06-Gene-essentiality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gene essentiality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "from pandas import DataFrame\n",
    "from cobra.io import read_sbml_model\n",
    "%matplotlib inline\n",
    "model = read_sbml_model('data/e_coli_core.xml.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Knocking out a single gene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the model, exactly one gene is associated with reaction with [phosphoglycerate kinase](http://bigg.ucsd.edu/universal/reactions/PGK) (PGK)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frozenset({<Gene b2926 at 0x11ac145c0>})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.PGK.genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the name of this gene in the model (the id is the Blattner number b2926), reveals that it is indeed [pgk](http://ecocyc.org/gene?orgid=ECOLI&id=EG10703)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pgk'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.genes.b2926.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is [pgk](http://ecocyc.org/gene?orgid=ECOLI&id=EG10703) essential? Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "with model as pgk_mutant:\n",
    "    pgk_mutant.genes.b2926.knock_out()\n",
    "    print(pgk_mutant.slim_optimize())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, it is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform all possible single gene deletions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a `for` loop allows to iterate over all genes in the model and knock out each one of them individually. We're using the model as a context to undo every gene deletion before proceeding to the next knockout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "essentiality = {}\n",
    "for gene in model.genes:\n",
    "    with model:\n",
    "        gene.knock_out()\n",
    "        essentiality[gene] = model.slim_optimize(error_value=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{<Gene b0351 at 0x11abe88d0>: 0.8739215069684303,\n",
       " <Gene b1241 at 0x11abe8978>: 0.8739215069684303,\n",
       " <Gene s0001 at 0x11abe8ba8>: 0.21114065257211811,\n",
       " <Gene b1849 at 0x11abe8dd8>: 0.8739215069684366,\n",
       " <Gene b3115 at 0x11abe8e80>: 0.8739215069684366,\n",
       " <Gene b2296 at 0x11abed0f0>: 0.8739215069684366,\n",
       " <Gene b1276 at 0x11abed320>: 0.8739215069684366,\n",
       " <Gene b0118 at 0x11abed550>: 0.8739215069684366,\n",
       " <Gene b0474 at 0x11abed780>: 0.8739215069684366,\n",
       " <Gene b0116 at 0x11abed9b0>: 0.7823510529477373,\n",
       " <Gene b0726 at 0x11abedbe0>: 0.8583074080226895,\n",
       " <Gene b0727 at 0x11abede10>: 0.8583074080226895,\n",
       " <Gene b2587 at 0x11abef080>: 0.8739215069684314,\n",
       " <Gene b0356 at 0x11abef2b0>: 0.8739215069684314,\n",
       " <Gene b1478 at 0x11abef4e0>: 0.8739215069684314,\n",
       " <Gene b3731 at 0x11abef710>: 0.3742298749331111,\n",
       " <Gene b3733 at 0x11abef940>: 0.3742298749331111,\n",
       " <Gene b3735 at 0x11abefb70>: 0.3742298749331111,\n",
       " <Gene b3739 at 0x11abefda0>: 0.8739215069684335,\n",
       " <Gene b3732 at 0x11abeffd0>: 0.3742298749331108,\n",
       " <Gene b3737 at 0x11abf3240>: 0.3742298749331108,\n",
       " <Gene b3736 at 0x11abf3470>: 0.3742298749331108,\n",
       " <Gene b3738 at 0x11abf36a0>: 0.3742298749331108,\n",
       " <Gene b3734 at 0x11abf38d0>: 0.3742298749331108,\n",
       " <Gene b0720 at 0x11abf3b00>: 0.0,\n",
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       " <Gene b3603 at 0x11abf5860>: 0.8739215069684316,\n",
       " <Gene b2779 at 0x11abf5a90>: 5.597179969023558e-18,\n",
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       " <Gene b4152 at 0x11abfb0f0>: 0.8739215069684334,\n",
       " <Gene b4153 at 0x11abfb320>: 0.8739215069684334,\n",
       " <Gene b1817 at 0x11abfb550>: 0.8739215069684334,\n",
       " <Gene b2415 at 0x11abfb780>: 0.0,\n",
