examples/05-GOUproblem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEWpy example\n",
    "\n",
    "Increasing the production of succinate in E.coli by modifying genes expression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by loading the Genome-scale Metabolic Model using REFRAMED or COBRApy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cobra.io.sbml import read_sbml_model\n",
    "\n",
    "model = read_sbml_model('models/ec/e_coli_core.xml.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and import the required modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mewpy.optimization.evaluation import BPCY,WYIELD\n",
    "from mewpy.problems import GOUProblem\n",
    "from mewpy.simulation import get_simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may have a quick look into the reactions included in the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>lb</th>\n",
       "      <th>ub</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BIOMASS_Ecoli_core_w_GAM</th>\n",
       "      <td>Biomass Objective Function with GAM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_glc__D_e</th>\n",
       "      <td>D-Glucose exchange</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_o2_e</th>\n",
       "      <td>O2 exchange</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_succ_e</th>\n",
       "      <td>Succinate exchange</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         name      lb      ub\n",
       "id                                                                           \n",
       "BIOMASS_Ecoli_core_w_GAM  Biomass Objective Function with GAM     0.0  1000.0\n",
       "EX_glc__D_e                                D-Glucose exchange   -10.0  1000.0\n",
       "EX_o2_e                                           O2 exchange -1000.0  1000.0\n",
       "EX_succ_e                                  Succinate exchange     0.0  1000.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim = get_simulator(model)\n",
    "sim.find(['BIOMASS','EX_o2_','EX_glc_','EX_succ_']).drop(columns=['stoichiometry','gpr','annotations'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now define the optimization problem by defining the medium and objective functions, BPCY and WYIELD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "BIOMASS_ID = 'BIOMASS_Ecoli_core_w_GAM'\n",
    "O2 = 'EX_o2_e'\n",
    "GLC = 'EX_glc__D_e'\n",
    "PRODUCT_ID = 'EX_succ_e'\n",
    "sim.objective = BIOMASS_ID\n",
    "\n",
    "evaluator_1 = BPCY(BIOMASS_ID, PRODUCT_ID, method = 'lMOMA')\n",
    "evaluator_2 = WYIELD(BIOMASS_ID,PRODUCT_ID)\n",
    "\n",
    "medium = {GLC:(-10,1000),O2:(-20,1000)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We instantiate the problem, a gene over/under expression problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = GOUProblem(model,\n",
    "                     [evaluator_1,evaluator_2],\n",
    "                     envcond=medium,\n",
    "                     candidate_min_size=2,\n",
    "                     candidate_max_size=6\n",
    "                     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An EA optimizer is instantiated for the defined optimization problem and ran. The `max_generations` value depends on the difficulty and size of the solution space. For illustrative purposes, we only will consider 10 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running NSGAII\n",
      "Eval(s)|     Worst      Best    Median   Average   Std Dev|     Worst      Best    Median   Average   Std Dev|\n",
      "    100|  0.000000  0.000000  0.000000  0.000000  0.000000|  0.000000  4.176218  0.027958  0.085566  0.413395|\n",
      "    200|  0.000000  0.000000  0.000000  0.000000  0.000000|  0.023941  4.176218  0.091307  0.167364  0.575745|\n",
      "    300|  0.000000  0.000000  0.000000  0.000000  0.000000|  0.103086  4.361139  0.103086  0.325617  0.896205|\n",
      "    400|  0.000000  0.000000  0.000000  0.000000  0.000000|  0.103086  4.361139  0.105533  0.737138  1.439676|\n",
      "    500|  0.000000  0.000000  0.000000  0.000000  0.000000|  0.112671  4.361139  0.137993  1.769197  1.956861|\n",
      "    600|  0.000000  2.502324  0.000000  0.025023  0.248978|  2.660304  7.365741  4.176218  4.198621  0.427755|\n",
      "    700|  0.000000  2.502324  0.000000  0.100093  0.490353|  4.176218  7.365741  4.361139  4.409516  0.609786|\n",
      "    800|  0.000000  2.502324  0.000000  0.250232  0.750697|  4.361139  7.365741  4.361139  4.661599  0.901380|\n",
      "    900|  0.000000  2.502324  0.000000  0.625581  1.083538|  4.361139  7.365741  4.361139  5.112290  1.301031|\n",
      "   1000|  0.000000  2.502324  2.502324  1.644012  1.175972|  4.361139  8.082702  7.365741  6.387538  1.425185|\n",
      "   1100|  0.749913  2.502324  2.502324  2.451284  0.285434|  7.365741  8.082702  7.365741  7.388591  0.118723|\n",
      "   1200|  0.749913  2.502324  2.502324  2.361722  0.466952|  7.365741  8.082702  7.365741  7.429170  0.193429|\n",
      "   1300|  0.749913  2.502324  2.502324  2.146169  0.687087|  7.365741  8.082702  7.365741  7.533107  0.279686|\n",
      "   1400|  0.749913  2.502324  2.405245  1.832316  0.815924|  7.365741  8.082702  7.602300  7.698912  0.303129|\n",
      "   1500|  0.749913  2.502324  2.405245  1.606812  0.824024|  7.365741  8.082702  7.602300  7.824922  0.249036|\n",
      "   1600|  0.749913  2.502324  2.405245  2.059409  0.670002|  7.365741  8.248792  7.602300  7.688754  0.210866|\n",
      "   1700|  0.774104  2.502324  2.405245  2.353489  0.279972|  7.365741  8.248792  7.602300  7.611358  0.116095|\n",
      "   1800|  0.774104  2.502324  2.405245  2.321599  0.347644|  7.365741  8.248792  7.602300  7.610562  0.149040|\n",
