{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0594e679",
   "metadata": {},
   "source": [
    "# Libraries and useful functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b84bb91",
   "metadata": {},
   "source": [
    "**Important**\n",
    "\n",
    "The results of this Python tutorial might be different from the one you generate in MatLab because we are going to use a different E. Coli model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e880402b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Set parameter LicenseID to value 2583256\n",
      "Academic license - for non-commercial use only - expires 2025-11-13\n"
     ]
    }
   ],
   "source": [
    "from cobra.io import load_matlab_model\n",
    "from gurobipy import GRB \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from pytfa.io import load_thermoDB,                  \\\n",
    "                        read_lexicon, annotate_from_lexicon,            \\\n",
    "                        read_compartment_data, apply_compartment_data\n",
    "\n",
    "from pytfa.io.json import load_json_model, save_json_model\n",
    "\n",
    "from pytfa import ThermoModel\n",
    "\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce5a7679",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_path = './models/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce6faf27",
   "metadata": {},
   "outputs": [],
   "source": [
    "def setSolver(model, solver_name):\n",
    "    \"\"\"\n",
    "    Set the solver for a given COBRA model.\n",
    "\n",
    "    Parameters:\n",
    "    model (cobra.Model): The COBRA model to set the solver for.\n",
    "    solver_name (str): The name of the solver to use ('cplex' or 'gurobi').\n",
    "\n",
    "    Returns:\n",
    "    None: The function modifies the model in place.\n",
    "    \"\"\"\n",
    "    model.solver = solver_name\n",
    "\n",
    "def printSolverParams(model, solver):\n",
    "    if solver == \"gurobi\":\n",
    "        print(model.solver.problem.Params)\n",
    "    elif solver == \"cplex\":\n",
    "        print(\"feasibility:\" + str(model.solver.configuration.tolerances.feasibility))\n",
    "        print(\"integrality:\"+ str(model.solver.configuration.tolerances.integrality))\n",
    "        print(\"optimality\" + str(model.solver.configuration.tolerances.optimality))\n",
    "    elif solver == \"glpk\":\n",
    "        print(\"feasibility:\" + str(model.solver.configuration.tolerances.feasibility))\n",
    "        print(\"integrality:\"+ str(model.solver.configuration.tolerances.integrality))\n",
    "    else:\n",
    "        raise ValueError(\"Cannot modify tolerance params for solver \" + solver)\n",
    "    pass\n",
    "\n",
    "def setSolverParams(model, solver, FeasibilityTol =1e-9, IntFeasTol=1e-9, OptimalityTol=1e-9):\n",
    "    \"\"\"\n",
    "    Set solver parameters for a given COBRA model.\n",
    "    Parameters:\n",
    "    model (cobra.Model): The COBRA model to set the solver parameters for.\n",
    "    solver (str): The name of the solver to use ('cplex' or 'gurobi').\n",
    "    FeasibilityTol (float): The feasibility tolerance to set.\n",
    "    IntFeasTol (float): The integrality tolerance to set.\n",
    "    OptimalityTol (float): The optimality tolerance to set.\n",
    "\n",
    "    Returns:\n",
    "    None: The function modifies the model in place.\n",
    "    \"\"\"\n",
    "    if solver == \"gurobi\":\n",
    "        model.solver.problem.Params.FeasibilityTol = FeasibilityTol\n",
    "        model.solver.problem.Params.IntFeasTol = IntFeasTol\n",
    "        model.solver.problem.Params.OptimalityTol = OptimalityTol\n",
    "        model.solver.problem.Params.Presolve = 1\n",
    "        model.solver.problem.Params.Timelimit = 3600\n",
    "        model.solver.problem.setParam(GRB.Param.NumericFocus, 1)\n",
    "        model.solver.problem.setParam(GRB.Param.ScaleFlag, 2)\n",
    "    elif solver == \"cplex\":\n",
    "        model.solver.configuration.tolerances.feasibility = FeasibilityTol\n",
    "        model.solver.configuration.tolerances.integrality = IntFeasTol\n",
    "        model.solver.configuration.tolerances.optimality = OptimalityTol\n",
    "        model.solver.problem.parameters.read_scale = -1\n",
    "        model.solver.problem.parameters.emphasis.numerical = 1\n",
    "        model.solver.problem.parameters.timelimit = 3600\n",
    "        model.solver.configuration.presolve = True\n",
    "        #model_engro2_thermo.solver.problem.parameters.mip.display.set(3)\n",
    "        #model_engro2_thermo.solver.problem.set_results_stream()\n",
    "    elif solver == \"glpk\":\n",
    "        model.solver.configuration.tolerances.feasibility = FeasibilityTol\n",
    "        model.solver.configuration.tolerances.integrality = IntFeasTol\n",
    "    else:\n",
    "        raise ValueError(\"Cannot modify tolerance params for solver \" + solver)\n",
    "    \n",
    "    printSolverParams(model, solver)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c8d35f",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f29adb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No defined compartments in model iMM904. Compartments will be deduced heuristically using regular expressions.\n",
      "Using regular expression found the following compartments:c, e, g, m, n, r, v, x\n"
     ]
    }
   ],
   "source": [
    "model_yeast =  load_matlab_model(os.path.join(models_path, \"Yeast.mat\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84651cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This model seems to have metCharge instead of metCharges field. Will use metCharge for what metCharges represents.\n",
      "No defined compartments in model model_red. Compartments will be deduced heuristically using regular expressions.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using regular expression found the following compartments:c, e, p\n"
     ]
    }
   ],
   "source": [
    "model_ecoli =  load_matlab_model(os.path.join(models_path, \"small_ecoli.mat\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e43286a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feasibility:1e-09\n",
      "integrality:1e-09\n",
      "optimality1e-09\n",
      "feasibility:1e-09\n",
      "integrality:1e-09\n",
      "optimality1e-09\n"
     ]
    }
   ],
   "source": [
    "setSolver(model_yeast, \"cplex\")\n",
    "setSolverParams(model_yeast, \"cplex\", FeasibilityTol =1e-9, IntFeasTol=1e-9, OptimalityTol=1e-9)\n",
    "\n",
    "setSolver(model_ecoli, \"cplex\")\n",
    "setSolverParams(model_ecoli, \"cplex\", FeasibilityTol =1e-9, IntFeasTol=1e-9, OptimalityTol=1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deb8ed6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yeast model:\n",
      "Number of genes: 905\n",
      "Number of reactions: 1577\n",
      "Number of metabolites: 1228\n",