       " <Gene b1818 at 0x11abfb9b0>: 0.8739215069684315,\n",
       " <Gene b1819 at 0x11abfbbe0>: 0.8739215069684315,\n",
       " <Gene b2416 at 0x11abfbe10>: 0.0,\n",
       " <Gene b4122 at 0x11abfd080>: 0.8739215069684307,\n",
       " <Gene b1612 at 0x11abfd2b0>: 0.8739215069684307,\n",
       " <Gene b1611 at 0x11abfd4e0>: 0.8739215069684307,\n",
       " <Gene b3528 at 0x11abfd710>: 0.8739215069684307,\n",
       " <Gene b1852 at 0x11abfd940>: 0.8638133095040009,\n",
       " <Gene b1779 at 0x11abfdb70>: 1.3397418963809056e-16,\n",
       " <Gene b1101 at 0x11abfdda0>: 0.8739215069684306,\n",
       " <Gene b1621 at 0x11abfdfd0>: 0.8739215069684306,\n",
       " <Gene b2417 at 0x11abff240>: 0.8739215069684306,\n",
       " <Gene b3870 at 0x11abff470>: 0.8739215069684306,\n",
       " <Gene b1297 at 0x11abff6a0>: 0.8739215069684306,\n",
       " <Gene b0810 at 0x11abff8d0>: 0.8739215069684306,\n",
       " <Gene b0809 at 0x11abffb00>: 0.8739215069684306,\n",
       " <Gene b0811 at 0x11abffd30>: 0.8739215069684306,\n",
       " <Gene b1761 at 0x11abfff60>: 0.8513885233462078,\n",
       " <Gene b1524 at 0x11ac031d0>: 0.8739215069684304,\n",
       " <Gene b1812 at 0x11ac03400>: 0.8739215069684304,\n",
       " <Gene b0485 at 0x11ac03630>: 0.8739215069684304,\n",
       " <Gene b3213 at 0x11ac03860>: 0.8739215069684304,\n",
       " <Gene b3212 at 0x11ac03a90>: 0.8739215069684304,\n",
       " <Gene b4077 at 0x11ac03cc0>: 0.8739215069684304,\n",
       " <Gene b2029 at 0x11ac03ef0>: 0.8638133095039995,\n",
       " <Gene b0875 at 0x11ac06160>: 0.8739215069684302,\n",
       " <Gene b1136 at 0x11ac06390>: 1.4099155226041516e-15,\n",
       " <Gene b4015 at 0x11ac065c0>: 0.8739215069684301,\n",
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       " <Gene b1380 at 0x11ac06a20>: 0.8739215069684301,\n",
       " <Gene b4014 at 0x11ac06c50>: 0.8739215069684301,\n",
       " <Gene b2976 at 0x11ac06e80>: 0.8739215069684301,\n",
       " <Gene b3236 at 0x11ac090f0>: 0.8258192905969888,\n",
       " <Gene b1479 at 0x11ac09320>: 0.8739215069684304,\n",
       " <Gene b2463 at 0x11ac09550>: 0.8739215069684304,\n",
       " <Gene b2279 at 0x11ac09780>: 0.21166294973531072,\n",
       " <Gene b2282 at 0x11ac099b0>: 0.21166294973531072,\n",
       " <Gene b2284 at 0x11ac09be0>: 0.21166294973531072,\n",
       " <Gene b2287 at 0x11ac09e10>: 0.21166294973531072,\n",
       " <Gene b2276 at 0x11ac0c080>: 0.21166294973531072,\n",
       " <Gene b2278 at 0x11ac0c2b0>: 0.21166294973531072,\n",
       " <Gene b2288 at 0x11ac0c4e0>: 0.21166294973531072,\n",
       " <Gene b2285 at 0x11ac0c710>: 0.21166294973531072,\n",
       " <Gene b2280 at 0x11ac0c940>: 0.21166294973531072,\n",
       " <Gene b2281 at 0x11ac0cb70>: 0.21166294973531072,\n",
       " <Gene b2283 at 0x11ac0cda0>: 0.21166294973531072,\n",
       " <Gene b2286 at 0x11ac0cfd0>: 0.21166294973531072,\n",
       " <Gene b2277 at 0x11ac0e240>: 0.21166294973531072,\n",
       " <Gene b1602 at 0x11ac0e470>: 0.8739215069684289,\n",
       " <Gene b3962 at 0x11ac0e6a0>: 0.8739215069684289,\n",
       " <Gene b1603 at 0x11ac0e8d0>: 0.8739215069684289,\n",
       " <Gene b0451 at 0x11ac0eb00>: 0.8739215069684289,\n",
       " <Gene b0114 at 0x11ac0ed30>: 0.7966959254309577,\n",
       " <Gene b0115 at 0x11ac0ef60>: 0.7966959254309577,\n",
       " <Gene b3916 at 0x11ac111d0>: 0.8739215069684318,\n",
       " <Gene b1723 at 0x11ac11400>: 0.8739215069684318,\n",
       " <Gene b3114 at 0x11ac11630>: 0.8739215069684318,\n",
       " <Gene b3951 at 0x11ac11860>: 0.8739215069684318,\n",
       " <Gene b0902 at 0x11ac11a90>: 0.8739215069684318,\n",
       " <Gene b2579 at 0x11ac11cc0>: 0.8739215069684318,\n",
       " <Gene b0903 at 0x11ac11ef0>: 0.8739215069684318,\n",
       " <Gene b3952 at 0x11ac14160>: 0.8739215069684318,\n",
       " <Gene b4025 at 0x11ac14390>: 0.8631595522084197,\n",
       " <Gene b2926 at 0x11ac145c0>: 1.1746245567390042e-17,\n",
       " <Gene b0767 at 0x11ac147f0>: 0.8638133095040007,\n",
       " <Gene b3612 at 0x11ac14a20>: 0.8739215069684331,\n",
