community_model_approaches_26.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:\n",
      "D:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll\n",
      "D:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
      "  stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import cobra\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.optimize import linprog\n",
    "import scipy\n",
    "import mip\n",
    "from copy import deepcopy\n",
    "\n",
    "def save_dict(data, name):\n",
    "    with open(name, 'w' ) as file:\n",
    "        json.dump( data, file )"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth:  0.28236539253226034\n",
      "Growth:  2.5586946126133867\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for rec in model_SA.reactions:\n",
    "    rec.lower_bound = max(rec.lower_bound, -1000)\n",
    "    rec.upper_bound = min(rec.upper_bound, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "  </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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snm3 = pd.read_csv(\"SNM3.csv\", sep =\";\")\n",
    "snm3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "BIOMASS_DP = \"Growth\" \n",
    "BIOMASS_SA = \"BIOMASS_iYS_wild_type\"\n",
    "models = [model_DP.copy(), model_SA.copy()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from community_models import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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())\n",
    "\n",
    "model_DP.medium = compm_DP \n",
    "model_SA.medium = compm_SA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = Model(model_DP, BIOMASS_DP)\n",
    "model2 = Model(model_SA, BIOMASS_SA)\n",
    "\n",
    "community_model1 = model1 + model2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "community_model2 = MIP_community_model(model1, model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ignoring reaction 'ACALD' since it already exists.\n",
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      "Ignoring reaction 'ACGS' since it already exists.\n",
      "Ignoring reaction 'ACKr' since it already exists.\n",
      "Ignoring reaction '3HAD100' since it already exists.\n",
      "Ignoring reaction '3HAD120' since it already exists.\n",
      "Ignoring reaction '3HAD140' since it already exists.\n",
      "Ignoring reaction 'ACACT1r' since it already exists.\n",
      "Ignoring reaction '3HAD60' since it already exists.\n",
      "Ignoring reaction 'ACM6PH' since it already exists.\n",
      "Ignoring reaction '3OAR100' since it already exists.\n",
      "Ignoring reaction '3OAR140' since it already exists.\n",
      "Ignoring reaction '3OAR60' since it already exists.\n",
      "Ignoring reaction '3OAS100' since it already exists.\n",
      "Ignoring reaction 'ACOATA' since it already exists.\n",
      "Ignoring reaction 'ACODA' since it already exists.\n",
      "Ignoring reaction 'AKGDH' since it already exists.\n",
      "Ignoring reaction 'ALAALAr' since it already exists.\n",
      "Ignoring reaction 'ALAD_L' since it already exists.\n",
      "Ignoring reaction 'ALAR' since it already exists.\n",
      "Ignoring reaction 'ALCD19' since it already exists.\n",
      "Ignoring reaction 'ALCD2ir' since it already exists.\n",
      "Ignoring reaction 'ARGDr' since it already exists.\n",
      "Ignoring reaction 'ADK1' since it already exists.\n",
      "Ignoring reaction 'AMANAPEr' since it already exists.\n",
      "Ignoring reaction 'ADK3' since it already exists.\n",
      "Ignoring reaction 'ADK4' since it already exists.\n",
      "Ignoring reaction 'ADKd' since it already exists.\n",