      "\n",
      "\n",
      "Ecoli model:\n",
      "Number of genes: 313\n",
      "Number of reactions: 599\n",
      "Number of metabolites: 304\n"
     ]
    }
   ],
   "source": [
    "#compare number of genes reactions and metabolites across models\n",
    "print(\"Yeast model:\")\n",
    "print(\"Number of genes: \" + str(len(model_yeast.genes)))\n",
    "print(\"Number of reactions: \" + str(len(model_yeast.reactions)))\n",
    "print(\"Number of metabolites: \" + str(len(model_yeast.metabolites)))\n",
    "print(\"\\n\")\n",
    "print(\"Ecoli model:\")\n",
    "print(\"Number of genes: \" + str(len(model_ecoli.genes)))\n",
    "print(\"Number of reactions: \" + str(len(model_ecoli.reactions)))\n",
    "print(\"Number of metabolites: \" + str(len(model_ecoli.metabolites)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cc6082",
   "metadata": {},
   "source": [
    "# Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6531fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "rxns_ids_yeast = [rxn.id for rxn in model_yeast.reactions]\n",
    "rxns_ids_ecoli = [rxn.id for rxn in model_ecoli.reactions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c908bd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#let's find exchange reactions for glucose and oxygen\n",
    "print('EX_glc(e)' in rxns_ids_yeast and 'DM_glc_e' in rxns_ids_ecoli)\n",
    "print('EX_o2(e)' in rxns_ids_yeast and 'DM_o2_e' in rxns_ids_ecoli)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59da1c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's find biomass reactions\n",
    "\"biomass_SC5_notrace\" in rxns_ids_yeast and \"Ec_biomass_iJO1366_WT_53p95M\" in rxns_ids_ecoli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a267c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1.0 \\cdot biomass_{SC5 notrace} - 1.0 \\cdot biomass_{SC5 notrace reverse e32ff}$"
      ],
      "text/plain": [
       "1.0*biomass_SC5_notrace - 1.0*biomass_SC5_notrace_reverse_e32ff"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_yeast.objective.expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4c0429e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'max'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_yeast.objective.direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c88510ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8444850706132706"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_yeast.slim_optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "196e7785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1.0 \\cdot Ec_{biomass iJO1366 WT 53p95M} - 1.0 \\cdot Ec_{biomass iJO1366 WT 53p95M reverse 55db7}$"
      ],
      "text/plain": [
       "1.0*Ec_biomass_iJO1366_WT_53p95M - 1.0*Ec_biomass_iJO1366_WT_53p95M_reverse_55db7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ecoli.objective.expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3e62adab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'max'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ecoli.objective.direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "712cdcb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8109621653343251"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ecoli.slim_optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d163ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_bounds(model, bounds_dict):\n",
    "    \"\"\"\n",
    "    Set the bounds of reactions in a COBRA model.\n",
    "\n",
    "    Parameters:\n",
    "    model (cobra.Model): The COBRA model to set the bounds for.\n",
    "    bounds_dict (dict): A dictionary where keys are reaction IDs and values are tuples of (lower_bound, upper_bound).\n",
    "\n",
    "    Returns:\n",
    "    None: The function modifies the model in place.\n",
    "    \"\"\"\n",
    "    for rxn_id, (lower_bound, upper_bound) in bounds_dict.items():\n",
    "        if rxn_id in model.reactions:\n",
    "            model.reactions.get_by_id(rxn_id).bounds = (lower_bound, upper_bound)\n",
    "        else:\n",
    "            print(f\"Reaction {rxn_id} not found in the model.\")\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8a21d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def part_2(model, ex_glc_id, ex_o2_id, biomass_id):\n",
    "\n",
    "    #save original bounds\n",
    "    original_bounds = {}\n",
    "    for rxn in [ex_glc_id, ex_o2_id, biomass_id]:\n",
    "        original_bounds[rxn] = (model.reactions.get_by_id(rxn).lower_bound, model.reactions.get_by_id(rxn).upper_bound)\n",
    "\n",
    "    #set glucose and o2 uptake rates as requested\n",
    "    model.reactions.get_by_id(ex_glc_id).lower_bound = -10\n",
    "    model.reactions.get_by_id(ex_glc_id).upper_bound = 0\n",
    "    model.reactions.get_by_id(ex_o2_id).lower_bound = -100\n",
    "    model.reactions.get_by_id(ex_o2_id).upper_bound = 0\n",
    "\n",
    "    # Allow model not to grow\n",
    "    model.reactions.get_by_id(biomass_id).lower_bound = 0\n",
    "\n",
    "    #set objective function to biomass\n",
    "    model.objective = biomass_id\n",
    "\n",
    "    #set objective sense to maximize\n",
    "    model.objective.direction = 'max'\n",
    "\n",
    "    #optimize\n",
    "    solution_aerobic = model.optimize()\n",
    "\n",
    "    #now set anaerobic conditions\n",
    "    model.reactions.get_by_id(ex_o2_id).lower_bound = 0\n",
    "    model.reactions.get_by_id(ex_o2_id).upper_bound = 0\n",
    "    \n",
    "    solution_anaerobic = model.optimize()\n",
    "\n",
    "    set_bounds(model, original_bounds)\n",
    "\n",
    "\n",
    "    return solution_aerobic, solution_anaerobic, original_bounds\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90315e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution_aerobic_yeast, solution_anaerobic_yeast, original_bounds_yeast = part_2(model_yeast, \"EX_glc(e)\", \"EX_o2(e)\", \"biomass_SC5_notrace\")\n",
    "solution_aerobic_ecoli, solution_anaerobic_ecoli, original_bounds_ecoli = part_2(model_ecoli, \"DM_glc_e\", \"DM_o2_e\", \"Ec_biomass_iJO1366_WT_53p95M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98df8fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.975</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biomass_SC5_notrace</th>\n",
       "      <td>0.974881</td>\n",
