banana-oil-sucrose.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f9f1753-3b9e-4fc6-bf41-644afdcdafdc",
   "metadata": {},
   "source": [
    "![](banana-oil-500x500.webp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac8dccf1",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.27.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cobra\n",
    "cobra.__version__  # '0.27.0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9715eff6",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "# load_json_model, load_matlab_model, read_sbml_model\n",
    "model = cobra.io.read_sbml_model(\"yeast-GEM-8.7.0/model/yeast-GEM.xml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85fdff2a-8bf8-49b4-beb5-2b96b4c104b0",
   "metadata": {},
   "source": [
    "![](yeastGEMgithub.png)\n",
    "\n",
    "[yeast-GEM](https://github.com/SysBioChalmers/yeast-GEM#yeast-gem-the-consensus-genome-scale-metabolic-model-of-saccharomyces-cerevisiae)\n",
    "\n",
    "Downloaded [zip file](https://github.com/SysBioChalmers/yeast-GEM/releases/tag/v8.7.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17c61cb9-9274-4aab-b8e5-2323dc0dc455",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4131\n"
     ]
    }
   ],
   "source": [
    "print(len(model.reactions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "680830c2-bd60-499a-84b4-d5c7da0c133a",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2806\n"
     ]
    }
   ],
   "source": [
    "print(len(model.metabolites))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17449281-75d3-43a4-9f24-e967142ac4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "medium = model.medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "606e6f53-c314-4632-ac44-d0b04767bf03",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_1654 ammonium exchange  =  1000.0\n",
      "r_1714 D-glucose exchange  =  1.0\n",
      "r_1832 H+ exchange  =  1000.0\n",
      "r_1861 iron(2+) exchange  =  1000.0\n",
      "r_1992 oxygen exchange  =  1000.0\n",
      "r_2005 phosphate exchange  =  1000.0\n",
      "r_2020 potassium exchange  =  1000.0\n",
      "r_2049 sodium exchange  =  1000.0\n",
      "r_2060 sulphate exchange  =  1000.0\n",
      "r_2100 water exchange  =  1000.0\n",
      "r_4593 chloride exchange  =  1000.0\n",
      "r_4594 Cu2(+) exchange  =  1000.0\n",
      "r_4595 Mn(2+) exchange  =  1000.0\n",
      "r_4596 Zn(2+) exchange  =  1000.0\n",
      "r_4597 Mg(2+) exchange  =  1000.0\n",
      "r_4600 Ca(2+) exchange  =  1000.0\n"
     ]
    }
   ],
   "source": [
    "for key in medium:\n",
    "    print(key, model.reactions.get_by_id(key).name, \" = \" ,medium[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59b2d80d-3592-4fbd-922b-6549bb222bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "del medium[\"r_1714\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de4c7790-1c01-497e-9a41-82536762f81a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_1024 sucrose hydrolyzing enxyme frozenset({<Gene YIL162W at 0x7fe9cb3ab0d0>})\n",
      "r_2058 sucrose exchange frozenset()\n",
      "r_4317 sucrose glucohydrolase frozenset({<Gene YOL157C at 0x7fe9cb201690>})\n"
     ]
    }
   ],
   "source": [
    "for i, r in enumerate(model.reactions):\n",
    "    if \"sucrose\" in r.name.lower():\n",
    "        print(r.id, r.name, r.genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7f0844-0ecf-48de-98de-935d4edb3990",
   "metadata": {},
   "source": [
    "- r_2058 sucrose exchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "771276b6-50f7-4f84-9b9c-deea4d3a3448",
   "metadata": {},
   "outputs": [],
   "source": [
    "medium[\"r_2058\"] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76c0f3e0-28e9-4289-9990-2b1e0da36ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_1654 ammonium exchange  =  1000.0\n",
      "r_1832 H+ exchange  =  1000.0\n",
      "r_1861 iron(2+) exchange  =  1000.0\n",
      "r_1992 oxygen exchange  =  1000.0\n",
      "r_2005 phosphate exchange  =  1000.0\n",
      "r_2020 potassium exchange  =  1000.0\n",
      "r_2049 sodium exchange  =  1000.0\n",
      "r_2060 sulphate exchange  =  1000.0\n",
      "r_2100 water exchange  =  1000.0\n",
      "r_4593 chloride exchange  =  1000.0\n",
      "r_4594 Cu2(+) exchange  =  1000.0\n",
      "r_4595 Mn(2+) exchange  =  1000.0\n",
      "r_4596 Zn(2+) exchange  =  1000.0\n",
      "r_4597 Mg(2+) exchange  =  1000.0\n",
      "r_4600 Ca(2+) exchange  =  1000.0\n",
      "r_2058 sucrose exchange  =  1.0\n"
     ]
    }
   ],