      "Ignoring reaction 'ADNUC' since it already exists.\n",
      "Ignoring reaction 'ADPT' since it already exists.\n",
      "Ignoring reaction 'ATPM' since it already exists.\n",
      "Ignoring reaction 'ADSL1r' since it already exists.\n",
      "Ignoring reaction 'ADSL2r' since it already exists.\n",
      "Ignoring reaction 'ADSS' since it already exists.\n",
      "Ignoring reaction 'AGDC' since it already exists.\n",
      "Ignoring reaction 'CYTD' since it already exists.\n",
      "Ignoring reaction 'DHPS2' since it already exists.\n",
      "Ignoring reaction 'CYTK1' since it already exists.\n",
      "Ignoring reaction 'CYTK2' since it already exists.\n",
      "Ignoring reaction 'Cut1' since it already exists.\n",
      "Ignoring reaction 'DADK' since it already exists.\n",
      "Ignoring reaction 'DATPHs' since it already exists.\n",
      "Ignoring reaction 'CO2t' since it already exists.\n",
      "Ignoring reaction 'COabc' since it already exists.\n",
      "Ignoring reaction 'DCYTD' since it already exists.\n",
      "Ignoring reaction 'DMATT' since it already exists.\n",
      "Ignoring reaction 'CTPS1' since it already exists.\n",
      "Ignoring reaction 'CTPS2' since it already exists.\n",
      "Ignoring reaction 'DGK1' since it already exists.\n",
      "Ignoring reaction 'DHAPT' since it already exists.\n",
      "Ignoring reaction 'DHORTS' since it already exists.\n",
      "Ignoring reaction 'DHFR' since it already exists.\n",
      "Ignoring reaction 'DHFS' since it already exists.\n",
      "Ignoring reaction 'DHNPA2r' since it already exists.\n",
      "Ignoring reaction 'DNMPPA' since it already exists.\n",
      "Ignoring reaction 'DHORDi' since it already exists.\n",
      "Ignoring reaction 'DNTPPA' since it already exists.\n",
      "Ignoring reaction 'EX_acald_e' since it already exists.\n",
      "Ignoring reaction 'DPMVD' since it already exists.\n",
      "Ignoring reaction 'DPR' since it already exists.\n",
      "Ignoring reaction 'EX_h2o_e' since it already exists.\n",
      "Ignoring reaction 'EX_arg__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_h_e' since it already exists.\n",
      "Ignoring reaction 'EX_his__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_ile__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_k_e' since it already exists.\n",
      "Ignoring reaction 'EX_lac__D_e' since it already exists.\n",
      "Ignoring reaction 'EX_leu__L_e' since it already exists.\n",
      "Ignoring reaction 'DRPA' since it already exists.\n",
      "Ignoring reaction 'EX_lys__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_cl_e' since it already exists.\n",
      "Ignoring reaction 'EX_co2_e' since it already exists.\n",
      "Ignoring reaction 'EX_cys__L_e' since it already exists.\n",
      "Ignoring reaction 'DTMPK' since it already exists.\n",
      "Ignoring reaction 'EX_met__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_mg2_e' since it already exists.\n",
      "Ignoring reaction 'EX_mn2_e' since it already exists.\n",
      "Ignoring reaction 'EX_na1_e' since it already exists.\n",
      "Ignoring reaction 'EX_fe2_e' since it already exists.\n",
      "Ignoring reaction 'EX_nac_e' since it already exists.\n",
      "Ignoring reaction 'EX_nh4_e' since it already exists.\n",
      "Ignoring reaction 'EX_o2_e' since it already exists.\n",
      "Ignoring reaction 'EX_orn_e' since it already exists.\n",
      "Ignoring reaction 'EX_phe__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_pi_e' since it already exists.\n",
      "Ignoring reaction 'D_LACt2' since it already exists.\n",