       "      <td>-5.123289e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_13BDglcn(e)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.961300e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_2hb(e)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.600767e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_2mbac(e)</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_2mbald(e)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.336254e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YUMPS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTAT_SC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTESTH_SC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.313180e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTESTH_SCe</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTt</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1577 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.975 at 0x7af5cd3940d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution_aerobic_yeast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bcb29b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.000</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biomass_SC5_notrace</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_13BDglcn(e)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_2hb(e)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_2mbac(e)</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_2mbald(e)</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YUMPS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTAT_SC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTESTH_SC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTESTH_SCe</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYMSTt</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1577 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.000 at 0x7af5cd45a920>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution_anaerobic_yeast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d1f7792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.994</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DM_4CRSOL</th>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_5DRIB</th>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_AMOB</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_MTHTHF</th>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ec_biomass_iJO1366_WT_53p95M</th>\n",
       "      <td>0.993826</td>\n",
       "      <td>3.569410e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_250_trp-L_c</th>\n",
       "      <td>0.054893</td>\n",
       "      <td>-2.775558e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_251_tyr-L_c</th>\n",
       "      <td>0.133166</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_252_udcpdp_c</th>\n",
       "      <td>0.000055</td>\n",
       "      <td>-6.661338e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_253_utp_c</th>\n",
       "      <td>0.139236</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_254_val-L_c</th>\n",
       "      <td>0.408645</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>599 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.994 at 0x7af5cfcbf760>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution_aerobic_ecoli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b1303e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.273</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DM_4CRSOL</th>\n",
       "      <td>6.090768e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_5DRIB</th>\n",
       "      <td>3.067234e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_AMOB</th>\n",
       "      <td>5.462572e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_MTHTHF</th>\n",
       "      <td>3.659923e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ec_biomass_iJO1366_WT_53p95M</th>\n",
       "      <td>2.731286e-01</td>\n",
       "      <td>1.254270e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_250_trp-L_c</th>\n",
       "      <td>1.508599e-02</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_251_tyr-L_c</th>\n",
       "      <td>3.659732e-02</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_252_udcpdp_c</th>\n",
       "      <td>1.502207e-05</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_253_utp_c</th>\n",
       "      <td>3.826559e-02</td>\n",
       "      <td>4.163336e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_254_val-L_c</th>\n",
       "      <td>1.123061e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>599 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.273 at 0x7af5d0110bb0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution_anaerobic_ecoli"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb38d5d1",
   "metadata": {},
   "source": [
    "# Part 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f811d6",
   "metadata": {},
   "source": [
    "**Here we are going to use pyTFA to convert the COBRApy model of E. Coli to a thermo model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c5d92e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "def part_3(model, ex_glc_id, ex_o2_id, biomass_id, filename):\n",
    "\n",
    "\n",
    "    model.objective = biomass_id\n",
    "    model.objective.direction = 'max'\n",
    "\n",
    "    # Create a grid of flux values between 0 and 10\n",
    "    x = np.arange(0, 11, 1)  # x for glucose\n",
    "    y = np.arange(0, 11, 1)  # y for oxygen\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "\n",
    "    # Allocate an array for the solution\n",
    "    Z = np.zeros((len(x), len(y)))\n",
    "\n",
    "    # Store original bounds to restore them\n",
    "    original_glc_lb = model.reactions.get_by_id(ex_glc_id).lower_bound\n",
    "    original_glc_ub = model.reactions.get_by_id(ex_glc_id).upper_bound\n",
    "    original_o2_lb = model.reactions.get_by_id(ex_o2_id).lower_bound\n",
    "    original_o2_ub = model.reactions.get_by_id(ex_o2_id).upper_bound\n",
    "\n",
    "    # Loop over the grid (matching MATLAB indexing)\n",
    "    for id_x in range(len(x)):\n",
    "        for id_y in range(len(y)):\n",
    "\n",
    "            # FIX the glucose and oxygen uptake rates\n",
    "            model.reactions.get_by_id(ex_glc_id).bounds = (-X[id_x, id_y], -X[id_x, id_y])\n",
    "            model.reactions.get_by_id(ex_o2_id).bounds = (-Y[id_x, id_y], -Y[id_x, id_y])\n",
    "\n",
    "            # Solve the FBA model\n",
    "            try:\n",
    "                sol = model.optimize()\n",
    "                \n",
    "                # Save the solution on the grid\n",
    "                if sol.status == 'optimal':\n",
    "                    Z[id_x, id_y] = sol.objective_value\n",
    "                else:\n",
    "                    print(f\"Non-optimal status: {sol.status} at glucose={-X[id_x, id_y]}, oxygen={-Y[id_x, id_y]}\")\n",
    "                    Z[id_x, id_y] = 0\n",
    "                    \n",
    "            except Exception as e:\n",
    "                print(f\"Optimization failed at glucose={-X[id_x, id_y]}, oxygen={-Y[id_x, id_y]}: {e}\")\n",
    "                Z[id_x, id_y] = 0\n",
    "            \n",
    "            # Restore original bounds for next iteration\n",
    "            model.reactions.get_by_id(ex_glc_id).bounds = (original_glc_lb, original_glc_ub)\n",
    "            model.reactions.get_by_id(ex_o2_id).bounds = (original_o2_lb, original_o2_ub)\n",
    "\n",
