02-Genome-scale-metabolic-models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genome-scale metabolic models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cobra.io import read_sbml_model\n",
    "model = read_sbml_model('data/iJO1366.xml.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metabolites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model contains a list of metabolites. Here are the first ten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Metabolite 10fthf_c at 0x10e0330f0>,\n",
       " <Metabolite 12dgr120_c at 0x10d8e1588>,\n",
       " <Metabolite 12dgr140_c at 0x10a6f0470>,\n",
       " <Metabolite 12dgr141_c at 0x112293358>,\n",
       " <Metabolite 12dgr160_c at 0x10a532198>,\n",
       " <Metabolite 12dgr161_c at 0x10a532b70>,\n",
       " <Metabolite 12dgr180_c at 0x115ca49b0>,\n",
       " <Metabolite 12dgr181_c at 0x115ca4978>,\n",
       " <Metabolite 12ppd__R_c at 0x115ca45f8>,\n",
       " <Metabolite 12ppd__S_c at 0x115ca4c88>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 1805 metabolites in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1805"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.metabolites)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can access a specific metabolite using dot notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Metabolite identifier</strong></td><td>g3p_c</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>Glyceraldehyde 3-phosphate</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x0115e3a160</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Formula</strong></td><td>C3H5O6P</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartment</strong></td><td>c</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>In 14 reaction(s)</strong></td><td>\n",
       "                    TALA, FBA, GAPD, TGBPA, TPI, EDA, DDPGALA, TKT1, F6PA, DXPS, DRPA, TRPS1, TKT2, TRPS3</td>\n",
       "            </tr>\n",
       "        </table>"
      ],
      "text/plain": [
       "<Metabolite g3p_c at 0x115e3a160>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "**Warning:** One cannot use dot notation to access metabolites, reactions, or genes if their identifiers do not resemble proper Python variable names.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-2772b042e9ed>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-2772b042e9ed>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    model.metabolites.10fthf_c\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "model.metabolites.10fthf_c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "**Solution:** Use the method `get_by_id` instead!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Metabolite identifier</strong></td><td>10fthf_c</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>10-Formyltetrahydrofolate</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x010e0330f0</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Formula</strong></td><td>C20H21N7O7</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartment</strong></td><td>c</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>In 9 reaction(s)</strong></td><td>\n",
       "                    BIOMASS_Ec_iJO1366_WT_53p95M, MTHFC, BIOMASS_Ec_iJO1366_core_53p95M, ULA4NFT, FTHFD, GARFT, FMETTRS, AICART, FTHFLi</td>\n",
       "            </tr>\n",
       "        </table>"
      ],
      "text/plain": [
       "<Metabolite 10fthf_c at 0x10e0330f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.get_by_id('10fthf_c')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metabolites are associated with compartments in the cell. Glyceraldehyde 3-phosphate (`g3p_c`) is associated with the `c` (Cytosol) compartment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c.compartment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _E. coli_ model has three compartments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'c': 'cytosol', 'e': 'extracellular space', 'p': 'periplasm'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compartments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some metabolites (like Glucose for example) can be associated with multiple compartments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.glc__D_c.compartment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.glc__D_p.compartment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full name of the metabolite is available via the `.name` attribute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D-Glucose'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.glc__D_c.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can look up the molecular formula of glucose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C3H5O6P'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c.formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.elements` attribute returns a dictionary representation of the formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 3, 'H': 5, 'O': 6, 'P': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c.elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, one can look up the molecular weight of a metabolite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168.041961"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c.formula_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can gather additional information (like references to external datbases) about the metabolite through the annotation attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SBO': 'SBO:0000247',\n",