      "   1900|  0.774104  2.502324  2.405245  2.247317  0.473248|  7.365741  8.248792  7.602300  7.609213  0.216220|\n",
      "   2000|  0.774104  2.870729  2.405245  2.090115  0.605827|  5.709827  8.248792  7.602300  7.600257  0.350192|\n"
     ]
    }
   ],
   "source": [
    "from mewpy.optimization import EA\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "ea = EA(problem, max_generations= 20)\n",
    "final_pop = ea.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modification</th>\n",
       "      <th>Size</th>\n",
       "      <th>BPCY</th>\n",
       "      <th>WYIELD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'b4090': 0.03125, 'b3236': 2, 'b2914': 0, 'b0...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.774104</td>\n",
       "      <td>8.248792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0.03125}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.502324</td>\n",
       "      <td>7.365741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'b3236': 2, 'b3732': 0, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.476403</td>\n",
       "      <td>7.617307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'b1136': 0.03125, 'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.032442</td>\n",
       "      <td>8.146401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.405245</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'b1136': 0.03125, 'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.032442</td>\n",
       "      <td>8.146401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'b3236': 2, 'b1603': 0, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>2.024576</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0.25}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.870729</td>\n",
       "      <td>5.709827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.405245</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0.125}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.726887</td>\n",
       "      <td>6.656063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>{'b1136': 0.03125, 'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.032442</td>\n",
       "      <td>8.146401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'b3236': 2, 'b3732': 0, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.476403</td>\n",
       "      <td>7.617307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.405245</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>{'b3236': 2, 'b1603': 0, 'b0721': 0}</td>\n",
       "      <td>3</td>\n",
       "      <td>2.024576</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.405245</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>{'b3236': 2, 'b0721': 0}</td>\n",
       "      <td>2</td>\n",
       "      <td>2.405245</td>\n",
       "      <td>7.602300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Modification  Size      BPCY  \\\n",
       "0   {'b4090': 0.03125, 'b3236': 2, 'b2914': 0, 'b0...     4  0.774104   \n",
       "1                      {'b3236': 2, 'b0721': 0.03125}     2  2.502324   \n",
       "2                {'b3236': 2, 'b3732': 0, 'b0721': 0}     3  1.476403   \n",
       "3          {'b1136': 0.03125, 'b3236': 2, 'b0721': 0}     3  1.032442   \n",
       "4                            {'b3236': 2, 'b0721': 0}     2  2.405245   \n",
       "5          {'b1136': 0.03125, 'b3236': 2, 'b0721': 0}     3  1.032442   \n",
       "6                {'b3236': 2, 'b1603': 0, 'b0721': 0}     3  2.024576   \n",
       "7                         {'b3236': 2, 'b0721': 0.25}     2  2.870729   \n",
       "8                            {'b3236': 2, 'b0721': 0}     2  2.405245   \n",
       "9                        {'b3236': 2, 'b0721': 0.125}     2  2.726887   \n",
       "10         {'b1136': 0.03125, 'b3236': 2, 'b0721': 0}     3  1.032442   \n",
       "11               {'b3236': 2, 'b3732': 0, 'b0721': 0}     3  1.476403   \n",
       "12                           {'b3236': 2, 'b0721': 0}     2  2.405245   \n",
       "13               {'b3236': 2, 'b1603': 0, 'b0721': 0}     3  2.024576   \n",
       "14                           {'b3236': 2, 'b0721': 0}     2  2.405245   \n",
       "15                           {'b3236': 2, 'b0721': 0}     2  2.405245   \n",
       "\n",
       "      WYIELD  \n",
       "0   8.248792  \n",
       "1   7.365741  \n",
       "2   7.617307  \n",
       "3   8.146401  \n",
       "4   7.602300  \n",
       "5   8.146401  \n",
       "6   7.602300  \n",
       "7   5.709827  \n",
       "8   7.602300  \n",
       "9   6.656063  \n",
       "10  8.146401  \n",
       "11  7.617307  \n",
       "12  7.602300  \n",
       "13  7.602300  \n",
       "14  7.602300  \n",
       "15  7.602300  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ea.dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ea.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b4090': 0.03125, 'b3236': 2, 'b2914': 0, 'b0721': 0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pop[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Flux rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reaction ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BIOMASS_Ecoli_core_w_GAM</th>\n",
       "      <td>0.101954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_succ_e</th>\n",
       "      <td>7.592670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Flux rate\n",
       "Reaction ID                        \n",
       "BIOMASS_Ecoli_core_w_GAM   0.101954\n",
       "EX_succ_e                  7.592670"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = problem.simulate(solution=final_pop[0].values,method='pFBA')\n",
    "res.find([PRODUCT_ID,BIOMASS_ID])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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