      "Ignoring reaction 'EX_glc__D_e' since it already exists.\n",
      "Ignoring reaction 'EX_rib__D_e' since it already exists.\n",
      "Ignoring reaction 'EX_glu__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_ribflv_e' since it already exists.\n",
      "Ignoring reaction 'EX_so4_e' since it already exists.\n",
      "Ignoring reaction 'EX_trp__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_tyr__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_thm_e' since it already exists.\n",
      "Ignoring reaction 'EX_ura_e' since it already exists.\n",
      "Ignoring reaction 'EX_val__L_e' since it already exists.\n",
      "Ignoring reaction 'EX_zn2_e' since it already exists.\n",
      "Ignoring reaction 'ENO' since it already exists.\n",
      "Ignoring reaction 'G3PD2' since it already exists.\n",
      "Ignoring reaction 'FBA' since it already exists.\n",
      "Ignoring reaction 'FBA2' since it already exists.\n",
      "Ignoring reaction 'FBA3' since it already exists.\n",
      "Ignoring reaction 'G6PBDH' since it already exists.\n",
      "Ignoring reaction 'G6PDA' since it already exists.\n",
      "Ignoring reaction 'FE2abc' since it already exists.\n",
      "Ignoring reaction 'G6PI' since it already exists.\n",
      "Ignoring reaction 'G6PP' since it already exists.\n",
      "Ignoring reaction 'GCALDD' since it already exists.\n",
      "Ignoring reaction 'GF6PTA' since it already exists.\n",
      "Ignoring reaction 'GGTT' since it already exists.\n",
      "Ignoring reaction 'GLUR' since it already exists.\n",
      "Ignoring reaction 'FRTT' since it already exists.\n",
      "Ignoring reaction 'FRUK' since it already exists.\n",
      "Ignoring reaction 'GHMT2r' since it already exists.\n",
      "Ignoring reaction 'GK1' since it already exists.\n",
      "Ignoring reaction 'FTHFLi' since it already exists.\n",
      "Ignoring reaction 'FUM' since it already exists.\n",
      "Ignoring reaction 'G1PACT' since it already exists.\n",
      "Ignoring reaction 'G1SAT' since it already exists.\n",
      "Ignoring reaction 'GLUTRR' since it already exists.\n",
      "Ignoring reaction 'GLCpts' since it already exists.\n",
      "Ignoring reaction 'GLNS' since it already exists.\n",
      "Ignoring reaction 'GLUabc' since it already exists.\n",
      "Ignoring reaction 'GLUt4' since it already exists.\n",
      "Ignoring reaction 'GLUPRT' since it already exists.\n",
      "Ignoring reaction 'GLYAT' since it already exists.\n",
      "Ignoring reaction 'ILETA' since it already exists.\n",
      "Ignoring reaction 'GUAPRT' since it already exists.\n",
      "Ignoring reaction 'H2Ot' since it already exists.\n",
      "Ignoring reaction 'HCO3E' since it already exists.\n",
      "Ignoring reaction 'ILEt2r' since it already exists.\n",
      "Ignoring reaction 'IMPC' since it already exists.\n",
      "Ignoring reaction 'IMPD' since it already exists.\n",
      "Ignoring reaction 'INSH' since it already exists.\n",
      "Ignoring reaction 'IPDDI' since it already exists.\n",
      "Ignoring reaction 'HEXTT' since it already exists.\n",
      "Ignoring reaction 'HISabc' since it already exists.\n",
      "Ignoring reaction 'KAS14' since it already exists.\n",
      "Ignoring reaction 'KAS15' since it already exists.\n",
      "Ignoring reaction 'KAS16' since it already exists.\n",
      "Ignoring reaction 'HMBS' since it already exists.\n",
      "Ignoring reaction 'HPYRRx' since it already exists.\n",
      "Ignoring reaction 'Kt2r' since it already exists.\n",
      "Ignoring reaction 'LDH_L' since it already exists.\n",