    "    # Plot the solution\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    # Create surface plot\n",
    "    surf = ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.8)\n",
    "\n",
    "    # Add labels and title\n",
    "    ax.set_xlabel('Glucose uptake [mmol/gDW/hr]')\n",
    "    ax.set_ylabel('Oxygen uptake [mmol/gDW/hr]')\n",
    "    ax.set_zlabel('Growth rate [1/hr]')\n",
    "    ax.set_title('Phenotypic Phase Plane')\n",
    "\n",
    "    # Add colorbar\n",
    "    fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "\n",
    "    # Save the figure\n",
    "    plt.savefig(filename, dpi=1200, bbox_inches='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a77fdd16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This model seems to have metCharge instead of metCharges field. Will use metCharge for what metCharges represents.\n",
      "No defined compartments in model model_red. Compartments will be deduced heuristically using regular expressions.\n",
      "Using regular expression found the following compartments:c, e, p\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read LP format model from file /tmp/tmpcv_uqy22.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 304 rows, 1198 columns, 12812 nonzeros\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:51:10,800 - thermomodel_None - INFO - # Model initialized with units kcal/mol and temperature 298.15 K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting numpy float coefficients to standard Python floats...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:51:11,169 - thermomodel_None - INFO - # Model preparation starting...\n",
      "2025-09-24 09:51:11,263 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,264 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,265 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,267 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,268 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,269 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,302 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,303 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,304 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,305 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,307 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,308 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,326 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,327 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,328 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,329 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,330 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,331 - thermomodel_None - WARNING - Warning : NULL/L\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 6406 numpy coefficients to float.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:51:11,385 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,387 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,388 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,389 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,390 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,391 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,392 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,393 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,394 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,395 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,396 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,397 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,398 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,400 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,401 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,402 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,403 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,404 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,409 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,410 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,411 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,412 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,413 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,414 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,415 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,416 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,417 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,418 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,419 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,420 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,421 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,424 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,426 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,427 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,428 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,429 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,430 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,431 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,432 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,434 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,435 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,436 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,439 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,441 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,442 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,443 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,445 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,446 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,448 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,449 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,450 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,451 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,452 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,454 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,457 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,458 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,459 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,460 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,461 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,462 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,480 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,481 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,482 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,483 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,485 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,486 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,488 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,489 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,490 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,492 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,493 