community_model_shuttle_COMPM_14.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "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": 52,
   "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": 53,
   "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": 54,
   "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",
       "  </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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snm3 = pd.read_csv(\"SNM3.csv\", sep =\";\")\n",
    "snm3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "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": 57,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## 1) Constructing of community model explicitely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_stoichiometry_matrix(model):\n",
    "    metabolites = model.metabolites \n",
    "    reactions = model.reactions \n",
    "    S = np.zeros((len(metabolites), len(reactions)))\n",
    "    \n",
    "    met_id = dict()\n",
    "    rec_id = dict()\n",
    "    for i,reaction in enumerate(model.reactions):\n",
    "        rec_id[reaction.id] = i\n",
    "        for metabolite, stoich in reaction.metabolites.items():\n",
    "            met_id[metabolite.id] = int(metabolites.index(metabolite))\n",
    "            S[metabolites.index(metabolite), i] = stoich\n",
    "    return S, met_id, rec_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model():\n",
    "    def __init__(self, model, biomass_function):\n",
    "        \"\"\" This is a new class of metabolic model, capable of flux balance analysis\n",
    "        Attributes:\n",
    "        models (list): CobraPy models of single organisms which will be used in construction\n",
    "        biomass_reactions (list): List of strings containing the ids for the growth reactions\n",
    "        \"\"\"\n",
    "        self.biomass_function = biomass_function\n",
    "        self.model = model\n",
    "        self.id = model.id\n",
    "        # Compute stoichimetry_matrix\n",
    "        S, met_id, rec_id = create_stoichiometry_matrix(model)\n",
    "        self.num_reactions = S.shape[1]\n",
    "        self.num_metabolites = S.shape[0]\n",
    "        self.stoichiometry_matrix = scipy.sparse.csr_matrix(S)\n",
    "        self.met_id = met_id\n",
    "        self.rec_id = rec_id \n",
    "        # Set objective\n",
    "        idx = self.rec_id[biomass_function]\n",
    "        c = np.zeros(self.num_reactions)\n",
    "        c[idx] = 1\n",
    "        self.objective_c = c\n",
    "        # Set bounds\n",
    "        self._reset_bounds()\n",
    "    \n",
    "    @property\n",
    "    def reactions(self):\n",
    "        return self.model.reactions\n",
    "    @property\n",
    "    def exchanges(self):\n",
    "        return self.model.exchanges\n",
    "    @property\n",
    "    def metabolites(self):\n",
    "        return self.model.metabolites\n",
    "    @property\n",
    "    def medium(self):\n",
    "        return self.model.medium\n",
    "\n",
    "    def set_medium(self, medium):\n",
    "        ex_ids = [ex.id for ex in self.exchanges]\n",
    "        new_med = {}\n",
    "        for key,val in medium.items():\n",
    "            if key in ex_ids:\n",
    "                new_med[key] = val\n",
    "        self.model.medium = new_med\n",
    "        self._reset_bounds()\n",
    "        \n",
    "    def optimize(self, disp=False):\n",
    "        sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
    "        sol[\"fun\"] = -sol[\"fun\"] # As we have to minimize\n",
    "        return sol \n",
    "    \n",
    "    def slim_optimize(self, disp=False):\n",
    "        sol = self.optimize(disp=disp)\n",
    "        return sol[\"fun\"]\n",
    "\n",
    "    def summary(self):\n",
    "        sol = self.optimize()\n",
    "        flux = sol[\"x\"]\n",
    "        ex_ids = [ex.id for ex in self.exchanges]\n",
    "        fluxes = []\n",
    "        for ex in ex_ids:\n",
    "            idx = self.rec_id[ex]\n",
    "            fluxes.append(flux[idx])\n",
    "        summary_df = pd.DataFrame({\"Exchange reaction\": ex_ids, \"Flux\": fluxes})\n",
    "        summary_df.sort_values([\"Flux\"], inplace=True)\n",
    "        return summary_df\n",
    "\n",
    "    def _reset_bounds(self):\n",
    "        self.bounds = []\n",