       " <Gene b4395 at 0x11ac14c50>: 0.8739215069684331,\n",
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       " <Gene b4090 at 0x11ac1ab70>: 0.8739215069684327,\n",
       " <Gene b0722 at 0x11ac1ada0>: 0.8142975075325386,\n",
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       " <Gene b0729 at 0x11ac1d6a0>: 0.8583074080226918,\n",
       " <Gene b0728 at 0x11ac1d8d0>: 0.8583074080226918,\n",
       " <Gene b0008 at 0x11ac1db00>: 0.8739215069684326,\n",
       " <Gene b2464 at 0x11ac1dd30>: 0.8739215069684326,\n",
       " <Gene b2935 at 0x11ac1df60>: 0.8739215069684326,\n",
       " <Gene b2465 at 0x11ac201d0>: 0.8739215069684326,\n",
       " <Gene b3919 at 0x11ac20400>: 0.7040369478590276}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "essentiality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame.from_dict(essentiality, orient='index')\n",
    "df = df.rename(columns={0: 'growth'})\n",
    "df = df.sort_values('growth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ad62128>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac2f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG6EUAACAboRSAAAAuhFKAQAA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae07898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.bar().axes\n",
    "ax.figure.set_size_inches(16, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. How many genes are in each category (essential and non-essential)? How many non-essential genes show a reduced growth rate?\n",
    "2. Determine the essentialiy of genes for all other available carbon sources (if this is too complicated, determine the essentiality profile for one other carbon source).\n",
    "\n",
    "Hints:\n",
    "* Sometimes an optimal solution cannot be found for certain knockouts (an exception is raised). Use `try` and `except` to catch infeasible solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "carbon_exchanges = ['EX_ac_e', 'EX_etoh_e', 'EX_for_e',\n",
    "                    'EX_fru_e', 'EX_fum_e', 'EX_glc__D_e',\n",
    "                    'EX_lac__D_e', 'EX_mal__L_e', 'EX_pyr_e', 'EX_succ_e']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene essentiality with respect to each biomass precursor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_precursors = model.reactions.BIOMASS_Ecoli_core_w_GAM.reactants\n",
    "len(biomass_precursors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Metabolite 3pg_c at 0x11ab95e48>,\n",
       " <Metabolite accoa_c at 0x11abb14e0>,\n",
       " <Metabolite atp_c at 0x11abb6828>,\n",
       " <Metabolite e4p_c at 0x11abbd048>,\n",
       " <Metabolite f6p_c at 0x11abbda90>,\n",
       " <Metabolite g3p_c at 0x11abc3d30>,\n",
       " <Metabolite g6p_c at 0x11abc8048>,\n",
       " <Metabolite gln__L_c at 0x11abc8438>,\n",
       " <Metabolite glu__L_c at 0x11abc8780>,\n",
       " <Metabolite h2o_c at 0x11abc2278>,\n",
       " <Metabolite nad_c at 0x11abd6630>,\n",
       " <Metabolite nadph_c at 0x11abd6f28>,\n",
       " <Metabolite oaa_c at 0x11abdbf28>,\n",
       " <Metabolite pep_c at 0x11abdf278>,\n",
       " <Metabolite pyr_c at 0x11abdfc18>,\n",
       " <Metabolite r5p_c at 0x11abe15f8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_precursors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determining essentiality profile for precursor: 3pg_c\n",
      "Determining essentiality profile for precursor: accoa_c\n",
      "Determining essentiality profile for precursor: atp_c\n",
      "Determining essentiality profile for precursor: e4p_c\n",
      "Determining essentiality profile for precursor: f6p_c\n",
      "Determining essentiality profile for precursor: g3p_c\n",
      "Determining essentiality profile for precursor: g6p_c\n",
      "Determining essentiality profile for precursor: gln__L_c\n",
      "Determining essentiality profile for precursor: glu__L_c\n",
      "Determining essentiality profile for precursor: h2o_c\n",
      "Determining essentiality profile for precursor: nad_c\n",