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,494 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,516 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,517 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,518 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,519 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,520 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,522 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,579 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,580 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,581 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,582 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,583 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,584 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,586 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,587 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,589 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,590 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,592 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,592 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,594 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,595 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,597 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,598 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,599 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,600 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,601 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,604 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,604 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,606 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,607 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,609 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,611 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,611 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,613 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,614 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,615 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,617 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,620 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,621 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,622 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,623 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,624 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,626 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,628 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,629 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,630 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,632 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,633 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,635 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,637 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,639 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,640 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,641 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,642 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,644 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,646 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,648 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,649 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,650 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,651 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,653 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,655 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,657 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,658 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,660 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,661 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,662 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,665 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,666 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,667 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,669 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,670 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,671 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,698 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,699 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,700 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,702 - thermomodel_None - WARNING - Warning : NULL/U\n",
      "2025-09-24 09:51:11,703 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,704 - thermomodel_None - WARNING - Warning : NULL/L\n",
      "2025-09-24 09:51:11,707 - thermomodel_None - INFO - # Model preparation done.\n",
      "2025-09-24 09:51:11,709 - thermomodel_None - INFO - # Model conversion starting...\n",
      "2025-09-24 09:51:18,427 - thermomodel_None - INFO - # Model conversion done.\n",
      "2025-09-24 09:51:18,428 - thermomodel_None - INFO - # Updating cobra_model variables...\n",
      "2025-09-24 09:51:18,448 - thermomodel_None - INFO - # cobra_model variables are up-to-date\n"
     ]
    }
   ],
   "source": [
    "#This code can be found on the GitHub repository of PyTFA\n",
    "\n",
    "# Load the model\n",
    "cobra_model = load_matlab_model(os.path.join(models_path, 'small_ecoli.mat'))\n",
    "\n",
    "# Load reaction DB\n",
    "thermo_data = load_thermoDB(os.path.join(models_path, 'thermo_data.thermodb')) # Thermodynamic data\n",
    "lexicon = read_lexicon(os.path.join(models_path, 'lexicon.csv')) #dictionary of metabolites IDs\n",
    "compartment_data = read_compartment_data(os.path.join(models_path, 'compartment_data.json'))\n",
    "\n",
    "# Initialize the model\n",
    "tmodel = ThermoModel(thermo_data, cobra_model)\n",
    "tmodel.name = 'tutorial_ecoli'\n",
    "\n",
    "# Annotate the model\n",
    "annotate_from_lexicon(tmodel, lexicon)\n",
    "apply_compartment_data(tmodel, compartment_data)\n",
    "\n",
    "print(\"Converting numpy float coefficients to standard Python floats...\")\n",
    "conversion_count = 0\n",
    "for reaction in tmodel.reactions:\n",
    "    new_metabolites = {}\n",
    "    for metabolite, coeff in reaction.metabolites.items():\n",
    "        # Check if it's any kind of numpy number\n",
    "        if isinstance(coeff, np.number):\n",
    "            # Convert to standard Python float\n",
    "            new_metabolites[metabolite] = float(coeff)\n",
    "            conversion_count += 1\n",
    "        else:\n",
    "            # Keep it as is if it's already int, float, or something else expected\n",
    "            new_metabolites[metabolite] = coeff\n",
    "\n",
    "    # Important: Overwrite the reaction's metabolites dictionary\n",
    "    # Note: Directly modifying reaction.metabolites can sometimes be tricky.\n",
    "    # A safer way might be to remove all and add them back,\n",