   "source": [
    "for key in medium:\n",
    "    print(key, model.reactions.get_by_id(key).name, \" = \" , medium[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21959331-a0b4-446b-a531-464656d8f547",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.medium = medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b1867b7",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_0160 alcohol acetyltransferase (isoamyl alcohol) frozenset({<Gene YOR377W at 0x7fe9cb20f010>, <Gene YGR177C at 0x7fe9cb3966d0>})\n",
      "r_0179 aldehyde dehydrogenase (isoamyl alcohol, NAD) frozenset({<Gene YOL086C at 0x7fe9cb200510>, <Gene YDL168W at 0x7fe9cb35b010>, <Gene YBR145W at 0x7fe9cb34f290>})\n",
      "r_0180 aldehyde dehydrogenase (isoamyl alcohol, NAD) frozenset({<Gene YGL256W at 0x7fe9cb38f710>, <Gene YMR083W at 0x7fe9cb3e1e10>})\n",
      "r_0181 aldehyde dehydrogenase (isoamyl alcohol, NADP) frozenset({<Gene YCR105W at 0x7fe9cb358f90>, <Gene YMR318C at 0x7fe9cb3f17d0>})\n",
      "r_0656 isoamyl acetate-hydrolyzing esterase frozenset({<Gene YOR126C at 0x7fe9cb202e50>})\n",
      "r_1180 isoamyl acetate transport frozenset()\n",
      "r_1862 isoamyl acetate exchange frozenset()\n",
      "r_1863 isoamyl alcohol transport frozenset()\n",
      "r_1864 isoamyl alcohol transport frozenset()\n",
      "r_1865 isoamylol exchange frozenset()\n"
     ]
    }
   ],
   "source": [
    "for i, r in enumerate(model.reactions):\n",
    "    if \"isoamyl\" in r.name.lower():\n",
    "        print(r.id, r.name, r.genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da12f5c6-d116-4791-a03b-f135033d7aab",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "- r_1862 isoamyl acetate exchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a93de3a-1f01-42d4-9769-73669ee90c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Reaction identifier</strong></td><td>r_1862</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>isoamyl acetate exchange</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x7fe9ca9642d0</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Stoichiometry</strong></td>\n",
       "                <td>\n",
       "                    <p style='text-align:right'>s_0928 --></p>\n",
       "                    <p style='text-align:right'>isoamyl acetate --></p>\n",
       "                </td>\n",
       "            </tr><tr>\n",
       "                <td><strong>GPR</strong></td><td></td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Lower bound</strong></td><td>-0.0</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Upper bound</strong></td><td>1000.0</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<Reaction r_1862 at 0x7fe9ca9642d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = model.reactions.get_by_id(\"r_1862\")\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "823bc0a5-031e-4cd6-b41b-3b510ab3b52c",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximize\n",
      "1.0*r_2111 - 1.0*r_2111_reverse_58b69\n"
     ]
    }
   ],
   "source": [
    "# We can investigate the model objective\n",
    "print(model.objective) # biomass?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9ad09e9-addb-4574-aa7a-d95e7ed38806",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "growth\n"
     ]
    }
   ],
   "source": [
    "objr = model.reactions.get_by_id(\"r_2111\")\n",
    "print(objr.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "783e5234",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "model.objective = r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7cb3abfd-6695-462b-a9de-7657e2c215c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximize\n",
      "1.0*r_1862 - 1.0*r_1862_reverse_0b3f6\n"
     ]
    }
   ],
   "source": [
    "print(model.objective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2bddd974-163f-48d2-a555-8197b708b167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimise the model using fba\n",