    "        for rec in self.model.reactions:\n",
    "            self.bounds.append((rec.lower_bound, rec.upper_bound))\n",
    "\n",
    "    def __add__(self, model2):\n",
    "        \"\"\" Adding another model creates a community model \"\"\"\n",
    "        return CommunityModel([self,model2], [1.,1.])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CommunityModel(Model):\n",
    "    def __init__(self, models, weight):\n",
    "        self.models = models\n",
    "        self.id = \"|\".join([model.id for model in models])\n",
    "        self.shared_exchanges = []\n",
    "        self.weight = weight\n",
    "        for model in models:\n",
    "            for rec in model.exchanges:\n",
    "                if rec.id not in self.shared_exchanges:\n",
    "                    self.shared_exchanges.append(rec.id)\n",
    "        # Create community stoichimetry matrix with shuttel reactions!\n",
    "        self._shifts = [len(self.shared_exchanges)]\n",
    "        for i,model in enumerate(models):\n",
    "            self._shifts.append(self._shifts[i] + model.num_reactions)\n",
    "        S_EX = -np.eye(self._shifts[0])\n",
    "        matrices = [S_EX] + [m.stoichiometry_matrix.todense() for m in models]\n",
    "        S = scipy.linalg.block_diag(*matrices)\n",
    "        self.num_reactions = S.shape[1]\n",
    "        self.num_metabolites = S.shape[0]\n",
    "        for i, id in enumerate(self.shared_exchanges):\n",
    "            for j,model in enumerate(models):\n",
    "                if id in model.rec_id:\n",
    "                    S[i,self._shifts[j] + model.rec_id[id]] = 1\n",
    "        self.stoichiometry_matrix = scipy.sparse.csr_matrix(S)\n",
    "        # Cretae objective:\n",
    "        self._weighted_objective(weight)\n",
    "        # Create bounds\n",
    "        self._reset_bounds()\n",
    "    \n",
    "    @property\n",
    "    def reactions(self):\n",
    "        return [model.reactions for model in self.models]\n",
    "    @property\n",
    "    def exchanges(self):\n",
    "        return self.shared_exchanges\n",
    "    @property\n",
    "    def metabolites(self):\n",
    "        return [model.metabolites for model in self.models]\n",
    "    @property\n",
    "    def medium(self):\n",
    "        medium = {}\n",
    "        for model in self.models:\n",
    "            for key,val in model.medium:\n",
    "                medium[key] = val\n",
    "        return medium\n",
    "\n",
    "    def _weighted_objective(self, weight):\n",
    "        self.weight = weight\n",
    "        self.objective_c = np.zeros(self._shifts[0])\n",
    "        for i,model in enumerate(self.models):\n",
    "            self.objective_c = np.append(self.objective_c, weight[i]*model.objective_c)\n",
    "    def _reset_bounds(self):\n",
    "        self.bounds = []\n",
    "        for id in self.shared_exchanges:\n",
    "            min_lower_bound = 0\n",
    "            for model in self.models:\n",
    "                if id in model.rec_id:\n",
    "                    rec = model.reactions.get_by_id(id)\n",
    "                    if rec.lower_bound < min_lower_bound:\n",
    "                        min_lower_bound = rec.lower_bound \n",
    "            self.bounds.append((min_lower_bound, 1000))\n",
    "        for model in self.models:\n",
    "            self.bounds += model.bounds\n",
    "\n",
    "    def set_medium(self, medium):\n",
    "        for model in self.models:\n",
    "            model.set_medium(medium)\n",
    "        self._reset_bounds()\n",
    "\n",
    "    def summary(self):\n",
    "        sol = self.optimize()\n",
    "        flux = sol[\"x\"]\n",
    "        ex_ids = self.shared_exchanges\n",
    "        ex_flux = flux[:len(ex_ids)]\n",
    "        df_ex = pd.DataFrame({\"Exchanges\": ex_ids, \"Flux\": ex_flux})\n",
    "        df_ex.sort_values([\"Flux\"], inplace=True)\n",
    "        for i,model in enumerate(self.models):\n",
    "            shuttel_ids = [ex.id for ex in model.exchanges]\n",
    "            id = str(model.id) + \" Shuttel Flux\"\n",
    "            df_ex[id] = 0.\n",
    "            for sh in shuttel_ids:\n",
    "                idx = model.rec_id[sh]\n",
    "                df_ex[id][ex_ids.index(sh)] = flux[self._shifts[i] +idx]\n",
    "        \n",
    "        return df_ex\n",
    "    \n",