    "    # but this direct update often works for simple coefficient type changes.\n",
    "    reaction.metabolites.clear()\n",
    "    reaction.add_metabolites(new_metabolites, combine=False) # Use combine=False to replace\n",
    "\n",
    "if conversion_count > 0:\n",
    "    print(f\"Converted {conversion_count} numpy coefficients to float.\")\n",
    "else:\n",
    "    print(\"No numpy coefficients found to convert (or they were already standard floats/ints).\")\n",
    "\n",
    "## TFA conversion\n",
    "tmodel.prepare()\n",
    "tmodel.convert()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "56be5329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feasibility:1e-09\n",
      "integrality:1e-09\n",
      "optimality1e-09\n"
     ]
    }
   ],
   "source": [
    "setSolver(tmodel, \"cplex\")\n",
    "setSolverParams(tmodel, \"cplex\", FeasibilityTol =1e-9, IntFeasTol=1e-9, OptimalityTol=1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5caf2251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.811</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DM_4CRSOL</th>\n",
       "      <td>0.000181</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_5DRIB</th>\n",
       "      <td>0.000187</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_AMOB</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_MTHTHF</th>\n",
       "      <td>0.001087</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ec_biomass_iJO1366_WT_53p95M</th>\n",
       "      <td>0.810997</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_250_trp-L_c</th>\n",
       "      <td>0.044795</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_251_tyr-L_c</th>\n",
       "      <td>0.108668</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_252_udcpdp_c</th>\n",
       "      <td>0.000045</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_253_utp_c</th>\n",
       "      <td>0.113622</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_254_val-L_c</th>\n",
       "      <td>0.333469</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>599 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.811 at 0x7af5cb0ea350>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmodel.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cd3dc379",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_json_model(tmodel, os.path.join(models_path, 'small_ecoli_thermo.json')) #save thermo model to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "982cc1ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luca/Documents/GitLab/flux_directionality_profiles/.venv/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.\n",
      "  warn(f\"Solver status is '{status}'.\", UserWarning)\n",
      "2025-09-24 09:39:03,911 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:03,913 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-1: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:05,745 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:05,747 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-2: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:08,128 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:08,130 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-3: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:10,402 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:10,404 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-4: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:12,940 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:12,942 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-5: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:15,349 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:15,351 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-6: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:17,651 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:17,652 - thermomodel_None - WARNING - Solver status: infeasible\n",
      "2025-09-24 09:39:17,735 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:17,736 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-7: CPLEX Error  1217: No solution exists.\n",
      "Optimization failed at glucose=-1, oxygen=-7: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:19,980 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:19,982 - thermomodel_None - WARNING - Solver status: infeasible\n",
      "2025-09-24 09:39:20,065 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:20,067 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-8: CPLEX Error  1217: No solution exists.\n",
      "Optimization failed at glucose=-1, oxygen=-8: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:22,271 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:22,273 - thermomodel_None - WARNING - Solver status: infeasible\n",
      "2025-09-24 09:39:22,354 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:22,355 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-9: CPLEX Error  1217: No solution exists.\n",
      "Optimization failed at glucose=-1, oxygen=-9: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:39:24,575 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:24,577 - thermomodel_None - WARNING - Solver status: infeasible\n",
      "2025-09-24 09:39:24,663 - thermomodel_None - ERROR - CPLEX Error  1217: No solution exists.\n",
      "2025-09-24 09:39:24,664 - thermomodel_None - WARNING - Solver status: infeasible\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization failed at glucose=0, oxygen=-10: CPLEX Error  1217: No solution exists.\n",
      "Optimization failed at glucose=-1, oxygen=-10: CPLEX Error  1217: No solution exists.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "part_3(tmodel, \"DM_glc_e\", \"DM_o2_e\", \"Ec_biomass_iJO1366_WT_53p95M\", \"./part3_ecoli_thermo.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "369ef1c0",
   "metadata": {},
   "source": [
    "# Part 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7f39eccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-24 09:52:27,675 - thermomodel_tutorial_ecoli - INFO - # Model initialized with units kcal/mol and temperature 298.15 K\n"
     ]
    }
   ],
   "source": [
    "tmodel = load_json_model(os.path.join(models_path, 'small_ecoli_thermo.json')) #load thermo model from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0042fe32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feasibility:1e-09\n",
      "integrality:1e-09\n",
      "optimality1e-09\n"
     ]
    }
   ],
   "source": [
    "setSolver(tmodel, \"cplex\")\n",
    "setSolverParams(tmodel, \"cplex\", FeasibilityTol =1e-9, IntFeasTol=1e-9, OptimalityTol=1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9fbe07af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.811</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DM_4CRSOL</th>\n",
       "      <td>0.000181</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_5DRIB</th>\n",