      "Determining essentiality profile for precursor: nadph_c\n",
      "Determining essentiality profile for precursor: oaa_c\n",
      "Determining essentiality profile for precursor: pep_c\n",
      "Determining essentiality profile for precursor: pyr_c\n",
      "Determining essentiality profile for precursor: r5p_c\n"
     ]
    }
   ],
   "source": [
    "precursor_production = pd.DataFrame(columns=['knocked_gene', 'precursor', 'max_production'])\n",
    "index = 0\n",
    "for precursor in biomass_precursors:\n",
    "    print('Determining essentiality profile for precursor: ' + precursor.id)\n",
    "    with model:\n",
    "        model.objective = model.add_boundary(precursor, type='demand')\n",
    "        for gene in model.genes:\n",
    "            with model:\n",
    "                gene.knock_out()\n",
    "                precursor_production.loc[index] = gene.name, precursor.id, model.slim_optimize(error_value=0.)\n",
    "                index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "precursor_production['can_produce'] = precursor_production['max_production'].apply(lambda x: 0 if x < 0.05 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>knocked_gene</th>\n",
       "      <th>precursor</th>\n",
       "      <th>max_production</th>\n",
       "      <th>can_produce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mhpF</td>\n",
       "      <td>3pg_c</td>\n",
       "      <td>19.6233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adhE</td>\n",
       "      <td>3pg_c</td>\n",
       "      <td>19.6233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s0001</td>\n",
       "      <td>3pg_c</td>\n",
       "      <td>8.4933</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purT</td>\n",
       "      <td>3pg_c</td>\n",
       "      <td>19.6233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tdcD</td>\n",
       "      <td>3pg_c</td>\n",
       "      <td>19.6233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187</th>\n",
       "      <td>talB</td>\n",
       "      <td>r5p_c</td>\n",
       "      <td>10.0920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>talA</td>\n",
       "      <td>r5p_c</td>\n",
       "      <td>10.0920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>tktA</td>\n",
       "      <td>r5p_c</td>\n",
       "      <td>10.0920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>tktB</td>\n",
       "      <td>r5p_c</td>\n",
       "      <td>10.0920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>tpiA</td>\n",
       "      <td>r5p_c</td>\n",
       "      <td>9.6319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2192 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     knocked_gene precursor  max_production  can_produce\n",
       "0            mhpF     3pg_c         19.6233            1\n",
       "1            adhE     3pg_c         19.6233            1\n",
       "2           s0001     3pg_c          8.4933            1\n",
       "3            purT     3pg_c         19.6233            1\n",
       "4            tdcD     3pg_c         19.6233            1\n",
       "...           ...       ...             ...          ...\n",
       "2187         talB     r5p_c         10.0920            1\n",
       "2188         talA     r5p_c         10.0920            1\n",
       "2189         tktA     r5p_c         10.0920            1\n",
       "2190         tktB     r5p_c         10.0920            1\n",
       "2191         tpiA     r5p_c          9.6319            1\n",
       "\n",
       "[2192 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precursor_production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "df = precursor_production.pivot(index='precursor', columns='knocked_gene', values='can_produce')\n",
    "data = [go.Heatmap(z=df.values.tolist())]\n",
    "layout = go.Layout(xaxis=dict(title='knocked genes'), yaxis=dict(title='precursor'))\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig)"
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