    "    def set_weights(self, weight):\n",
    "        self._weighted_objective(weight)\n",
    "    \n",
    "    def get_model_growths(self):\n",
    "        mask = self.objective_c != 0\n",
    "        sol = self.optimize()\n",
    "        flux = sol[\"x\"]\n",
    "        return flux[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth DP:  0.28236539253224774\n",
      "Growth SA:  2.5586946126118373\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Sparse constraint matrix detected; setting 'sparse':True.\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Unknown solver options: sparse\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n"
     ]
    }
   ],
   "source": [
    "model_DP = Model(model_DP, BIOMASS_DP)\n",
    "print(\"Growth DP: \",model_DP.slim_optimize())\n",
    "model_SA = Model(model_SA, BIOMASS_SA)\n",
    "print(\"Growth SA: \",model_SA.slim_optimize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Sparse constraint matrix detected; setting 'sparse':True.\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Unknown solver options: sparse\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights 1:1:  2.5586946126136296\n",
      "SA growth:  2.5586946126136296\n",
      "DP growth:  -0.0\n"
     ]
    }
   ],
   "source": [
    "# Community model\n",
    "model = model_SA + model_DP\n",
    "print(\"Weights 1:1: \", model.slim_optimize())\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_medium(compm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Sparse constraint matrix detected; setting 'sparse':True.\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Unknown solver options: sparse\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights 1:1:  2.5586946126124674\n",
      "SA growth:  2.558694612613441\n",
      "DP growth:  -9.732222301975288e-13\n"
     ]
    }
   ],
   "source": [
    "model = model_SA + model_DP\n",
    "print(\"Weights 1:1: \", model.slim_optimize())\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Sparse constraint matrix detected; setting 'sparse':True.\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Unknown solver options: sparse\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-60-65ea33eaf871>:83: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_ex[id][ex_ids.index(sh)] = flux[self._shifts[i] +idx]\n",
      "<ipython-input-60-65ea33eaf871>:83: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_ex[id][ex_ids.index(sh)] = flux[self._shifts[i] +idx]\n"
     ]
    },
    {
     "data": {
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       "    }\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>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>EX_o2_e</td>\n",
       "      <td>-20.000000</td>\n",
       "      <td>-20.000000</td>\n",
       "      <td>4.014566e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EX_orn_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>3.400302e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>EX_pyr_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EX_cys__L_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-0.461361</td>\n",
       "      <td>-9.538639e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>EX_ser__D_e</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>EX_nh4_e</td>\n",
       "      <td>18.105153</td>\n",
       "      <td>13.105153</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EX_co2_e</td>\n",
       "      <td>27.946935</td>\n",
       "      <td>27.946935</td>\n",
       "      <td>7.097349e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>30.889459</td>\n",
       "      <td>30.889459</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>33.465960</td>\n",
       "      <td>43.465960</td>\n",
       "      <td>-1.000000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Exchanges       Flux  iYS854 Shuttel Flux  DP_83VPs_KB5 Shuttel Flux\n",
       "42      EX_o2_e -20.000000           -20.000000               4.014566e-13\n",
       "43     EX_orn_e -10.000000           -10.000000              -0.000000e+00\n",
       "51  EX_glc__D_e -10.000000           -10.000000               3.400302e-11\n",
       "52     EX_pyr_e -10.000000           -10.000000               0.000000e+00\n",
       "24  EX_cys__L_e -10.000000            -0.461361              -9.538639e+00\n",
       "..          ...        ...                  ...                        ...\n",