      "Ignoring reaction 'HXPRT' since it already exists.\n",
      "Ignoring reaction 'LEUt2r' since it already exists.\n",
      "Ignoring reaction 'LGTHL' since it already exists.\n",
      "Ignoring reaction 'GLYOX' since it already exists.\n",
      "Ignoring reaction 'MGt5' since it already exists.\n",
      "Ignoring reaction 'MACPD' since it already exists.\n",
      "Ignoring reaction 'MMSAD3' since it already exists.\n",
      "Ignoring reaction 'MNabc' since it already exists.\n",
      "Ignoring reaction 'MOHMT' since it already exists.\n",
      "Ignoring reaction 'MTHFC' since it already exists.\n",
      "Ignoring reaction 'MTHFD' since it already exists.\n",
      "Ignoring reaction 'MCOATA' since it already exists.\n",
      "Ignoring reaction 'ME1' since it already exists.\n",
      "Ignoring reaction 'NACt' since it already exists.\n",
      "Ignoring reaction 'ME2' since it already exists.\n",
      "Ignoring reaction 'METAT' since it already exists.\n",
      "Ignoring reaction 'METS' since it already exists.\n",
      "Ignoring reaction 'NAt3_1' since it already exists.\n",
      "Ignoring reaction 'METabc' since it already exists.\n",
      "Ignoring reaction 'NDPK2' since it already exists.\n",
      "Ignoring reaction 'MEVK2' since it already exists.\n",
      "Ignoring reaction 'MEVK3' since it already exists.\n",
      "Ignoring reaction 'MEVK4' since it already exists.\n",
      "Ignoring reaction 'NDPK4' since it already exists.\n",
      "Ignoring reaction 'NNAM' since it already exists.\n",
      "Ignoring reaction 'NNATr' since it already exists.\n",
      "Ignoring reaction 'GMPR' since it already exists.\n",
      "Ignoring reaction 'GMPS2' since it already exists.\n",
      "Ignoring reaction 'PC' since it already exists.\n",
      "Ignoring reaction 'GND' since it already exists.\n",
      "Ignoring reaction 'NTD10' since it already exists.\n",
      "Ignoring reaction 'PDH' since it already exists.\n",
      "Ignoring reaction 'NTD2' since it already exists.\n",
      "Ignoring reaction 'NTD3' since it already exists.\n",
      "Ignoring reaction 'PDHbr' since it already exists.\n",
      "Ignoring reaction 'PDHcr' since it already exists.\n",
      "Ignoring reaction 'NTD4' since it already exists.\n",
      "Ignoring reaction 'NTD5' since it already exists.\n",
      "Ignoring reaction 'NTD6' since it already exists.\n",
      "Ignoring reaction 'NTD7' since it already exists.\n",
      "Ignoring reaction 'NTD8' since it already exists.\n",
      "Ignoring reaction 'NTD9' since it already exists.\n",
      "Ignoring reaction 'PFK' since it already exists.\n",
      "Ignoring reaction 'PFK_4' since it already exists.\n",
      "Ignoring reaction 'PFL' since it already exists.\n",
      "Ignoring reaction 'PGAMT' since it already exists.\n",
      "Ignoring reaction 'PGI' since it already exists.\n",
      "Ignoring reaction 'PGK' since it already exists.\n",
      "Ignoring reaction 'PGL' since it already exists.\n",
      "Ignoring reaction 'PGM' since it already exists.\n",
      "Ignoring reaction 'PGMT' since it already exists.\n",
      "Ignoring reaction 'O2t' since it already exists.\n",
      "Ignoring reaction 'PHETA1' since it already exists.\n",
      "Ignoring reaction 'GRTT' since it already exists.\n",
      "Ignoring reaction 'OIVD1r' since it already exists.\n",
      "Ignoring reaction 'OIVD2' since it already exists.\n",
      "Ignoring reaction 'OIVD3' since it already exists.\n",
      "Ignoring reaction 'OMPDC' since it already exists.\n",
      "Ignoring reaction 'ORNTAC' since it already exists.\n",
      "Ignoring reaction 'ORPT' since it already exists.\n",