    "fba_solution = model.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81865c6c-10df-4b6e-af56-9e65084fd3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'optimal'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fba_solution.status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13e56e54-9f09-4dd6-901a-11bec62f64cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 1.124</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>r_0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.724587e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.734723e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_0005</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_4774</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_4775</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.035400e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_4776</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_4777</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_4778</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 1.124 at 0x7fe9ca84c390>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fba_solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5dbc5eb1-4655-4862-a789-6222d0d0fa48",
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ab7785d-3e96-4600-8e55-82c764a5f606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Objective</h3><p>1.0 r_1862 = 1.1236785703192131</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metabolite</th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Flux</th>\n",
       "      <th>C-Number</th>\n",
       "      <th>C-Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>s_1277</td>\n",
       "      <td>r_1992</td>\n",
       "      <td>1.325</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_1466</td>\n",
       "      <td>r_2058</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metabolite</th>\n",
       "      <th>Reaction</th>\n",
       "      <th>Flux</th>\n",
       "      <th>C-Number</th>\n",
       "      <th>C-Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>s_0458</td>\n",
       "      <td>r_1672</td>\n",
       "      <td>-4.134</td>\n",
       "      <td>1</td>\n",
       "      <td>34.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_0928</td>\n",
       "      <td>r_1862</td>\n",
       "      <td>-1.124</td>\n",
       "      <td>7</td>\n",
       "      <td>65.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_0805</td>\n",
       "      <td>r_2100</td>\n",
       "      <td>-3.134</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<cobra.summary.model_summary.ModelSummary at 0x7fe9cb044b90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "30797898-f419-48c8-ac38-89957f884203",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s_1277 oxygen\n",
      "s_1466 sucrose\n"
     ]
    }
   ],
   "source": [
    "for id in sm.uptake_flux['metabolite'].values:\n",
    "    print(id, model.metabolites.get_by_id(id).name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e5c67398-afde-41cb-a561-ec6ad74162a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_1672 carbon dioxide\n",
      "r_1862 isoamyl acetate\n",
      "r_2100 H2O\n"
     ]
    }
   ],
   "source": [
    "for id, row in sm.secretion_flux.iterrows():\n",
    "    if row[\"flux\"] != 0.0:\n",
    "        print(id, model.metabolites.get_by_id(row[\"metabolite\"]).name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b26dd29b-09d8-44e2-a03f-e50413914554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fba_solution.get_primal_by_id(\"r_2111\") # Growth!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2578ce39-b1d4-4d99-98b0-6828f65b4a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "molar_yeld = (-1. * fba_solution.get_primal_by_id(\"r_1862\")/fba_solution.get_primal_by_id(\"r_2058\") )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eede0666-eee8-4152-a23e-5f70b7761336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1236785703192156\n"
     ]
    }
   ],
   "source": [
    "print(molar_yeld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "baaff831-1511-4ad5-9784-ab5a5199206c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342.29648"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_suc = model.metabolites.get_by_id(\"s_1466\").formula_weight\n",
    "mw_suc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fb11df1-b381-4466-8d6c-bf52203c5863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.18486"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_banana_oil  = model.metabolites.get_by_id(\"s_0928\").formula_weight\n",
    "mw_banana_oil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ec0cd64b-69d4-4a8e-a582-4aadf4252778",
   "metadata": {},
   "outputs": [],
   "source": [
    "mass_yield = molar_yeld  * (mw_banana_oil/mw_suc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e08bdb3-cc93-4959-9d80-173071cd93d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4273661749662375 g/g\n"
     ]
    }
   ],
   "source": [
    "print( str(mass_yield) +\" g/g\")"
   ]
  }
 ],
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