       "98  EX_ser__D_e  10.000000             0.000000               1.000000e+01\n",
       "39     EX_nh4_e  18.105153            13.105153               5.000000e+00\n",
       "23     EX_co2_e  27.946935            27.946935               7.097349e-13\n",
       "19  EX_lac__L_e  30.889459            30.889459               0.000000e+00\n",
       "10     EX_h2o_e  33.465960            43.465960              -1.000000e+01\n",
       "\n",
       "[111 rows x 4 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = model.summary()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Summary_exchange_shuttels_COMPM_weight_1_1_RM.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Sparse constraint matrix detected; setting 'sparse':True.\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Unknown solver options: sparse\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights 1:10  2.904521879693608\n",
      "SA growth:  2.175506751508334\n",
      "DP growth:  0.07290151281852739\n"
     ]
    }
   ],
   "source": [
    "model.set_weights([1,10])\n",
    "print(\"weights 1:10 \", model.slim_optimize())\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Sparse constraint matrix detected; setting 'sparse':True.\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-59-5ceee4aecab6>:49: OptimizeWarning: Unknown solver options: sparse\n",
      "  sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
      "<ipython-input-60-65ea33eaf871>:83: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_ex[id][ex_ids.index(sh)] = flux[self._shifts[i] +idx]\n",
      "<ipython-input-60-65ea33eaf871>:83: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_ex[id][ex_ids.index(sh)] = flux[self._shifts[i] +idx]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>EX_o2_e</td>\n",
       "      <td>-20.000000</td>\n",
       "      <td>-19.967526</td>\n",
       "      <td>-0.032474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>EX_ser__L_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EX_orn_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-7.418185</td>\n",
       "      <td>-2.581815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EX_cys__L_e</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-0.361048</td>\n",
       "      <td>-9.638952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>EX_nh4_e</td>\n",
       "      <td>15.292785</td>\n",
       "      <td>12.282427</td>\n",
       "      <td>3.010359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>EX_ser__D_e</td>\n",
       "      <td>15.845526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.845526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>26.201712</td>\n",
       "      <td>26.201712</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EX_co2_e</td>\n",
       "      <td>29.082032</td>\n",
       "      <td>29.136054</td>\n",
       "      <td>-0.054022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>31.159853</td>\n",
       "      <td>41.159853</td>\n",
       "      <td>-10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Exchanges       Flux  iYS854 Shuttel Flux  DP_83VPs_KB5 Shuttel Flux\n",
       "42      EX_o2_e -20.000000           -19.967526                  -0.032474\n",
       "56  EX_ser__L_e -10.000000           -10.000000                   0.000000\n",
       "43     EX_orn_e -10.000000           -10.000000                  -0.000000\n",
       "51  EX_glc__D_e -10.000000            -7.418185                  -2.581815\n",
       "24  EX_cys__L_e -10.000000            -0.361048                  -9.638952\n",
       "..          ...        ...                  ...                        ...\n",
       "39     EX_nh4_e  15.292785            12.282427                   3.010359\n",
       "98  EX_ser__D_e  15.845526             0.000000                  15.845526\n",
       "19  EX_lac__L_e  26.201712            26.201712                   0.000000\n",
       "23     EX_co2_e  29.082032            29.136054                  -0.054022\n",
       "10     EX_h2o_e  31.159853            41.159853                 -10.000000\n",
       "\n",
       "[111 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = model.summary()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Summary_exchange_shuttels_COMPM_weight_10_1_RM.csv\")"
   ]
  }
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