      "Ignoring reaction 'GTHOr' since it already exists.\n",
      "Ignoring reaction 'PMEVK' since it already exists.\n",
      "Ignoring reaction 'PMPK' since it already exists.\n",
      "Ignoring reaction 'GTHPi' since it already exists.\n",
      "Ignoring reaction 'PYK' since it already exists.\n",
      "Ignoring reaction 'SDPDS' since it already exists.\n",
      "Ignoring reaction 'RBK' since it already exists.\n",
      "Ignoring reaction 'RIBFLVt2' since it already exists.\n",
      "Ignoring reaction 'PPA' since it already exists.\n",
      "Ignoring reaction 'PPBNGS' since it already exists.\n",
      "Ignoring reaction 'PPCDC' since it already exists.\n",
      "Ignoring reaction 'PPM' since it already exists.\n",
      "Ignoring reaction 'PPM2' since it already exists.\n",
      "Ignoring reaction 'RIBabc1' since it already exists.\n",
      "Ignoring reaction 'RNDR1' since it already exists.\n",
      "Ignoring reaction 'SDPTA' since it already exists.\n",
      "Ignoring reaction 'RNDR2' since it already exists.\n",
      "Ignoring reaction 'RNDR3' since it already exists.\n",
      "Ignoring reaction 'RNDR4' since it already exists.\n",
      "Ignoring reaction 'RNTR1' since it already exists.\n",
      "Ignoring reaction 'RNTR2' since it already exists.\n",
      "Ignoring reaction 'RNTR3' since it already exists.\n",
      "Ignoring reaction 'RNTR4' since it already exists.\n",
      "Ignoring reaction 'PPTT' since it already exists.\n",
      "Ignoring reaction 'PRAGSr' since it already exists.\n",
      "Ignoring reaction 'PRAIS_1' since it already exists.\n",
      "Ignoring reaction 'PRASCSi' since it already exists.\n",
      "Ignoring reaction 'PRFGS' since it already exists.\n",
      "Ignoring reaction 'RPE' since it already exists.\n",
      "Ignoring reaction 'RPI' since it already exists.\n",
      "Ignoring reaction 'PRPPS' since it already exists.\n",
      "Ignoring reaction 'PTAr' since it already exists.\n",
      "Ignoring reaction 'PTPATi' since it already exists.\n",
      "Ignoring reaction 'THMabc' since it already exists.\n",
      "Ignoring reaction 'THRD_L' since it already exists.\n",
      "Ignoring reaction 'TKT1' since it already exists.\n",
      "Ignoring reaction 'TKT2' since it already exists.\n",
      "Ignoring reaction 'UMPK' since it already exists.\n",
      "Ignoring reaction 'UPP3S' since it already exists.\n",
      "Ignoring reaction 'UPPDC1' since it already exists.\n",
      "Ignoring reaction 'TMDS' since it already exists.\n",
      "Ignoring reaction 'TMPPP' since it already exists.\n",
      "Ignoring reaction 'TPI' since it already exists.\n",
      "Ignoring reaction 'TRDR' since it already exists.\n",
      "Ignoring reaction 'UPPRT' since it already exists.\n",
      "Ignoring reaction 'URAt2' since it already exists.\n",
      "Ignoring reaction 'URIDK2r' since it already exists.\n",
      "Ignoring reaction 'TRPt2r' since it already exists.\n",
      "Ignoring reaction 'VALTA' since it already exists.\n",
      "Ignoring reaction 'VALt2r' since it already exists.\n",
      "Ignoring reaction 'TYRt2r' since it already exists.\n",
      "Ignoring reaction 'UAMAGS' since it already exists.\n",
      "Ignoring reaction 'UAMAS' since it already exists.\n",
      "Ignoring reaction 'UAPGR' since it already exists.\n",
      "Ignoring reaction 'UDCPDP' since it already exists.\n",
      "Ignoring reaction 'XPPT' since it already exists.\n",
      "Ignoring reaction 'XTSNH' since it already exists.\n",
      "Ignoring reaction 'ZNabc' since it already exists.\n",