       "      <td>0.000187</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_AMOB</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_MTHTHF</th>\n",
       "      <td>0.001087</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ec_biomass_iJO1366_WT_53p95M</th>\n",
       "      <td>0.810997</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_250_trp-L_c</th>\n",
       "      <td>0.044795</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_251_tyr-L_c</th>\n",
       "      <td>0.108668</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_252_udcpdp_c</th>\n",
       "      <td>0.000045</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_253_utp_c</th>\n",
       "      <td>0.113622</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMPD_254_val-L_c</th>\n",
       "      <td>0.333469</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>599 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.811 at 0x7af5ae642890>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmodel.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69522ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_glc = \"DM_glc_e\"\n",
    "id_o2 = \"DM_o2_e\"\n",
    "id_biomass = \"Ec_biomass_iJO1366_WT_53p95M\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0f91a55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 69 DM_ reactions\n",
      "Processing grid point: x=0, y=0 (glucose=0, oxygen=0)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Processing grid point: x=0, y=1 (glucose=0, oxygen=5)\n",
      "  Optimization failed with status: infeasible\n",
      "Processing grid point: x=0, y=2 (glucose=0, oxygen=10)\n",
      "  Optimization failed with status: infeasible\n",
      "Processing grid point: x=1, y=0 (glucose=5, oxygen=0)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Processing grid point: x=1, y=1 (glucose=5, oxygen=5)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Processing grid point: x=1, y=2 (glucose=5, oxygen=10)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Processing grid point: x=2, y=0 (glucose=10, oxygen=0)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Processing grid point: x=2, y=1 (glucose=10, oxygen=5)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Processing grid point: x=2, y=2 (glucose=10, oxygen=10)\n",
      "  Performing FVA on 69 DM reactions...\n",
      "Grid point (0, 0): 0 forced, 0 optional\n",
      "Grid point (0, 1): 0 forced, 0 optional\n",
      "Grid point (0, 2): 0 forced, 0 optional\n",
      "Grid point (1, 0): 14 forced, 0 optional\n",
      "Grid point (1, 1): 14 forced, 1 optional\n",
      "Grid point (1, 2): 11 forced, 0 optional\n",
      "Grid point (2, 0): 14 forced, 0 optional\n",
      "Grid point (2, 1): 14 forced, 0 optional\n",
      "Grid point (2, 2): 14 forced, 1 optional\n",
      "\n",
      "=== VARIABILITY ANALYSIS RESULTS ===\n",
      "\n",
      "Glucose=0, Oxygen=0 (Growth rate: 0.0000)\n",
      "  Forced secretions (0): []\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=0, Oxygen=5 (Growth rate: 0.0000)\n",
      "  Forced secretions (0): []\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=0, Oxygen=10 (Growth rate: 0.0000)\n",
      "  Forced secretions (0): []\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=5, Oxygen=0 (Growth rate: 0.1366)\n",
      "  Forced secretions (14): ['DM_4CRSOL', 'DM_5DRIB', 'DM_AMOB', 'DM_MTHTHF', 'DM_5mtr_e', 'DM_ac_e', 'DM_ala-D_e', 'DM_alaala_e', 'DM_etoh_e', 'DM_for_e', 'DM_glyclt_e', 'DM_h_e', 'DM_meoh_e', 'DM_succ_e']\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=5, Oxygen=5 (Growth rate: 0.3681)\n",
      "  Forced secretions (14): ['DM_4CRSOL', 'DM_5DRIB', 'DM_AMOB', 'DM_MTHTHF', 'DM_5mtr_e', 'DM_ac_e', 'DM_ala-D_e', 'DM_alaala_e', 'DM_co2_e', 'DM_for_e', 'DM_glyclt_e', 'DM_h_e', 'DM_h2o_e', 'DM_meoh_e']\n",
      "  Optional secretions (1): ['DM_etoh_e']\n",
      "\n",
      "Glucose=5, Oxygen=10 (Growth rate: 0.4650)\n",
      "  Forced secretions (11): ['DM_4CRSOL', 'DM_5DRIB', 'DM_AMOB', 'DM_MTHTHF', 'DM_5mtr_e', 'DM_ala-D_e', 'DM_alaala_e', 'DM_co2_e', 'DM_h_e', 'DM_h2o_e', 'DM_meoh_e']\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=10, Oxygen=0 (Growth rate: 0.2732)\n",
      "  Forced secretions (14): ['DM_4CRSOL', 'DM_5DRIB', 'DM_AMOB', 'DM_MTHTHF', 'DM_5mtr_e', 'DM_ac_e', 'DM_ala-D_e', 'DM_alaala_e', 'DM_etoh_e', 'DM_for_e', 'DM_glyclt_e', 'DM_h_e', 'DM_meoh_e', 'DM_succ_e']\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=10, Oxygen=5 (Growth rate: 0.5204)\n",
      "  Forced secretions (14): ['DM_4CRSOL', 'DM_5DRIB', 'DM_AMOB', 'DM_MTHTHF', 'DM_5mtr_e', 'DM_ac_e', 'DM_ala-D_e', 'DM_alaala_e', 'DM_etoh_e', 'DM_for_e', 'DM_glyclt_e', 'DM_h_e', 'DM_h2o_e', 'DM_meoh_e']\n",
      "  Optional secretions (0): []\n",
      "\n",
      "Glucose=10, Oxygen=10 (Growth rate: 0.7362)\n",
      "  Forced secretions (14): ['DM_4CRSOL', 'DM_5DRIB', 'DM_AMOB', 'DM_MTHTHF', 'DM_5mtr_e', 'DM_ac_e', 'DM_ala-D_e', 'DM_alaala_e', 'DM_co2_e', 'DM_for_e', 'DM_glyclt_e', 'DM_h_e', 'DM_h2o_e', 'DM_meoh_e']\n",
      "  Optional secretions (1): ['DM_etoh_e']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analysis complete!\n",
      "Growth rate matrix shape: (3, 3)\n",
      "Variability matrices shape: (3, 3, 69), (3, 3, 69)\n",
      "Results saved as: phenotypic_phase_plane_variability.png\n"
     ]
    }
   ],
   "source": [
    "# Part 4: Phenotypic phase plane with variability\n",
    "\n",
    "# Create a list of all boundary reactions\n",
    "# Search for the prefix 'DM'\n",
    "index_list = []\n",
    "for i, rxn in enumerate(tmodel.reactions):\n",
    "    if 'DM_' in rxn.id:\n",
    "        index_list.append(i)\n",
    "\n",
    "print(f\"Found {len(index_list)} DM_ reactions\")\n",
    "\n",
    "# Create a grid of flux values between 0 and 10 with a step of 5\n",
    "x = np.arange(0, 11, 5)  # x for glucose: [0, 5, 10]\n",
    "y = np.arange(0, 11, 5)  # y for oxygen: [0, 5, 10]\n",
    "X, Y = np.meshgrid(x, y)\n",
    "\n",
    "# Allocate an array for the solution\n",
    "Z = np.zeros((len(x), len(y)))\n",
    "\n",
    "# Create two 3-dimensional matrices for the variability analysis for all\n",
    "# the boundary reactions and glucose-oxygen combinations\n",
    "VA_max = np.zeros((len(x), len(y), len(index_list)))\n",
    "VA_min = np.zeros((len(x), len(y), len(index_list)))\n",
    "\n",
    "# Store original bounds to restore them\n",
    "original_glc_lb = tmodel.reactions.get_by_id(id_glc).lower_bound\n",
    "original_glc_ub = tmodel.reactions.get_by_id(id_glc).upper_bound\n",
    "original_o2_lb = tmodel.reactions.get_by_id(id_o2).lower_bound\n",
    "original_o2_ub = tmodel.reactions.get_by_id(id_o2).upper_bound\n",
    "original_biomass_lb = tmodel.reactions.get_by_id(id_biomass).lower_bound\n",
    "\n",
    "# Store original objective\n",
    "original_objective = tmodel.objective\n",
    "\n",
    "# Loop over the grid\n",