       " 'bigg.metabolite': 'g3p',\n",
       " 'biocyc': 'META:GAP',\n",
       " 'chebi': ['CHEBI:12983',\n",
       "  'CHEBI:12984',\n",
       "  'CHEBI:14333',\n",
       "  'CHEBI:17138',\n",
       "  'CHEBI:181',\n",
       "  'CHEBI:18324',\n",
       "  'CHEBI:21026',\n",
       "  'CHEBI:29052',\n",
       "  'CHEBI:5446',\n",
       "  'CHEBI:58027',\n",
       "  'CHEBI:59776'],\n",
       " 'hmdb': 'HMDB01112',\n",
       " 'kegg.compound': ['C00118', 'C00661'],\n",
       " 'metanetx.chemical': 'MNXM2378',\n",
       " 'reactome': '29578',\n",
       " 'seed.compound': 'cpd00102',\n",
       " 'unipathway.compound': ['UPC00118', 'UPC00661']}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c.annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can use these annotations to look up the compound on [KEGG](http://www.genome.jp/dbget-bin/www_bget?cpd:C00118) for example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metabolites are not isolated things. They participate in reactions as substrates and products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frozenset({<Reaction TALA at 0x116ba7c50>,\n",
       "           <Reaction FBA at 0x11673d860>,\n",
       "           <Reaction GAPD at 0x1167d7c88>,\n",
       "           <Reaction TGBPA at 0x116bc3080>,\n",
       "           <Reaction TPI at 0x116be8908>,\n",
       "           <Reaction EDA at 0x116714128>,\n",
       "           <Reaction DDPGALA at 0x1166a8780>,\n",
       "           <Reaction TKT1 at 0x116bd95f8>,\n",
       "           <Reaction F6PA at 0x11671f7b8>,\n",
       "           <Reaction DXPS at 0x1166f42b0>,\n",
       "           <Reaction DRPA at 0x1166df748>,\n",
       "           <Reaction TRPS1 at 0x116bf3780>,\n",
       "           <Reaction TKT2 at 0x116bd9390>,\n",
       "           <Reaction TRPS3 at 0x116bf37b8>})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metabolites.g3p_c.reactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model contains a list of reactions. Here are the first 10 of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Reaction DM_4crsol_c at 0x116401b70>,\n",
       " <Reaction DM_5drib_c at 0x116401c88>,\n",
       " <Reaction DM_aacald_c at 0x116401e80>,\n",
       " <Reaction DM_amob_c at 0x116401ef0>,\n",
       " <Reaction DM_mththf_c at 0x116401f28>,\n",
       " <Reaction DM_oxam_c at 0x116401fd0>,\n",
       " <Reaction BIOMASS_Ec_iJO1366_WT_53p95M at 0x116409048>,\n",
       " <Reaction BIOMASS_Ec_iJO1366_core_53p95M at 0x1164090f0>,\n",
       " <Reaction EX_12ppd__R_e at 0x116409f98>,\n",
       " <Reaction EX_12ppd__S_e at 0x1164094a8>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2583 reactions in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2583"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.reactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look at the reactions associated with Glyceraldehyde 3-phosphate (`g3p`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TALA TALA: g3p_c + s7p_c <=> e4p_c + f6p_c Transaldolase\n",
      "FBA FBA: fdp_c <=> dhap_c + g3p_c Fructose-bisphosphate aldolase\n",
      "GAPD GAPD: g3p_c + nad_c + pi_c <=> 13dpg_c + h_c + nadh_c Glyceraldehyde-3-phosphate dehydrogenase\n",
      "TGBPA TGBPA: tagdp__D_c <=> dhap_c + g3p_c Tagatose-bisphosphate aldolase\n",
      "TPI TPI: dhap_c <=> g3p_c Triose-phosphate isomerase\n",
      "EDA EDA: 2ddg6p_c --> g3p_c + pyr_c 2-dehydro-3-deoxy-phosphogluconate aldolase\n",
      "DDPGALA DDPGALA: 2dh3dgal6p_c <=> g3p_c + pyr_c 2-dehydro-3-deoxy-6-phosphogalactonate aldolase\n",
      "TKT1 TKT1: r5p_c + xu5p__D_c <=> g3p_c + s7p_c Transketolase\n",
      "F6PA F6PA: f6p_c <=> dha_c + g3p_c Fructose 6-phosphate aldolase\n",
      "DXPS DXPS: g3p_c + h_c + pyr_c --> co2_c + dxyl5p_c 1-deoxy-D-xylulose 5-phosphate synthase\n",
      "DRPA DRPA: 2dr5p_c --> acald_c + g3p_c Deoxyribose-phosphate aldolase\n",
      "TRPS1 TRPS1: 3ig3p_c + ser__L_c --> g3p_c + h2o_c + trp__L_c Tryptophan synthase (indoleglycerol phosphate)\n",
      "TKT2 TKT2: e4p_c + xu5p__D_c <=> f6p_c + g3p_c Transketolase\n",
      "TRPS3 TRPS3: 3ig3p_c --> g3p_c + indole_c Tryptophan synthase (indoleglycerol phosphate)\n"
     ]
    }
   ],
   "source": [
    "for reaction in model.metabolites.g3p_c.reactions:\n",
    "    print(reaction.id, reaction, reaction.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second reaction in this list is Glyceraldehyde-3-phosphate dehydrogenase (GAPD)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Glyceraldehyde-3-phosphate dehydrogenase'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.GAPD.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximize\n",