      "Ignoring reaction 'TALA' since it already exists.\n",
      "Ignoring reaction 'THDPS' since it already exists.\n",
      "Ignoring reaction 'EX_glyclt_e' since it already exists.\n",
      "Ignoring reaction 'THIORDXi' since it already exists.\n",
      "Ignoring reaction 'UAG2EMA' since it already exists.\n"
     ]
    }
   ],
   "source": [
    "community_model3_1_1 = create_bag_of_react_model([model_DP, model_SA],[BIOMASS_DP, BIOMASS_SA], [1,1])\n",
    "community_model3_10_1 = create_bag_of_react_model([model_DP, model_SA],[BIOMASS_DP, BIOMASS_SA], [10,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  2.558694612608908\n",
      "DP growth:  -4.679449020693844e-12\n",
      "SA growth:  2.5586946126135874\n",
      "MBR Weights 10:1:  2.9045218796909653\n",
      "DP growth:  0.07290151281797515\n",
      "SA growth:  2.1755067515112136\n"
     ]
    }
   ],
   "source": [
    "community_model1.set_weights([1,1])\n",
    "print(\"MBR Weights 1:1: \", community_model1.slim_optimize())\n",
    "single_growth = community_model1.optimize().x[community_model1.objective_c != 0]\n",
    "print(\"DP growth: \", single_growth[0])\n",
    "print(\"SA growth: \", single_growth[1])\n",
    "community_model1.set_weights([10,1])\n",
    "print(\"MBR Weights 10:1: \", community_model1.slim_optimize())\n",
    "single_growth = community_model1.optimize().x[community_model1.objective_c != 0]\n",
    "print(\"DP growth: \", single_growth[0])\n",
    "print(\"SA growth: \", single_growth[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  2.558694612608013\n",
      "SA growth:  2.5586946126067533\n",
      "DP growth:  1.2596206122997742e-12\n",
      "MBR Weights 10:1:  2.9045218796909653\n",
      "SA growth:  2.175506751511213\n",
      "DP growth:  0.0729015128179752\n"
     ]
    }
   ],
   "source": [
    "community_model2.weights = [1,1]\n",
    "print(\"MBR Weights 1:1: \", community_model2.optimize())\n",
    "print(\"SA growth: \", community_model2.x2[community_model2.obj2].x)\n",
    "print(\"DP growth: \", community_model2.x1[community_model2.obj1].x)\n",
    "community_model2.weights = [10,1]\n",
    "print(\"MBR Weights 10:1: \", community_model2.optimize())\n",
    "print(\"SA growth: \", community_model2.x2[community_model2.obj2].x)\n",
    "print(\"DP growth: \", community_model2.x1[community_model2.obj1].x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  2.558694612705676\n",
      "SA growth: 2.558694612734215\n",
      "DP growth: -2.8539162925648968e-11\n",
      "MBR Weights 10:1:  2.9045218796909635\n",
      "SA growth: 2.1755067515112225\n",
      "DP growth: 0.07290151281797412\n"
     ]
    }
   ],
   "source": [
    "print(\"MBR Weights 1:1: \", community_model3_1_1.slim_optimize())\n",
    "print(\"SA growth: \" + str(community_model3_1_1.reactions.get_by_id(BIOMASS_SA).flux))\n",
    "print(\"DP growth: \" + str(community_model3_1_1.reactions.get_by_id(BIOMASS_DP).flux))\n",
    "print(\"MBR Weights 10:1: \", community_model3_10_1.slim_optimize())\n",
    "print(\"SA growth: \" + str(community_model3_10_1.reactions.get_by_id(BIOMASS_SA).flux))\n",
    "print(\"DP growth: \" + str(community_model3_10_1.reactions.get_by_id(BIOMASS_DP).flux))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "coopm = community_model2.compute_coopm()\n",
    "coopm2 = optimize_coopm_community(community_model3_1_1, community_model3_1_1.slim_optimize(), [BIOMASS_DP, BIOMASS_SA], [1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EX_glc__D_e': 5.746981720642888,\n",
       " 'EX_4abz_e': 1.8817593176694293e-05,\n",
       " 'EX_cl_e': 0.0031451105499038278,\n",
