      "EX_4abz_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_ala__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_cl_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pi_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_ribflv_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_gly_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_thr__L_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_arg__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_lys__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_orn_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_k_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_pro__L_e\n",
      "SA growth: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_ca2_e\n",
      "SA growth: 2.5586946126085213\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: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_zn2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_co2_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_cu2_e\n",
      "SA growth: 2.558694612613397\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: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\n",
      "EX_fe2_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_fum_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_his__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_ile__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_met__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_so4_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_trp__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\n",
      "EX_val__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852741\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: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\n",
      "EX_na1_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.0\n",
      "EX_ni2_e\n",
      "SA growth: 2.558694612613397\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: 2.558694612613397\n",
      "DP growth: 0.0\n",
      "EX_mobd_e\n",
      "SA growth: 0.0\n",
      "DP growth: 0.2823653925333652\n",
      "EX_pyr_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\n",
      "EX_ser__L_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\n",
      "EX_urea_e\n",
      "SA growth: 2.1755067515083337\n",
      "DP growth: 0.07290151281852744\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 met == ide:\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": 88,
   "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, 0.6))\n",
    "\n",
    "# Decorations    \n",
    "plt.title('The effect of removing each single metabolite from the integrated community on the growth with COMPM media', **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_compm.pdf\", bbox_inches='tight', dpi=1000) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EX_cl_e': 0.016592706807384002,\n",
       " 'EX_pyr_e': 10.0,\n",
       " 'EX_so4_e': 0.010557173971642877,\n",
       " 'EX_mg2_e': 0.024203301149647583,\n",
       " 'EX_co2_e': 0.21226941455991016,\n",
       " 'EX_ser__L_e': 10.0,\n",
       " 'EX_glc__D_e': 10.0,\n",
       " 'EX_arg__L_e': 10.0,\n",
       " 'EX_thm_e': 0.001141177797225575,\n",
       " 'EX_na1_e': 5.090522931794354,\n",
       " 'EX_phe__L_e': 10.0,\n",
       " 'EX_mn2_e': 0.010707719788924139,\n",
       " 'EX_mobd_e': 1.7910862288293614e-05,\n",
       " 'EX_trp__L_e': 0.03925018692530509,\n",
       " 'EX_o2_e': 20.0,\n",
       " 'EX_cys__L_e': 10.0,\n",
       " 'EX_lys__L_e': 0.9223436956285822,\n",
       " 'EX_leu__L_e': 0.8034579715954936,\n",
       " 'EX_nac_e': 7.953051307191044,\n",
       " 'EX_orn_e': 10.0,\n",
       " 'EX_pi_e': 5.519506278294781,\n",
       " 'EX_k_e': 0.47512400261618165,\n",
       " 'EX_his__L_e': 0.15415385138287618,\n",
       " 'EX_ribflv_e': 0.0007637848411345721,\n",
       " 'EX_urea_e': 10.0,\n",
       " 'EX_zn2_e': 0.008993582449543406,\n",
       " 'EX_fe2_e': 0.037761215092876554,\n",
       " 'EX_glu__L_e': 10.0,\n",
       " 'EX_val__L_e': 10.0,\n",
       " 'EX_h2o_e': 10.0,\n",
       " 'EX_thr__L_e': 0.06937020546125328,\n",
       " 'EX_fum_e': 10.0,\n",
       " 'EX_26dap__M_e': 0.02734480105817464,\n",
       " 'EX_ni2_e': 0.010022613010017957,\n",
       " 'EX_cobalt2_e': 0.009981830860061647,\n",
       " 'EX_cu2_e': 0.009257250408242637,\n",
       " 'EX_ca2_e': 0.014677908938624291,\n",
       " 'EX_pro__L_e': 0.06044704997960759,\n",
       " 'EX_gly_e': 10.0,\n",
       " 'EX_met__L_e': 0.04208610986094591,\n",
       " 'EX_ala__L_e': 10.0,\n",
       " 'EX_ile__L_e': 0.07944457706685171,\n",
       " 'EX_4abz_e': 0.00018293671908057858}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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