      "-1.0*BIOMASS_Ec_iJO1366_core_53p95M_reverse_5c8b1 + 1.0*BIOMASS_Ec_iJO1366_core_53p95M\n"
     ]
    }
   ],
   "source": [
    "print(model.objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ATP maintenance requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Reaction identifier</strong></td><td>ATPM</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>ATP maintenance requirement</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x01165e5c50</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Stoichiometry</strong></td>\n",
       "                <td>\n",
       "                    <p style='text-align:right'>atp_c + h2o_c --> adp_c + h_c + pi_c</p>\n",
       "                    <p style='text-align:right'>ATP + H2O --> ADP + H+ + Phosphate</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>3.15</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Upper bound</strong></td><td>1000.0</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<Reaction ATPM at 0x1165e5c50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.ATPM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The math (scary!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\* Problem: Unknown *\\\n",
      "\n",
      "Maximize\n",
      " obj: + BIOMASS_Ec_iJO1366_core_53p95M\n",
      " - BIOMASS_Ec_iJO1366_core_53p95M_reverse_5c8b1\n",
      "\n",
      "Subject To\n",
      " r_1: + ULA4NFT_reverse_07217 - ULA4NFT - MTHFC_reverse_f6fcc + MTHFC\n",
      " + GARFT_reverse_7ecb6 - GARFT - FTHFLi_reverse_a6dc7 + FTHFLi\n",
      " + FTHFD_reverse_44321 - FTHFD + FMETTRS_reverse_3b6c6 - FMETTRS\n",
      " + AICART_reverse_b7b59 - AICART\n",
      " + 0.000223 BIOMASS_Ec_iJO1366_core_53p95M_reverse_5c8b1\n",
      " - 0.000223 BIOMASS_Ec_iJO1366_core_53p95M\n",
      " + 0.000223 BIOMASS_Ec_iJO1366_WT_53p95M_reverse_06c4a\n",
      " - 0.000223 BIOMASS_Ec_iJO1366_WT_53p95M = 0\n",
      " r_2: - PAPA120_reverse_75d70 + PAPA120 + DAGK120_reverse_7cd00\n",
      " - DAGK120 - x_666 + x_665 = 0\n",
      " r_3: - PAPA140_reverse_255f5 + PAPA140 + DAGK140_reverse_87f8f\n",
      " - DAGK140 - x_668 + x_667 = 0\n",
      " r_4: - PAPA141_reverse_945ab + PAPA141 + DAGK141_reverse_f6e5f\n",
      " - DAGK141 - x_670 + x_669 = 0\n",
      " r_5: - PAPA160_reverse_c64df + PAPA160 + DAGK160_reverse_0238d\n",
      " - DAGK160 - x_672 + x_671 = 0\n",
      " r_6: - PAPA161_reverse_1bc33 + PAPA161 + DAGK161_reverse_9bfe7\n",
      " - DAGK161 - x_674 + x_673 = 0\n",
      " r_7: - PAPA180_reverse_9f7f6 + PAPA180 + DAGK180_reverse_eb3e3\n",
      " - DAGK180 - x_676 + x_675 = 0\n",
      " r_8: - PAPA181_reverse_fb09e + PAPA181 + DAGK181_reverse_8c0c8\n",
      " - DAGK181 - x_678 + x_677 = 0\n",
      " r_9: - LCARR_reverse_9213d + LCARR - ALR4x_reverse_17ebf + ALR4x\n",
      " - x_682 + x_681 = 0\n",
      " r_10: - LCARS_reverse_66c3d + LCARS - x_686 + x_685 = 0\n",
      " r_11: - PGK_reverse_02696 + PGK - GAPD_reverse_459c1 + GAPD = 0\n",
      " r_12: + DHNCOAT_reverse_58c26 - DHNCOAT - DHNCOAS_reverse_af3a9\n",
      " + DHNCOAS = 0\n",
      " r_13: + AAMYL_reverse_58557 - AA\n"
     ]
    }
   ],
   "source": [
    "print(model.solver.__str__()[0:1550])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene-Protein-Reaction associations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glyceraldehyde-3-phosphate dehydrogenase is associated with a single gene. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b1779'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.GAPD.gene_reaction_rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phosphofructokinase (PFK) on the other hand seems to be associated with to isozymes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b3916 or b1723'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.PFK.gene_reaction_rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can display the gene names (typical 4 letter gene codes) instead of the identifiers (Blattner numbers in this case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pfkA or pfkB'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.PFK.gene_name_reaction_rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here a very complicated gene to reaction mapping (ATP synthase)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'(atpA and atpD and atpC and atpG and atpH and atpE and atpF and atpB and atpI) or (atpB and atpF and atpE and atpH and atpG and atpC and atpD and atpA)'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.reactions.ATPS4rpp.gene_name_reaction_rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding things"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "One can use `.query('search term', 'attribute_to_search_in')` to search in model metabolites, reactions, and genes. For example, one can search metabolites that contain the term _glucose_ in their name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6-Acetyl-D-glucose\n",
      "ADPglucose\n",
      "DTDP-4-dehydro-6-deoxy-D-glucose\n",
      "DTDPglucose\n",
      "UDPglucose\n",