       " 'EX_thr__L_e': 0.036136514774758904,\n",
       " 'EX_k_e': 0.055481726720438156,\n",
       " 'EX_pro__L_e': 0.006217822150725922,\n",
       " 'EX_ca2_e': 0.0015098276484246155,\n",
       " 'EX_mg2_e': 0.004886748996120382,\n",
       " 'EX_mn2_e': 0.0012925558614843122,\n",
       " 'EX_cobalt2_e': 0.001026770521430396,\n",
       " 'EX_zn2_e': 0.0010192218981230695,\n",
       " 'EX_cu2_e': 0.0009522373161735098,\n",
       " 'EX_o2_e': 7.376856052262698,\n",
       " 'EX_fe2_e': 0.006453594204572476,\n",
       " 'EX_so4_e': 0.0018283165616786784,\n",
       " 'EX_thm_e': 0.00013581420683153394,\n",
       " 'EX_cys__L_e': 0.1968042976978547,\n",
       " 'EX_na1_e': 0.5804867061673419,\n",
       " 'EX_ni2_e': 0.0010309655343456302,\n",
       " 'EX_nac_e': 0.0003704532521579851,\n",
       " 'EX_26dap__M_e': 0.0028127941692003876,\n",
       " 'EX_mobd_e': 2.0331653157836757e-06}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coopm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EX_cl_e': 0.016302785473123364,\n",
       " 'EX_arg__L_e': 3.552713678800501e-15,\n",
       " 'EX_k_e': 0.061146276356333594,\n",
       " 'EX_ca2_e': 0.013300598274540035,\n",
       " 'EX_mg2_e': 0.024043860079501277,\n",
       " 'EX_mn2_e': 0.00987131638492656,\n",
       " 'EX_cobalt2_e': 0.009045179587176941,\n",
       " 'EX_zn2_e': 0.008232565782979461,\n",
       " 'EX_cu2_e': 0.008388590590234104,\n",
       " 'EX_o2_e': 0.002861492940135712,\n",
       " 'EX_fe2_e': 0.02286686831387244,\n",
       " 'EX_so4_e': 0.006604821602657734,\n",
       " 'EX_thm_e': 0.00016937469269103357,\n",
       " 'EX_cys__L_e': 10.0,\n",
       " 'EX_na1_e': 0.532239187962162,\n",
       " 'EX_ni2_e': 0.00908213491886679,\n",
       " 'EX_nac_e': 0.0030688309233153337,\n",
       " 'EX_mobd_e': 1.791086228893973e-06}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coopm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  0.31944228579680517\n",
      "SA growth:  0.29045481875022927\n",
      "DP growth:  0.028987467046575892\n",
      "MBR Weights 10:1:  0.5755915442522821\n",
      "SA growth:  0.29047649842778606\n",
      "DP growth:  0.028511504582449602\n"
     ]
    }
   ],
   "source": [
    "community_model2.set_medium(coopm)\n",
    "community_model2.weights = [1,1]\n",
    "print(\"MBR Weights 1:1: \", community_model2.optimize())\n",
    "print(\"SA growth: \", community_model2.x2[community_model2.obj2].x)\n",
    "print(\"DP growth: \", community_model2.x1[community_model2.obj1].x)\n",
    "community_model2.weights = [10,1]\n",
    "print(\"MBR Weights 10:1: \", community_model2.optimize())\n",
    "print(\"SA growth: \", community_model2.x2[community_model2.obj2].x)\n",
    "print(\"DP growth: \", community_model2.x1[community_model2.obj1].x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  0.31949740676623206\n",
      "SA growth: 0.29045218796934646\n",
      "DP growth: 0.02904521879688559\n",
      "MBR Weights 10:1:  0.580904375938417\n",
      "SA growth: 0.2904521879693519\n",
      "DP growth: 0.02904521879690651\n"
     ]
    }
   ],
   "source": [
    "community_model3_1_1.medium = coopm\n",
    "print(\"MBR Weights 1:1: \", community_model3_1_1.slim_optimize())\n",
    "print(\"SA growth: \" + str(community_model3_1_1.reactions.get_by_id(BIOMASS_SA).flux))\n",
    "print(\"DP growth: \" + str(community_model3_1_1.reactions.get_by_id(BIOMASS_DP).flux))\n",
    "community_model3_10_1.medium = coopm\n",
    "print(\"MBR Weights 10:1: \", community_model3_10_1.slim_optimize())\n",
    "print(\"SA growth: \" + str(community_model3_10_1.reactions.get_by_id(BIOMASS_SA).flux))\n",