    "for id_x in range(len(x)):\n",
    "    for id_y in range(len(y)):\n",
    "        \n",
    "        # Display the position on grid\n",
    "        print(f\"Processing grid point: x={id_x}, y={id_y} (glucose={x[id_x]}, oxygen={y[id_y]})\")\n",
    "        \n",
    "        # Get current glucose and oxygen values\n",
    "        glc_uptake = x[id_x]\n",
    "        o2_uptake = y[id_y]\n",
    "        \n",
    "        # Set the lower bound of biomass to 0\n",
    "        tmodel.reactions.get_by_id(id_biomass).lower_bound = 0\n",
    "        \n",
    "        # Calculate the optimal growth rate at a single point of \n",
    "        # oxygen and glucose uptake\n",
    "        if glc_uptake == 0:\n",
    "            tmodel.reactions.get_by_id(id_glc).bounds = (0, 0)\n",
    "\n",
    "        else:\n",
    "            tmodel.reactions.get_by_id(id_glc).bounds = (-glc_uptake, -glc_uptake)\n",
    "        \n",
    "        if o2_uptake == 0:\n",
    "            tmodel.reactions.get_by_id(id_o2).bounds = (0, 0)\n",
    "        else:\n",
    "            tmodel.reactions.get_by_id(id_o2).bounds = (-o2_uptake, -o2_uptake)\n",
    "\n",
    "        # Set objective to biomass\n",
    "        tmodel.objective = id_biomass\n",
    "        \n",
    "        # Solve for optimal growth\n",
    "        sol = tmodel.slim_optimize()\n",
    "\n",
    "        if not math.isnan(sol):\n",
    "\n",
    "            # Store solution in the Z matrix\n",
    "            Z[id_x, id_y] = sol\n",
    "            \n",
    "            # Variability analysis of the boundary reactions\n",
    "            \n",
    "            # Constrain the biomass reaction to the optimal value\n",
    "            tmodel.reactions.get_by_id(id_biomass).lower_bound = sol\n",
    "            \n",
    "            # Get the reaction IDs for DM reactions\n",
    "            dm_reaction_ids = [tmodel.reactions[idx].id for idx in index_list]\n",
    "            \n",
    "            # Perform flux variability analysis on DM reactions\n",
    "            print(f\"  Performing FVA on {len(dm_reaction_ids)} DM reactions...\")\n",
    "            \n",
    "            for j, rxn_idx in enumerate(index_list):\n",
    "                rxn_id = tmodel.reactions[rxn_idx].id\n",
    "                \n",
    "                # Set objective to current DM reaction\n",
    "                tmodel.objective = rxn_id\n",
    "                \n",
    "                # Perform maximization\n",
    "                tmodel.objective.direction = 'max'\n",
    "                sol_max = tmodel.optimize()\n",
    "                max_value = sol_max.objective_value if sol_max.status == 'optimal' else 0\n",
    "                \n",
    "                # Perform minimization\n",
    "                tmodel.objective.direction = 'min'\n",
    "                sol_min = tmodel.optimize()\n",
    "                min_value = sol_min.objective_value if sol_min.status == 'optimal' else 0\n",
    "                \n",
    "                # Store the values\n",
    "                VA_max[id_x, id_y, j] = max_value\n",
    "                VA_min[id_x, id_y, j] = min_value\n",
    "        else:\n",
    "            print(f\"  Optimization failed with status: {tmodel.solver.status}\")\n",
    "            Z[id_x, id_y] = 0\n",
    "            # Set all FVA values to 0 for failed optimization\n",
    "            VA_max[id_x, id_y, :] = 0\n",
    "            VA_min[id_x, id_y, :] = 0\n",
    "        \n",
    "        # Restore bounds for next iteration\n",
    "        tmodel.reactions.get_by_id(id_glc).bounds = (original_glc_lb, original_glc_ub)\n",
    "        tmodel.reactions.get_by_id(id_o2).bounds = (original_o2_lb, original_o2_ub)\n",
    "        tmodel.reactions.get_by_id(id_biomass).lower_bound = original_biomass_lb\n",
    "        tmodel.objective = original_objective\n",
    "\n",
    "# Based on the previous results, identify which metabolites must\n",
    "# be secreted in each set of oxygen and glucose and which ones can either be\n",
    "# secreted or not\n",
    "\n",
    "# Initialize cell-like structure using nested lists\n",
    "Rxn_forced = [[[] for _ in range(len(y))] for _ in range(len(x))]\n",
    "Rxn_can = [[[] for _ in range(len(y))] for _ in range(len(x))]\n",
    "\n",
    "for id_x in range(len(x)):\n",
    "    for id_y in range(len(y)):\n",
    "        \n",
    "        # Find the reaction indices for the metabolites that must be secreted\n",
    "        # (minimum flux > threshold)\n",
    "        rxn_idx_forced = np.where(VA_min[id_x, id_y, :] > 1e-9)[0]\n",
    "        \n",
    "        # Find the reaction indices for the metabolites that can possibly be secreted\n",
    "        # (minimum = 0 and maximum > 0)\n",
    "        condition_min_zero = VA_min[id_x, id_y, :] == 0\n",
    "        condition_max_positive = VA_max[id_x, id_y, :] > 0\n",
    "        rxn_idx_can = np.where(condition_min_zero & condition_max_positive)[0]\n",
    "        \n",
    "        # Store the results - convert indices back to reaction names\n",
    "        Rxn_forced[id_x][id_y] = [tmodel.reactions[index_list[idx]].id for idx in rxn_idx_forced]\n",
    "        Rxn_can[id_x][id_y] = [tmodel.reactions[index_list[idx]].id for idx in rxn_idx_can]\n",
    "        \n",
    "        print(f\"Grid point ({id_x}, {id_y}): {len(Rxn_forced[id_x][id_y])} forced, {len(Rxn_can[id_x][id_y])} optional\")\n",
    "\n",
    "# Print summary results\n",
    "print(\"\\n=== VARIABILITY ANALYSIS RESULTS ===\")\n",
    "for id_x in range(len(x)):\n",
    "    for id_y in range(len(y)):\n",
    "        print(f\"\\nGlucose={x[id_x]}, Oxygen={y[id_y]} (Growth rate: {Z[id_x, id_y]:.4f})\")\n",
    "        print(f\"  Forced secretions ({len(Rxn_forced[id_x][id_y])}): {Rxn_forced[id_x][id_y]}\")\n",
    "        print(f\"  Optional secretions ({len(Rxn_can[id_x][id_y])}): {Rxn_can[id_x][id_y][:5]}{'...' if len(Rxn_can[id_x][id_y]) > 5 else ''}\")\n",
    "\n",
    "# Plot the growth rate surface\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Create surface plot\n",
    "surf = ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.8)\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel('Glucose uptake [mmol/gDW/hr]')\n",
    "ax.set_ylabel('Oxygen uptake [mmol/gDW/hr]')\n",
    "ax.set_zlabel('Growth rate [1/hr]')\n",
    "ax.set_title('Phenotypic Phase Plane with Variability Analysis')\n",
    "\n",
    "# Add colorbar\n",
    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('phenotypic_phase_plane_variability.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nAnalysis complete!\")\n",
    "print(f\"Growth rate matrix shape: {Z.shape}\")\n",
    "print(f\"Variability matrices shape: {VA_max.shape}, {VA_min.shape}\")\n",
    "print(f\"Results saved as: phenotypic_phase_plane_variability.png\")"
   ]
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