    "print(\"DP growth: \" + str(community_model3_10_1.reactions.get_by_id(BIOMASS_DP).flux))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  0.0\n",
      "SA growth:  0.0\n",
      "DP growth:  0.0\n",
      "MBR Weights 10:1:  0.0\n",
      "SA growth:  0.0\n",
      "DP growth:  0.0\n"
     ]
    }
   ],
   "source": [
    "community_model2.set_medium(coopm2)\n",
    "community_model2.weights = [1,1]\n",
    "print(\"MBR Weights 1:1: \", community_model2.optimize())\n",
    "print(\"SA growth: \", community_model2.x2[community_model2.obj2].x)\n",
    "print(\"DP growth: \", community_model2.x1[community_model2.obj1].x)\n",
    "community_model2.weights = [10,1]\n",
    "print(\"MBR Weights 10:1: \", community_model2.optimize())\n",
    "print(\"SA growth: \", community_model2.x2[community_model2.obj2].x)\n",
    "print(\"DP growth: \", community_model2.x1[community_model2.obj1].x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MBR Weights 1:1:  0.5117389225411352\n",
      "SA growth: 0.2558694612705676\n",
      "DP growth: 0.2558694612705676\n",
      "MBR Weights 10:1:  2.8145640739762436\n",
      "SA growth: 0.2558694612705676\n",
      "DP growth: 0.2558694612705676\n"
     ]
    }
   ],
   "source": [
    "community_model3_1_1.medium = coopm2\n",
    "print(\"MBR Weights 1:1: \", community_model3_1_1.slim_optimize())\n",
    "print(\"SA growth: \" + str(community_model3_1_1.reactions.get_by_id(BIOMASS_SA).flux))\n",
    "print(\"DP growth: \" + str(community_model3_1_1.reactions.get_by_id(BIOMASS_DP).flux))\n",
    "community_model3_10_1.medium = coopm2\n",
    "print(\"MBR Weights 10:1: \", community_model3_10_1.slim_optimize())\n",
    "print(\"SA growth: \" + str(community_model3_10_1.reactions.get_by_id(BIOMASS_SA).flux))\n",
    "print(\"DP growth: \" + str(community_model3_10_1.reactions.get_by_id(BIOMASS_DP).flux))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COOPM alphas model\n",
    "\n",
    "Here is a collection of COOPM medias for different alpha values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "community_model2 = MIP_community_model(model1, model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = [0.,0.01,0.1,0.2,0.5,0.8,0.9,0.99,1.]\n",
    "coopms = []\n",
    "for alpha in alphas:\n",
    "    coopms.append(community_model2.compute_alpha_coopm(alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "      </g>\r\n",
       "     </g>\r\n",
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       "      <!-- EX_ca2_e -->\r\n",
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       "      </g>\r\n",
       "     </g>\r\n",
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       "      <!-- EX_cobalt2_e -->\r\n",
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       "      <!-- EX_cu2_e -->\r\n",
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       "      </g>\r\n",
       "     </g>\r\n",
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       "      <!-- EX_o2_e -->\r\n",
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       "      </g>\r\n",
       "     </g>\r\n",
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       "      <!-- EX_ni2_e -->\r\n",
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   "source": [
    "df = pd.DataFrame(coopms)\n",
    "df.index = alphas\n",
    "df.T.plot.bar(figsize=(20,10))\n",
    "plt.yscale(\"log\")\n",
    "plt.ylabel(\"COOPM medium flux\")\n",
    "plt.xlabel(\"COOPM medium\")\n",
    "plt.savefig(\"COOPM_alpha_plot.pdf\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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