      "UDPglucose\n",
      "UDPglucose\n"
     ]
    }
   ],
   "source": [
    "for metabolite in model.metabolites.query('glucose', 'name'):\n",
    "    print(metabolite.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The stoichiometric matrix S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ..., -1., -1.,  1.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cobra.util import create_stoichiometric_matrix\n",
    "s = create_stoichiometric_matrix(model)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how the the stoichiometry matrix S looks like when visualized as a matrix plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x11949ea90>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IUpDYiAX7QvR6xVambyxNbB9Q+tUvV1ZuyvYjm0YtPmDFVKIoG84FSq+RQwcd\nR1oH9KrFXvS/0yMgKT5O9xm2HbREICzt9F6T04cEhx1eQUSCw66MObYd1GWp2wcA1hZWYsboVOVZ\ntj/YscjKq9Vzhl6ntFIzPHqdftN87HZeEQWyc4GdE3S80z5Q9018vN0Uk45ezgcA4JA1rH9Ur6LZ\nbZMgSJj0xm70jOfx7QUvShZPRs+UeCX4ORVtZBdWgchA/rdn8cU/L6Bk8WQkJcQhp7gaReV1AAL2\nt7PGpgXZZT76fh6G9UvG2GH9lLKzC6tQVF4HjuOwdMpILPr4KF5+ZDRyy2pb2RtvP34eflHGOacf\nl/WIQ0ZabxBCUHneiVtf3YO/P3sT9p+qx6aCatQ2+rB37u347davAHAYO6wvHm2x1117uBK5hbVI\n65eE6otuPJw+WLGTTk/rjS1lZ7Dq38Zi1nsH8M1ZD/7rgR/iJxOGKXbS654aDyIDm4prg+xgqWJG\nTffK/HIk2uICSp6W9pg5NtDet7+6A40+GQULJ2HLl9+htLKhlZ00cMnueeHmo6bspAFAlGVsLjmN\naRlDIBAR72w/iVuu7g/bJZkiMob2QUlVfUALz/NYOmUkFm4+gqqLbkzPTMNj49OUiT93QwmKKuvQ\nLyUBVw5IUfqRQm0HXNJiez1rXBpkmWDuhhKU1jRi27O3aTJquopd8tANunbSgiBhxl8PguM4zGDo\nY8dTcUW9YkOcU1yNQ998j8/+eRGjruiBWWOHtnqGtS/mSEAZR/uxpKoeo1N7YVNhNWoavJh717V4\n/MZhcLoFXTtph8MWZM+uZyddUlWPkUN6Yvnek3h/bwW2P3dbUB+KshxkJz20bxJ4ps/YOUPtxme0\n2ELLEsH6wmqUVNUjs8WXgM7B4f1T8IcHR+C+dw/g77+eiP/e+hXSU/ugpKYOGal9UFbTiGVTRyKn\npFrxC+B5rlXfmIYZE5DO+nSVoP+sSY7aBI81yaH/WXtJaoLHBnFR2y3T/NnASWzZ9DkaFF1tS8qm\npbaflE6/XyIej5/8+R9fkqELtpJ3//ElaWz2KuZTWqZ5rF00NTtS20kTQpR2YM2XWNtSPXMmlm56\nvBhrQ6tub2qaSG2+WZM+LbtndV307KRZW2VqUsa2NWvvTD+0DNacjoK2NdtWaujZXrPPG0FNgxao\nDbWeGZ7aBM3vl0h9k8fwGbbt2N/smGDt7SVJ1rWTZtvBTPvQkL0sPWz/0Tpr9RnNU8/sji2PzYcQ\nopSpts0dWfwFAAAgAElEQVRn57o6CBjbNzBpghfV4o7uEruD3f7pbePMWAaYUXSYlVfTVSsrNqm8\n0IBJbxzCyCt6YMUTWejbIxFA+AeTmtHgG9WJ1sXIwoTNa0NhTStPxBgiA6vWGKGsVDpTaWdFpxMJ\n2bReHt0mdkd3AMugF3x0tJXck01j9N/MoLYiGmKjifE8hysH9kHp4kl4cNRlmPDSbvx15z+D7Gat\ngE5ERaapsmOmA3da+hBdBk1tX83oI3i+tat4DJEB25dm7of6H2osO12C6fGmpUNgY4CoYSYao169\nwkEk8uh0kYbRJzMzs9V2p6vDqvjGigeV1rOhtqd6z9VcaCRXLdxKLja6SX2Th3x46JRheqPyPzjw\nLblu8WeaIiCjGNDhiLrCaadIoS1l63lvdjTCPTZLS7Rj5XmKZqePXLf4M7Jiv/54Y/NUH4H2zLoi\nMvq3n7fydjSiLVQZbYVeHugObuHdkUnT47PUoLERWDkWZWI00L3e4NKKcyFJAffs1YcqWsk1zZzV\n5vdLpOp8A3lv7zdk6IKt5OZlOxSZOI3XQWVr6woqNd1y2cnT2OwNconVkrmqnw8Fl0sgFxvdSnuq\nJ61Wvnr/9aD3nFZ9zQSj18rP75fI7PUlreSXq/LKw3qp68lCCTFmwj6fSB56Z69uGjat0W8aE4OO\nFy29htH5hGfqmg3rTWXqahm5yyWQZqePnKlrNqRfi14Wajm0Wq9hZuxoPa+GWSYdE3d0IOjxWWo3\n5oZmL8Ys2YnZ60uwJq8Sjy4PhFnMLa3FwyOvwOJPvsTDI6/QdJVlt2/s1ornOUwbMwSl1Q1B2042\nGp+e268oypizoRS3vnYAZZWNKHnhTux47jbY7TzmbijBXW/twft7TuCZNfmYs74ILo8Q5O7Luj5T\ny5akhLgg8Yosk1aiH0q/mWDwbrcfGb/fhoylu5T21NtGh9p+60HvOUGQsOCjo4FQsQbHLqnz0nMp\nBoDMtD74qPS0Ih7KKazGR0W1yCmstrT1zymqxpr8Sjy6PF8Jz0nHhyBIePT9/FZhMoPHDW9YnlqM\npf5NQwwUnLqIR5fnYW5OCeZtKMX8jZdCDlA6vF6xVT84XQJuf20v1uVXadIhCBJmvncI0/9yADOX\nH1LEFtSpZ/KbOzHxlb2GIRnYetC+pPlQC5Lsgipl/OYUVWP94eogV/FQbcSO/+zCqlZu+FYQ9YrD\n7uZxSJmz2o25odmLlERHkJ0xa2NK7S6NFGxAaMcXLRtWtR0yLbPB5cXTa0qCzLgEQUKTx4fxy3ZD\nAhAHIMEB7JxzO3omxCvu52q7aa9PRFJiXEinHCM7ZzXcbj+8ogiHzRbSLTyUYsvqc1p9Yka5Bmi7\nFKvvUWbGuvObAc1Hyz4XCPSfka2z0yXg06/OtLLf1qsnSz9rH8zaW1OwCnNaN602o+EP9OpNXzKy\nTIL0FW63P2DLLEohQzKw7a0eh+oYICxzNTs22Wc2FNYEhaSlz3YLxSEhwPxNZaaE/F0FKckOTYbS\nu0eCMnno5KKdycbv1YLe0Up00rBQTxoK9QrWbufRv1dSEIOWZYLNR06jb49ElL44GQsmD4cfwPi0\nXpj0+h5MfnMPGpxeNDR7sfpwhbK6X1VQjrHLdsDtCVboaL182ckRCklJcejbM9FU3I5Qilmzz1Fo\n9UmoPLWseoye33LsO1M0apWhPlqMdd7QWglSZvXfW7+CrIpHr6X4Y+/RleOm4lqFCdHj4ex2Xvmw\nz6nHOIuUZIdhW9J8Nx89HVSPpKQ4JCTYlVg4emB3MOwcoXyG0sqORfX/UGCfocGzwlYimpGJdNYn\nIyOTPLu2qEvYSocDPVkVez9chFLK6dFiRUHk90vkYqNbCZm6fM8J8sNFW8nQBVvJyP/+O3lmTWFQ\nPOK2KmHU8s+OVqwZybzbI1+rilqz+Rs9r2efrCWnprJ0tS6lIyBJl+I+a93TOvyXfrMyZzaWe6hD\nZs3QZCUNuoPisCsyaTOWE/SbVexpTdA1+RWa+ZiJF6x2oDFDt1WmrjURXC6BNDZ7SWOzV+k7vz9w\neMGKgyctMRi1QpSNU70qr5ysPlTR4WOjvV4M4bZ7KOWU1fy14PdL5LnsYk2LjVUHyxUmp5WmPWG2\nvux8WJNfQZ5bV6ypuGwrg7b6AjfLpKNa3NHVEErxRbeFrGKvpKpeUU6olWjFFfWtlDxqRaEWqAKN\nxglQ5230jBUxgNYp2ElJceiZEo+khDjwLcdy2e08/vDgCHxa+j28XtHUdo8e88SKuqi7tiwTFJXX\nobDiItZbUKypEc5z7WWHHU67s+NNK0Z0OPnrgWjkK8sEZTWNioJSK017wqhOajk/nQ8z0lMhy3KQ\nwpOiLbovq/PHUt4RzzHCoBO9KyBoMGQGx6SgYC0A7HYeY4cFDjVQWwbwPIf0tD544ZNjQYzKbufx\n8iOXzv4zmpSyHAhgbjbQuNUBZpTebufxyvTRysBPSorDmifH4cVPv8TqvAp4vaLidKBXBw6qNuEu\nyQSzhvVF1rB+sHGXHIWsMF3K4IwcH8zk0RlgZaN0vM3KSrPszGOFfq15aLfzWPLwDVi05SgWbD4K\ncNEzV43kv4G6cJYcW2ie7HeH6crMLLc769NVYncYQW0/a8UumG4jjfI2U35Hy26N4HIJ5KG395L7\n39xFRizeSk5fbFK2iWqZptExVerzEGevL1HOobNCyy0v7QzrHM1IyKc7s1+s0m/kbENtlqNtrrLj\nhR4/xh5DRoi1Y7pYByyfTwySYbenuCPqTfC6euwOWf7XjSmhZ7YlCBLcPj9ufmknfDJweNGdAICn\nVhcjRxW1zUzegH5MlFAwExfELA1WnzWKX9GWvK3QYNUMkTU9pOFmc4oDJ7g/Oi4tqsa4eszlltVq\nhse1mp/aDFOrPDPoFiZ43QGhYkpEk3lhJLfveo4OQMCEqnePBBQuvhtFL0zCx8dO46aXd+HBUZe3\nkhPqQd2eWra/ZtCWOOVtYUhGMsywTbXCoCEU2L5TiweoiI4jasFU50LL0Sa3rBayZM6OXQtNTl8r\nEzyzZp1t7kczy+3O+nQHcYcRImH2Eym0h3mZWnOu5zYtSTJpdvqIx+MnU97dT1YdrGi3E8BZeqIZ\n0SSiolYRrPhNKxRsNLQpO47V429tizhMT3SjN/4bm71k6IKtSuwZmn5VXrkpk1W9fNEdrDsIgSXB\nflcDVQJGg0dle2in1ZYfRNZeVfA8h5RkBxIS7Mh+agL8soTxS7drKvXoydhq6LkQa123qjDqKLD0\nRJPYAACKK+oVxw+2/VinEHWbGu2K2muXwI7jVt6O3CVlodpqSm/8K05AXPA1QZCwufi7kGMoIvPK\nDCfvrE93X0kbwe+XSM2FRkvPhFISRrItzeSltnNem1ep64DAorHZS65b/Fmr1bTPJ5Ip7+4nH+w7\nGaTsc7kEsiqvPCgwVWOzlzz0zl7y4YHyVsFu2IDvLH1qhLOateIMREGD1kdqV6XlxNHWPNaplLJa\nNvhadu1a/a212m2rHbcZsIrONfkVQStjvfR098dGcFxXUEnW5lWGpXBmgY5YSXMcV8lx3DGO48o4\njitqudaX47jtHMd92/Ldp+U6x3HcOxzHneQ47ijHcRlmyoiGVWZHQxRl/MfqQ5j46n7UXmwy9Qwb\n1EVv9ThvY2lEVo9m8lKvVnk+oFgyo0DtmRKPwkWTW7l7Oxw2rHxiLN7fd0oJFOT1irj77b1Yn1eJ\nORuKUdfkwdMr9mHy6ztxeY84FFdehNcrIqeoGqIoY+3hSizIPRqUr5YMOBy5cKhntO57vSImvbEL\nCzcHjlYye2CDGRrClW1ruYHPUikFtdzi1eEJ9MwC6eoSgNIv4crgreyKWFly3olzGKcKdKZVPpW7\n01ggPM9hRkYqeI4zpeCOBNpk3cFxXCWALELIBebaKwDqCCEvcRy3EEAfQsgCjuPuA/AMgPsAjAfw\nNiFkvFH+3cG6I1yIoozvG50Y0q+n6We0YitQCIKER5fntTrzLhyYzcvqqeZmIMsEa/IqMTMzVVH6\nNTl9+MmKApxtcKLOTUCFJL1twKBecbh2cD+MGdobP50wHJuKa/FI+uBWdEfKWiSUhl+rnHkbj+Cl\nqSNNKTFDWYWoy9Aqz+yLoCOsS6jlE2BexKOuAw3WZBaCIGFV3inYbXb89KbhQY5B6nbVa4dItE9n\nWnc8DGBly++VAKYw11e1rPTzAfTmOO7ydii/W8Bu500xaHWELvUAo3A4bFjz5PiIvP3N5qU2T4oU\nHHZbUN49U+Kx6Zc3Y8szt6L4xcnY9twEDEgAGiTgRJ0fflHER0XVaHL7dFfyakb2fO6RNtGstbLT\nys9u5/HajNG6p5Vr0RlKxqkXtCmcVWe4UJehp4uYnjnE0stQXQdZJthYai7sLH1+7sYSvL79JB4Z\nMzioXK121aPLipVIqP+h0FYmTQBs4ziumOO4p1uuDSKEnGn5/T2AQS2/BwOoYZ6tbbkWg0UEDVCd\nbaL6nijK+K+tX7XyzAtH/MHmZQbqLbj6ntZ/vTrRib2xJLjeTR4fJry8F4s2H8Gfd1dhwtWDcP+I\nAXhg5CAsmToK1XUeTFi2C9/VN2PexlJ4vaJuOUAgvjNrG2yUloJu29kYxWx9WHGUljuyWfFEuF6F\nRopqtSKtLdBkpDphEmiMdLOMS10HUZRRUtlg+Dxbvt3O481ZmShcNFkRYdD7WvVQi8C07qmVpexY\nV9c9LBGUGcG13gfA4JbvgQCOALgVQIMqTX3L91YAE5nrOxEQlajzfBpAEYCitLS0NgnmuwqsBh1S\nezqZzZeaTUUi8le4J4aogwNp/deK4KdWNrEKKVqP8w2uoJNAWAVhfZOH/Gr1YXLlgq3kgbf2kOey\nSxQPNJdLULzQmp0+siqvXFGU0bxdLsHQRJHSR5Wd6ramabSuq/Mxc41FfZNHOeaM9do0a1bp84lk\n6p8PRDSKndnjs2h/6gUTMwLrSWgmrZX7ai9W9byh91wuodUcounZgGBaZaGjo+AB+B2AeQCOA7i8\n5drlAI63/P4rgMeY9Eo6vU93PD5LjXDsk9s6mSIV+autZRv9D8WstCaVUZn0GLKV+8uJx+NX2tDl\nEshNS7aRB9/eS/666xsy6r//Tv5z1WHS7PQpZVDLC/VxTWpouZmrJ6+WDa8RQo2P+iYPGbpgq/J5\n6J19rRi1GbQngw4Fj8dP5qwPb7FgpS2tQh22VT1vqGu4lpWHeiGhhXZn0gCSAfRgfh8CcC+AVwEs\nbLm+EMArLb/vB/A5AA7ABACHQ5Xxr8CkCbEWppJ9Q3c2zDLUUM9Hmh6je1orepdLIH/bf4rcvHQ7\nWb7nBHlmTSF58O09ZPneE62cN/QYJp207ISlK/W2msSFu5KmdEUCZleiVndnNH248VPCWeRYCeFr\nNi89+rR+U3QEk76yRcRxBMBXABa3XO/XIsr4FsAOAH1brnMA/gzgFIBjWqIO9edfhUmbASsKMGNr\n3FYbTiv0GIkmQj0fKUZtNT+tlThdYbtcAnng7T3k+he2kvf3nlDEIFrP0WefXVdMpry7L8hWm66+\n1WnDqVs4MMswjU7WpuWbEfWwOw+rdFpFOLsSWhbtl0iMPzO0a4m9COkEcUd7fDIzM9ud2bQnwn3L\nat1jt16hVq4ej1/Zdoc6YVxvkJodfPTbrAzSahlWEI5Mk36rI5vVN3nI9S9sJQ++tYecrXeSa1/Y\nSs7WO0ljs5ecrXe2youeTqO+pt4ia8kvKbREDuzLMByGYoZBj/ndF6YYtZn7bB3bsoOwshPTak+j\n59mxr5Xe7M5QTz+kl6d65W+WSUe1pwghBJPf3KNo4rsSjMydjBwOtO5RawE9O2h1AKOEBDu2PXsr\nAG23etY1lpbFpjFrqsXa4qrTh7JAYDX7kQIb6D0U2HbmeQ4Pj7wCDodNsRxIio/D1YN6ILVfEpZ9\nfhxzJl2FO17dg1F/3IFxL+1B5fkGAAFnFK9XhCBI+OnKQsVKgtpZs32rtkxgafB6Rc2TvHmew7T0\ngDWLlROn2TKNkJQUh4Pz70RSUpxuHpQOI9D7WvHOrVo0WLFyoeaS1NrCzGnz6vZn04dybNKK7c6e\nX0jz0jpsg1qQWHYTN8PJO+uTkZFJfrO2Y4/kiSQivZIOVRYrq2atErSCFbFvf60VntU276htfCRF\nKPVNHtLs9BGXS2hl3SBJMvF4/GTNoQricglEkgLnNP7n6sPkZysOktG//ZzUXGgkWb/dSrJ+u5U8\nu65EyUtvNamG+qw9o12j3rZey7VZb3ttBZEQR6n1AGbSEkJIs9NnydV7XV7reO1mytM7Yk5vJa0n\nPtIqW0txqM4X3UXcsc5iIPfuDiO5oHpQ+Hwi+eDAt5pxMNTo7Beh2e2tGVm3GdDIZte+sJX8el1h\n0HafFX+sPFhO5qwvDQoY7/OJ5P09J8iIxVvJDxYG8qg630De3/ctGf3bz0l9k8cUgzZrSmlUh6sW\nbtVl1Gxd2kNhyaYzK6+m0DMVbHb6yLUvbCUr94eOMGeVTor6Jg8hJPglaRZaDNqKOSULs0w66oP+\n79uXp7kViyboiSGcLgEpyQ4lFq/XHxDb2HkeCfF25RlqZO92+5GQYFfcXHmegyBIEPwSeC7glbW2\nqAKPZw1X4iez2y+vIILnOCTE25V7bp8fTkFAz/iA4b7DfslLkNLtFUU4bIHrMiHgOQ5yy7iQCUFS\nfKD9HQ6bsuVjyxIESdny0XJFWUaCww6vIMJht8HrF5EQF6DLbufhdAuw85fiP2wsq8GMMalKPl5B\nDKKhb89EAIDb7Vfa2esXFXpTEh1wegJ50muiLAfareV3UnycQqMsEwiiBJkQ2HkejrjgdhElGRvK\nqmFDy7l4hMBuC/ag9IqB8pt8Ptz1xiHcdV1fpKf2xsbCCrz/1E34x1dn8ORN1wS24i11EWVZaWtB\nkpTfNLpckIegFKA/IcEOp1sI6jvqoVh7sQlD+vVUDlLgOS4oHc2bdcyh4422PTuOaBs64gJ9TfvN\n4bDB6xWD4l/Q8bAyvxx+QcTMzKFKHwOA4Jdgt/EQJVnpk6TEOIiijEf+dx8+fHI8UuIdShmyTJCU\nFIfv653onZgAu51vJa5hRQ5KPzLlsN90LImiDIfDBlGU0eT1YdxLe7Bv3kRs++dZgHCw22x4NDMN\ngighKSEOXl9gbCclxSmiVnae0vlJ82XHP613UmKcIj5h+5XmI8sEyckOU27hUc2kMzMzSeL0l7Fj\n9u1tCs7enpBlguzCKtjAB8UgaHL6MHbpDuycewv+sOUoSmsacc4TeMbBAZOv6w9HnB2jUnvhWG0T\n/ufBEbjl1V34zZ1X42+HqvDL267GI2MGY9r/7cWJ8z4kxgGDezlw/IKA6wclYni/ZGRd2R8fFdWA\ngECWZXx9zgsbgHuvH4CsK/sh++BJnKgPlucPSraBAyCKEjwiIEqAD1CCtmuNhhGDkhAXZ8f6n92I\nnNJq5ByqwNfnA2XddW1f7D9Zh6H9ElDn9qNHHIfKehF+ANf0jcOJOj/6OoA6ARiYxOOy3sm4b8QA\nvLS9HIk8YOOBYf2TIEky7HYbhvdPwcnvG/D1eV8QDfkLboOd53H/23sBAD6fjAZGfDvp6h7YebLZ\nsK9+OCABDV4RfRLj0Own+Ow/J+LFLUeRf/Ic+vdIVCbSBacPIECdS8K1lyUjtU8Ctv/zIq4ZmIgL\nLp/SSBzHgeM4NHpEJMUBbgFIsAfqSjH56l4oqGqEy3/pqMn4lvYWASS2rD+G9E5AdZ0XyQ4OPM/D\n55PQIAI9HBx+deswvLSjApel2CHJEhIc8dj+3G0QRAmj/rgDRYvuwOPvHcDxi4GoJZel2CFLgcZx\nOBx48uZhSHTEQZRlbCioxJkGN8Zf2R92G48brx6IH40YhP/57Gt4PF58cbwedgBX9rHjolvERR9w\nRU8HnpyYhje/OIlkB2Cz2TCwVxLWPTUBgiRh0qu7UN8SMGVAInBZ7xTIsoRvznrQJx5wCYCHADYA\ni+69BlPSByNr2W4AQP8E4II3MFZqG/z4Ys5ETHrtAK67LBlXDeqJ12aMCZIhr8wvx6b8KvA2HkP7\nJqH8XBO+Oe9DvwSgwQv0SeTQ6CPom2xHRmpvVF10oabBh5uG98aR003ol5KA26/qgT8dCDhF2wD0\nT7GhdzyPExf9uOe6vtj2TR1SHBz2zLsD97yxCzabDf1S4nHB6UVGah8cOHURt1zdD8fOOPH/xqfi\n0yNnMKx/Mqrr3PCLIk5d8OL5e67F0doGnDzbiMqLXiQ5ONh4Hn2THbjo8sHjJzj2hwdKCJEzDQct\ngOjkfAo4ZA3rF/WR8GzgMT1zCICAcmDq6MH49NgZ3PGDvrjl1f0AgLwFtyHBbodMCHKLa/DZV2fx\n0Kh++Lj0NIb3T0GCw455d1+HWWPTEB8Xh5lZqcguroLDEY8F96Thy++akJ7WB8WVdSBExsHyemQO\n649hA1LAgcPoIT1Rt78c4IDaBi/SJRln3TLu/WE/VF5w4vh5H56/+yokORwQJSnwIrhpKMpqGlF+\nvgk8x4Pnedx/w0B8mFcNSZZwwSVj3t1XISU+HjwXWAHHcTZMHzcMuUXVqPeIsNvtuHJgCtL6JGKo\nLGPXiYvgOGBQkg3XDemHNU9fhyV/P44Rlyfjsy/P4qFRV+DTo9/hvhEDwLcwuTFDemPFwQpkXt4b\nL00ZhY2ltSiurMN/P/BDyIRgc0kNfvZhIY6fdeOay5KQ1icRu/55EQCQbAc+f24idn5zAV+fPYX+\nPeLx0KjLcfR0E0YP6QUiy/gwvwZPTEhDgiMOkizjSG0Tfv/g9fj0qzMYndoHe46fh80WeEFkDu0D\nAPAJAl7ZfgoPpw9GfFwcsq4M0FtSVQdCAgw6a2hfTBlzBV7ccgyyTFBxwYkGr4RnbhyA9/bXYnj/\nRCQnJWLuPVfg3hEDce/bB/DRr8bjx38twKDeyfjgp5n47Nj3IAQABxRVXERNvQf33zAQr20/hesv\nS8bwfsn45zkPHrhhENJTe+PTY2ew6t/GISHBDm9zi2s7s9AalGLDJ7+eiKdWFoHneXzw00w8vaYU\n654a3xL97Xt8ddaNoqo6XNEnBb+7fiDueGMvds6+Fb/f+jXuGzEAkAl2nbiASdcNxJjUXvjqjBOP\nZQ7HlpLvUOcWkZHaGxwHPLb8ICouuCEx+uXL+vRA9lMTwHMc5m4oxrEzTvz8tiF4Z9cpcABWHKzA\n5tJapNgBjwis/48bMfnNPNR5/HDLwGPLC7HgR9fAYbfDxgeftiPLBGUV9WjwSkhP6wGe43HVoF6w\n2d0YPiAFv3vgevz9y7N4YORl+PjIdyiracTMcQPxwMjL8NSqIvzs1qtx+ORZ/OnAGdgAJMfzuPmq\nvrDzgV3rrAm98WhmGp7/6Ahq6tzgOQ5+2DB+WD+kp/XGikOVsNttuO3agbDb7Nj8H6Pw8J8Pol9K\nPMYN74+XHxmC3LLT8EsS4u02ZAzrB5kAHG8Dz/NI65MIm43HtMG98OnRMzgGcyvkqF5JZ2VlkQMH\n8qN2FU3BRsQSRVk5oXtaxhAIfgmiLKN3jwTl/oLco/jDgyNgt/NY8NFRLJsSiIDGbsUBYENhDR64\n4TL8duvXWPLwDcr2ErgU+WtjUQ2mZQxRtmEAFJGC1yvCbuexvrAahZV1eJ1ZldDnteJI0HwEUUJK\n0qVwoSxt7LZZECS8+MmXWDplJMSWGUu3m+w2nkYvY7er9JtuGem2kZYzf1MZAA5/eHAENpXUwm7j\nMTMrFV5BxKKPj4IHh5uuHoAZWalKOazoha2rur82FNZgeuYQpWx1oB+nWwiqP32WbQ9WVEDpdjhs\ncHv8cMTZgvJcebAcszLTMCenCK/MyEBKkiOITvq83c4HPc+WR7fYNP3Kg+V44uYrIQgS1hRUIN5u\nx+M3Dgs6qoytvyjKcHoERSzhcNjgdvuRlBQXLDrziYrYjO1nVlTCih5EWVZEY1QU8PymI/j9gyOQ\nlBgsNmBFYj1T4tHk9MFht0EQJTjstqC+UosQqehQ62xBtt9pW9LyWJEEFemow42ytCn96PYHiR7Z\ncukco2VrWRexY5kd76IoIz7e3h3EHVnk9gXvR83pJWahJ6OmYAeZUUhMlqnp1Z+d5Eb0sJOrPWAl\nLCk1VzJjiqRm6OxvtWzSav3MtF2kIIoy5m4ohV8S8fevLuD+EQOQMbQPEuLj8NjYoWHToWbyQPSc\n6qKWxf6rQm+cdYuDaDkOXY5BA61XWGqog6OzNs4UskywsaQm6HRmLbtPM4OfvunVkGUSsehnVvrI\nTLhNNl/aRvRZdZnhHocVbjS5cMDzHMYN74cbrxqEwwtvBw/g3d3lcHsEyDKB2+1X+sJKWeqXV2cw\nQz16eZ4zPIiivdHZR3nRvNt6qHDX4n5dCFYcQqalD9F07CAysKm4FqIoI7uwSjPMZbiQZYK1hyvx\n6PLWDhQdAS2HHKO0WkydOjLkltRi2ZSR7fYyNzPRWDGQFniew6yxaYiz8ejfMwlvPpaFnbNvxYeH\nqvDhwVOY8NJ2TP/LAZxrcOFvh052SJ+0dRyxDiTqcUlXj3qns7Q31E4uei+LtjJRtYMLvUZhZUGi\nCzN2ep31ycjIjJrTtMOBFbr17CxZw3gahS1SMS8kSQ4Z2a0j0JY6hXKXjxTMONGwbuVm8gnE/Cgh\nVecbSNX5BvIfKwvI0AVbyfAFW8mv1xS267jXanMr5bF23rQP2NCckT553iq0nFyMbLmtgLVtX5Nf\nERTBz0rd0V2cWbpy7I5woRcnIJyAMmbKaq8JZSVPM2lpECS95zuLMbAvCquMteZCIxm6YCsZtmAr\nOd/gIo3NXlLf5FFiFeuFPY0E1Ax6Tk5pSKcnFmxkQHXgr85+8WvREAmatJyQwolbQ0g3YdIZGZnk\nueyu6xYeDtriwdSWMtsjz0gyTb9fIr9ZV0huXrZdl1F3xjihAeCtBKRS40xds+IFR+FyCeTmZdvJ\n+F3ZxtkAACAASURBVD98TlwugVxsdJNfry5o10VLs9PXKh62GUQTY9aD2g28LYjUODPLpKNeJk06\nQeHQmTCSYbWXXM+KfNhKnrQekcjPbufx6rR0PH3LVZpnK8qyuYBNkVYS8TyHsUP7mrKw0ZN9XtYn\nRTHRpEhKisPff30LBvVKRnZRFTKW7sK2r89jTk5x0AnXani9Yth1TEl24ItnbrFs8toVLDiMdD9m\noA6sFA7C7ZeoZ9I8H/UkWoLTJcDt9mvei6QJVbiRAyOhjaagDDpS+dntfCuXZ7asUAoaNlJZpMDz\nHGaNSzMVJc6qAikpIQ4bf3ETnpx4FY6+OBn7n78dB8vrcO87+zT71+sVMfnNPVix/yTcbr9iU0zr\nKwiS4QG5skzw6VdnItY+Zs+G7CjY7bylPmCVweFYD6nzCnceRLWddFZWFsnPP9zlTPD04HQJGPOH\n7eiRaA8KD0k7zqztcCh4vSJufXkb9i24OyxHoEjbD0cyv7bmJYoycktrTdtoR3rsmc2TMga1CSqN\nXaLVr7JMsGL/Sby7+yQSbBwyh/UFj4Ab99Ipo/DjFfkY3j8Fr04fHeSQ8sGBE5iVMQxJCYHxaLZ9\nzNC/bMpIbD5yOiLjmubbEfxAbcsfiXLVY9esnXSMSXcwnC5BCd4CBA8GIDKr6IZmL9KX7ETp4kmt\nttFGsMoAI8F8O9KhxEyZ9J4ek2wLvF4Riz/50nSeeowhFP1Ot4AXtxxDxQUnAGB4/xS8NiNdyROA\nwujrmjzIWLoLdgD3jbwMb8xKj5j4grXxjxSDfj73CF6ZNrrDGHV7zodu4cxCiHbQ+q6MlGRHUFQ/\ndhscKWbVu0dCWAy6PQKzt3ceVsoKJU5i6WGD14fK1wxEUcbiT77EkoduMM1g9Bi0UZvxPIeeKfF4\nY1YGNv5iImZmDQUHILckIIudtfwgsv64DXe8thN1TR707ZmIkhfuRMmLk3HjVf0VEdXqgoo29wsb\naS8SMCv/jxRC6WrUduHtNZZjK+kYAIQnD+8qK2lZJthQWAPCEczKMpYfq92sjURQVtzbgdYxRMJF\nqDZj74uijI1FNZiRFQgD63QJeOz9PJxrckOQeUXsJgiSEn42p7gKK/Oqse3Z26Iubk5n7LxouWxf\na/V9e62ko55JFxUVdTYZYaOzBpSV8ml8hZyiaszKSgMQuW2ulXRer4iEBHubZH9GTJB6hqnvhyrP\nDEM0EzdEj6GHKt9qexgxD5oXDZQky0Rh0I/87z58870biQ4ggQf+MedOJDniNGM6076KBL0dgUi1\nMTsWvF5RCbxkBWwe3ULcAUSPZtgq9LY/6i2SWZMxrXR6edN8Q1kysFprjnCmngkFs5pwNp3XK+Lu\nt/fB6RKCntXKQ89iQBAkzTMCaVpZJlj08bGgPOnZkUa06q2gWXqezz0S0o1by7pDr620rArMWkpo\nlcPK2OmLKiHBrjBah8OGj351Kw6/cCeG9UuGIz4eH5edxp2v78S8jWWKyZ8sEzhdAu5+W9u6JBJW\nEGagNW/02iUUTfQ+239GoiQgoLy9++19ll33wxaJmDGm7qxPZmYmWZNfEdVG8kY43+AihARO7/Z4\n/OTU2Try4aFTijt2dn4VWZNfoRwlpHc0j98vtWoH1hWZen79366viMslKGV4PH7FoUTtuUbLosdB\nUe826tlI09P8zbo60/xYTyzqJcnmUd/kIR6PnzQ2e4nH41eOnKJt1ez0kX//627y7fcXScW5eoXu\n57KLSbPTF+QoQ+k2Om37YqM7qA3odXpsFj2bkMLj8St1Z59pbPYG9eHyvSfIqoPlZOXBciUv+gz9\nrm/yKGOBXqdHRVGaaN3pMV1sf/j9Evnl3w4Sl0tQ+lwPNL2aBlonWp6eW7jPJ5LGZi95f9+35Bcr\nDpLS6jMk67dbicslkL/s/pr84oMDSp9pgT2GjJZHT1JnaaGejaHGlhqSJJNVeeVKmATW61HPw1DP\ni5I+73IJZE1+BWls9rYaF+r+8Xj8ZE1+hTJe2HHNtr3H49d0uWf7Dt3BmYUQoKSyoUuupuuaPMha\nthvf1TXj9ld34KYl/8CdbxzCX3Z9C7fHj0UfH8OU0VdAloCFm4/C6xU13/jU6kPdDjzPYerowVj4\n0VHM31SG7+qb8dI/KjD9L/uwfG853B4/Fn/yJaaOHgxBkJTVjyjKmL+pDM9vCqz+sgurMPOvhzDp\nrV2Y/NYeiKKMB0dcjh+vKIDTJeDR5XmYs6EYczeUaq74aPAaWQ5E1Ju1PA/ZhdVYlVeBBbmBFUpO\nUTXWFVRhQW6gfg3NXoxZshOzc4qQ/scduP21nbj/3QOYvb4Yz390BHe8sRO3vbIdO8tdmPxmHm5/\n/SDKz9UrZf6/Dw/j4ZFXgOc5eL0i7np7L+ZuKIXgl1qNFZ7nMOma/shYugvzWwLusO33X1u/QpPT\nh5tf3YU5OcXKyn7SG7sx4y8HMW9jqRKkZ9WhCoxbtgPv7T4Ft8ePORuK8dLnJ3DP9QORW1yLe97Z\nq+wGaH+ea3BhzJKdyFq2Gxca3crKe+XBcmQu2Y75m0rh9YqYt7EMk9/ag/mbyvCjHw7C5Dd34663\n9gbay+XF59/UY15uKR4ccXmrHQEFjeE8d2MJ5m86otBA7aPnbSrGXW/vVWIgsytuuqKUZYIXtxzD\n8r0n8cXxekz5czHOe4Hvmpux7ItyfHGiASv2f4NbX97WajUtCBIe/1uB4gsw+c09uP3V7Xg2uxhZ\nS7djdk5JIJ61S0DW0u1oaPZifm4Z5m86Ynr1LcsEZVWNuG/EICzYfARzN5RClkmr3QMdl263H3e9\nuVuT1pnvHcTcDaVY9PFR5J+8iLFLd8Dt8eNHPxyEe/+0Hw3NXtz11l40OX2Yt7EMk97cjVnvHUJh\neR14nsPzuUewLr8Kz+cG2vr5TUcwd0MJ5m4ow6Q3dmNdfpUiZqNp9PrOEGY4eWd9MjMzu6RLOH2b\nXmx0K0F0mp0+UnGuPmi1JUky+WD/yVYrT0JIUMAa9YqOBXvvTF0z8fnEoLc//Wbdjtk3/pz1pcqq\njb791Stpj8dPZmeXtKJPK2aDx+Mnqw9VkNnZJUGrNvXKesW+k8qqjV1x+XwiWX0wsFKpOFdPvv3+\nIjl+5oKyUmJXzHTFQk9En7hsB1l9UHvHQftCq/0Iab2SdrkEsuZQZasDU9WrX3oIrFa701XxxUZ3\nq5W03y+RlfvLW9WFPsuuegkJ7Mq0VmPqE97Z1TdLg8fjJ1Pf3a95aK2arueyS8iH+0+Ri41uUnGu\nnlxsdAfG6r6T5PiZC2Togq2aB+BKkkxW7D9FpvxpP1l5oJx8sO8keXZdsbJjWpd3KQATPWjW6kqa\ntg2NM6Ielywt2flVpNnpI2N+90XQCp/ep/1LaaCrfZq3JMlk1YFysq6gUulfdr6ZXUmzwZ5YWtEd\nYndkZmaa7beogd8vkefWF5PnmMGjFyHL5RLI6N9+3mr7TtOuzaskHo+fTHl3v+5WnjJyI3p+s66Y\nTHxph+YWVW+AW0mn9YIJNfHMRCXz+yUyO7uE/GZtYatofYF6FZGbl24nfztwSbxjJeZJqPaLVKRB\nrbzYoExW86OiL3UkOvW4U9NgJCYxQxu91tjs1WX2tB9WHwq8aNUvOfXvcNtYS5yjR6+aQWvRo5U3\nTRPJccDCLJOOanEH0PUUh4EYE2OCvLpYe1EqvgACJz7PvetaJCXG6Zpx2e08pmek6mqfOWKsXbbb\nebw+Ywy2M+ZUbJtq5atHh146WSZBohq9QwbUyC6s0tz60Xztdh5Lp4yEjQ9YZLBKF9rOT996FeJb\nTtxOSLBr0s5u6dUwar9IWOZQcy0qEqJ0UNGCVcgyUURf1JabWmCox526HmbN/9jTxbXy6ZkSj54p\n8ZrP0r4vLL+Ax1fkI6eoBg3NXgiC1EqZ2RbbYvXcUoO1omD9ErTqo5c3TdPpMUnMcPLO+mRmZnZ6\nXNpIQ72aNlrFUTGCXhr1lthMO1mJTme0NVZDb0VDlTFq5Q1dJa/LM94JsHnrre7MhjnVivXcEWOL\n7UuWnnAR7SJAur13uQTywb6TZNiCreT+N3crymK91XUky48mvqFHD0yupKPeTvrw4cLOf5NZgFnb\nZDZ2ghkXZfXBm4Ig4c7Xd2FgryRsePpG2O28YutsxktOz7SMXm9y+pCxZAdKFk/WXTWFAs1j7uTh\neGd3JQoXTUZK8qWDXbXOd1TT1tbzHVnoHWDaEehsm/mOBq2vLBPUNXvwb38rQE2DDw5OwuB+vfDe\nT9LRv2dS2G2i1Z5smcCl1Xo0tLsWHd3GTroruYTLcuBIKmpFoZeGhkvU2u6xW2K1fSu9v7G4BnY7\nj1/dcQ3W/2yCso0NJfpQD171PZaWninxKFk8GUkJcWGLnGgev7jjhwqDZm1c1dtqNQ1aNq6sLbjV\nrTJ7JmIoO3azdrhm0RZmFArUOUXvWSuWE5ECG/+jf68kbPrlLZgz+Rr8ZvL1+MvjYzDupT1Yvvsb\nxfrBKp3ZhVVBY0mWCVbml8PrFZXzMNViJr28wrlnlEbrWpteFGaW2531ycjIJFP/fMC0wqOz4fOJ\n5KG395KH3tlL/nNVge62VEuBQn9rHcOkt0XXswvVK5M92cXIrpQiYJlSZEokYQZUUafe+oeiQasO\nWmnNgBV7aNWfWqyoy+mM7bOZcn0+kdz/5i7y/t5vTY2ltpTVVrCiqZoLjeS6xZ+R+iYPeWbNYdLs\n9JFn1xUZ2mBTUKskarkiSbKihP/N2mKFX2iJmdT0GB2wEao9tNJYaUd0F3HHvn15uoL/aIQgSHD7\n/Ljjjb1B4UjNQk/EEQmwWy6zYpnnNx3BS4+MikjMCVou0LaVZSjxUCiYEaGo84rEtjmcPEI943b7\ncePLO8BxHA49PylovFkdS+0tGlDTQSNC3vjyDtzyg/4oP9eMygse9EyyY+szt6B/r6SQebE0u91+\nOBw2SyKtto4nI7FLKHSL2B2ZmVnk1vnv4dXpY6IuHgALrU5xu/261gZ0i8cyPrWjCptOFOVWsafV\nFgtUtqsl03V6BDhstiDrDiN5Hd1GOhw2RSvPyo4pXXY7H/TN0qA1UdS0q+nQe4mof7P50OeoGGnq\n6MGa4TG1GC7Nx+sVAwcKtPQHbVOajx59NI0WjbRd2GvZhVWYlZmmtK1RvWkerKWM1rgBoBwikZQU\nF8SY6W/13KF9Sumg12h91e2n7mt6jRVV0PZj6Wf7ye3xY/GWo1jy8Cg44gJlegURPVPi8X29Ew6b\nDfe8sQt9kh2odwto8gEFi1pHcmTHIy2L0q5uf0oXjVfC0sfmYzQ+1P3MlqUnDhEECQkJduV5rTEi\nCBLi4+2mmHR0hbjSAIfOF/obgco21SZ0CQl2zeuyTLCuoAofldZi/c9uVAbOhsIaSJBBZODH44cC\nALILqpFdUI7TjQIOzr8TCQl25BRVgyMcZo5NhSwTzN9UBhBg3PB+mDpmMLYc+y4oUPlzOcXYeuwc\neiXYMGfyNbDZeJRU1SM9rTdKqxuQObQPOI6hjxAUVVxEdb0H656agNwjtSiuqMfYYX0xa1wgANPa\nw5X4qOg0HhpzBT4p+w4PjbkCW0pPY1j/ZGQO7YOymka8PG2UMtk3FtdgWvoQbCypUWgHoFzPLa1V\nvqmJIhtjm/2dXViFwop6cIRg3PB+mJGVik3FtSAcwdRRg7Fw81GkpwbXjecCZbJmX5QejyDirW3H\nceXAHtjw9E3geQ7zN5WBA4elU0Yq9WfbiwAorqwDx3F4Zfpo8DwXRCNtn0cyhuDxCUOVcosq6uEX\nZXxSdgZrnhyHT786o1nvaelDkF1UhdzCWgwfEAjSz/Mc1h6uxObi77D+5xOCGLU6NvnU0YPxfG4Z\nKi+4MHxAD7zM7IQCnnaHkNYnEbUNXqz/+Y0AgJnvHcIj6UNQdroBL00ZpQTql+WAeeWSh25QYmED\nwLyNpTh1tgkVF9zgeQ5XDuyB9T+7EVuOfYdp6UOwvrAaRVUXwREOo1J7YfneUyCE4K43d2NAzwT4\nRT++Pe/Dnnm34JZX9+Pa/vE47wXi4oFf3nEtSqvq8fvP/olXGJNCSvuQng6MGdoXX55pwughvfHB\n/gpkDu2LccP7ggAoOHkOxdX14DgeY4f3x9KHb8BPPjyMdf8+HluOfYeHR16BR9/Pw7B+yRg7rC9s\nPK85PnKKq1FUfqmfZZlg1vI8TM9IxayxqdhYUgOiEqMLkoQVByrxxTO3YPOx0yipbMCyqSNht/PK\nGBFFGY++n2+eyZiRiXTWp6ucFs7Kn6iJETU900tPPZeomRt1AlAfD+/ziYr5mtpzj5BLXk7UC0vt\njebziaS+yUNcLkF5nvXwo7JZSg+NvcF6C6odSeg16qzAPk9/s6DeXKxMktaDXtOql1b70nT0WerN\nxuahlbc6D/ajdrr4/+2deXgUVbr/v1XdaZombAEXBBJwrg53kC0LqyCbXh1XZHe5jjo6y9VRRxCQ\nmfE314uA4CjO5gajggQIq6KMLLIv2RPABVTIAihbEkLS6VRX1/n90TnF6Up1d3V3dae6U5/nyZNe\nqk+dc6rqrbfe8y50DlbmljXbF/3PRsqp7a++XiDLD5z0GS+dK5qDg/2NctzsftjvtNhr2WPP2m0p\ndLys7XbFgTKfiEXlOU3nhZ1neg7U1wtylXP62+UHTvpE89F8LB/sO+FzjRDiddNko0/Z84nF4/FG\nPWb9+VPS/6Ut5J/bvyLVtQ3y7+nvnl1ZKEeGKqM06biUOWbUAljUvmPbVJ5r9D97vdLziD2/6edI\nhGAWQoA5G42f9J9qyjTnw71/3YX0edtR51QvGEofs57Nzkf6vO2orWsEAGw6cka+69LtrFYen371\nozcvcJM3AltMkz56bTpyBvPv6+fzyD5jbTHWFp1Ch3Zt4HAkyZrC7A2leGDpIbmUlNeLohTZeeWY\nvf4wcgorsb7ktPzYuKH0dLP+SxLB7X/dKz/CsbbADaWnfVbdP8w9gaz53rwIbHDHmoJKrMmvlN+v\nLbzi9cKOUWm2YB9hRy/ajiELdqDOKSCnsFKeWzov9LdKTw7qAcDz3io5dN4kiWBD6WnwPAeOXGlL\nkgjmfnwULpeIB5bl+pgF6FhW5VXI49lw5DSKyqvl8awuqMD6Yu+8UA2aauAUejxoPzcdOePzCJ9T\nVImNpWf8Pmaz80bNDzabpVmOjj98chTrik75nGfTh3ifkuZsPIwX1pb6eIbM/fgoBMGDdSWn5DHS\nY07Pq/Gv7cDAeTuw/OBJiKKE4ooaef7XFZ8Cz3P45MsfkGS50i/q2tkhuY38nprk6DFQnncPZKXh\nupQO+OSpYViw7QSGzN+ByW/twYeHTsjXw+DeXeBomwS73SqPkR5f2qbVysvn6erCCjmvjPL8YFO1\nShLBhsOnsbbwlM+xYv+LooSXPv3Kx1RFM0zS76l3lma0SPKW+ktPz/Abm29UlFpCIFhNmpDAgS3s\nfy2h3G63hzy7srCZVku/YzULryZ10if3AM0yptTOlWMN1F/2vTKYRanhqmm7WqAaWrDfhOoJo7YN\nm1dDub3aeJRBG2rtKlMG+HuCULYRrO+BCBSSrpZLQ3muqJ1/NJOh2jlKtflQQuGDebQQ4tXAT1+s\nJWmzNpN+f/qMnK+p9zkfgrWnfKrTsn+14xjs+lQGY9H3SARNmuOABTrWmIsFdrvVR0sIhNXK+2zn\nb0WYXdxg79ysRqXMbcvzHAb37qI6d+wiGW3bwvM+WuqUrJ6w2Sw+9ky1sQbqL/ueDWKhnykXbvwt\n9AXCbrfKbQf6jXLBT8u2ysUhqjlvOuKrzbJjYdtlnyjUFqnosaSLWJMzfMP/1eYx2LxomTd/Yd/0\nO+WiLx0H/Q2r7VPsdis6tbf7aKryGEu8aw7sfrXk3/YHPR87JLfBdSntUfTiWIz8SQrGvrYLtyza\nhpsXfYGq2gY4nW7U1jXKdmZln9njodUjRO04Kv8r26Lzpbx+NaNFkrfUXyKGhQeDtfcpP6f/6R+b\nK3r4vK3NNDyl5qJm21X7TonW7dh9KrOAsb9X08iUWkYgzUdLP9Wgc+Yvixy7D/kJ49DJgPOoJcFP\noO/q6wXNTyuhrM+wGp8/22uw/iqzudHP/OVnpt+zbXk8zX2VGxtFOWlYONc2m22Q/r6hwU2WNWXd\nO1tdRwb/+TPSZ/Zm0nvWZvJD1WV5TUQ5Vva8U15XgeIzAj3V+tuWvqbvkQiaNADdSsHHA9RNa8aa\nYkx796CsbVBNgOZmBnyTNfE8h6s6tG3mRbK28JQccSWKkk+UVnZ+uY8dzt8cs1F/rEbiN5/xulJ8\ncOAEbn1jF5bnnvSxpVPb7Ko83/zS7Pho/mO1SMxAEYZqVUzYyE+aQ3nCgO5yTl9/0Zy0z6IoofBk\nNbILymVbMztXwap++JtTdrx/3PwlXr6rbzM7Pu0zzQPOvg6GbCfPr4TLJWLquwex/GAZZq4txcy1\nJar9Vc6vKEp4PqcYty3ZLZ+HoihhRk4JRry6Q3b7UzsGNOqPao+s5wRwJWkYtcmHolX6VPFZd1hO\n0rXpyBk8PKw3Fk8eiJTkthjyk6sxvu81+K+fdcW4xbsxfOF2LNvzLVwuUZ4fepwFwYMX1pVidZ73\n2mI9MPxV+mHP12BrZqw7X0JWZklk/GmLapo0tRN/xET/Bbvrs1rrRwfLfNJY+rMtqqGWhtRfFBvV\n1pbvP+mzL7UnAT01aVZTpPmAb56/jTzTtNI/csGOZvmela/ZfahpWFrmRiv+xsseU0KMoUkrP/OX\n+pPuS2lbp22rRYuGE02spkmr9Z3+XbrsIv+zPI/0nbuZ3Pn6F3IqVTVPDeVYgh13rZo0e53S10iU\nfNLxtGgYCsqTVutvQlkAU26n51xqMS2wr9XCZ7UIPi0o22cX+Pwt9oXSdih90GOOIzHvGcE0GGjR\njSXQzT5cqFugkoYGN3lqRQEZ/spWbwj6RwXkvr/uIe/vO0GqaxualfdSmj6U41A73wLBthfqwqHh\nIw5Hz3pPzpmbaARaoEg0lFF1a/IrQTjSLGsffWT2d8z9zVk05tJfoJI/qFtWS5noQu2vEdAz/YHL\nJWLy2/twqqZRNSUDNXHRqvSC4MGzq/Ow9VgN2vDAmBs64c0Hh/oEngDNI2TZ18HOVyVsGwkRFp6Z\nmUkOHcpLSAGthVgJcb32w65gB2svkJeFvwtXTQix7bhcImw2i65CPNTfBQoFjgXxcuPXOzcNPTfu\n7XcdJIloyplTW9eIUYt3Ytj1nUEkCYfKLuGZcTdgcnpP2JIszcLj/d0Awx1LwqQqTUQBrVw4UFtI\nCGWRIeSFCAZRlIKmc9SyD+WiW7DtqSuT2nb+jjldLGUFNF0gczrduPWN3XLxT7W+BXP70gMaRh0o\nHSrdDoDPoqUe+LtBBUOv/Wsh2IKr1jYA35v05IyesNutmgS0yyXiT58cxa7nR+OvD2ThjWlZ2Prs\nSBSXV2HYgm14ekUulu45jpnrSrDiUBkAyAFISqIuo7TYRFrqLxEWDpW2V7UFlEDpErW0H66bosfj\nTR26MkgqUq378LeIE267WhZn2PlVhlGzaE3ZGUr//BFsMZF1BaTud9FyNdUyhkjOoXCJxA5Nbdnh\nzpvb7SG/ff8guW3RVrJsv7cg8u9W5pObF2wn1bUN5Dcf5JK0WZvJjXM2k7PVdc3SouoFEsEmnZmZ\nSQoKClq6G2EjSQSr8ipAOILpWWmqti219+HsJ9zfawnu0LqPcPqh9htqMpi1/jD+eEcfvLzlG002\nv2A22WCPpf76EsmxCmZDV0u5qTdajnFLmUnCNRVEMm919QIy5m1DowTc3e8avD41XW6T2qvrGrwp\nHT796kfc3bcbOiS30d1EkzDmjnhHlCTAz1Mde/GoPW4GexTWKmADoSWKTcs+1HybA+Gv71TQ8jyH\nOf91I+762z788Y4+8gXJbsf6RNO2Ai2aKS8wLX7YSgEd6hj9ba+MTou2gAzW75YS0OGaPSKZt+R2\nNhTOvRUlc8fh9anpcpQljaC1Wnl0aNcGn315Fge/PY87/74PTqcbOUXhFc2NFMMLaaMnVwqEJBGU\nVNQAaH6hK4M8lBcRPYH9CT+2nZZGFCXM2XgEEwZ013TRsMEEyv5TQSsIHkx46yAGdO+A//30a595\noE8oM9aWYOba0maC2t8+2f4q51zN3q38ndpNIJjgM4Knhb+x+SNWgoitdh5rktvZ0Km9PeD6x5TM\nnhjxH1fj0/+5GXa7FRzhUFvfKAcUKQN6Aq09RIQWm0hL/aWnZ+juRxlrlNWRCVG33arZugIFdtDv\njRI2H6qt96ODZWT5gZM+qVnpd5TLdY1yyknld1qCM9ht6TyxvrmB/HnVymgFajdeYO3hgb4PZV0h\n3H60BMHCvZWwaxlnq+tI71mbyfB5W8npi7Xkpj9sln2y/cUCBJpLJIpNOp5d8ERRwsx1peAIfJKX\n60kobm/sb4DoVVMOxQVP6a5GK55LEsHza4pQVHEJ6akd8dqU9Ijmj+2TFtui1nmNF5c3Fm84c6nf\nikdaxxSuXzZ7nGPtnrg89yTW559Czq9HaC4JR5+8pr57EG8/OAifHP4B/9pfBo/kwSdPXSnzpWVN\ng0WrTdrwlVniVUAD3r4vmjhAfh0NqCDRerHQC4QjHCZl9NA9+EJrX5SZwyi04rnVyuO1KemyQI10\n/tj9aGlLrX/KxT76vV6LiZF8F8o2PM8BAXQzrWOJxJwTrLJ9tOAIB44P7Vyi6yGT0nuiawcHHr35\nJ5iemYaP8k5i5KJdyJ8zHsntbKrzoMt1pUXdbqm/RA4L15tww8uj5fbVEr+NJkq3OWUeaD3cDsP9\nLpRt6HYrNbhdRhOt7px6tcdu5y/HupbfKs0Zly67fEweofQRemXB4zhuGcdx5ziOO8p8lsJx3DaO\n475t+t+56XOO47g3OY77juO4wxzHpTO/eaRp+285jntE2w0EfrN2GRXaVy191rINXSgLRqA7Xtfd\nagAAIABJREFUNpsNjm7rT5ONBL28TbSgtugYTajWSHNss5p0KNpkoO3D+Y493ySJaOoLz3OYOti/\nqSFUL5ZwCGZC0suLRm2/SjNHKF4myv2sLz2NYQu91ZWW554M6IkV7kK/Fr3/fQC3Kz6bDWAHIeQG\nADua3gPAHQBuaPp7EsA/Aa9QB/ASgCEABgN4iQr2YBi9EC0Lm6ox2EHXcmKwEXXhXjAul4jxb+zC\njJzSqAq1WFzYFK+tv7lnR7RRus0pPw+1nXC/Y6MV6flG50Pr/AcTki3plaLnjU8LWr1MlPvheW85\nr9E/vRrrSk7hvd0nUOcUfCJbBcEjF3WYMKB76An/oTF3B8dxvQBsJoTc1PT+GIDRhJAfOI7rBmAX\nIeSnHMe93fQ6m92O/hFCftX0uc92/ojHhUNqr9SyOKW2jVIb1UM7dblEXey6wYjlIhoVzvF0buiB\n0ubPnm9A5PMhihJmri3xu6ioB/G42BoIOvcfHjyJnNyT8EgebH52PABg0lt7YeEtyGYqqdN5jXYw\nyzWEkB+aXv8I4Jqm190BVDLbnWr6zN/nzeA47kmO4wo4jis4f/583F2EtL9a+q0moLPzy7Eqr8JH\nOEd6QrMFOaNJLC+8aN10qNlASV29elHhWMPznE8OCfZ802s+1J5eQ9H+As0VfTpsiaAQip5PX+zC\n9qRBPXDsQiOOV4k4cbEagtuDXl2S8a9HsmC3W5uVR9NKxEe1yQCu24wTQt4hhGQSQjKvuuoqvZo1\nLMoTxuMh8EjxY4OPFbEyo9AKHez+6uoFZM3fbghBLUlh1MgLAauVb+Yu6s+UpfZeOVdq/fR4WlZA\nR5rcyV9bHZLboODFsRj3Hx1w+5JcjFm8HcfOVOOhpQc0VdTxR7hC+myTmQNN/881fX4aQE9mux5N\nn/n7PCiRDM7oqJVvKi6vRlFFjaaLMGoRTiGitrgZST+U7cXK3m218nh14gBMzvS1bya3s8luVi1N\nLKIY1WzuygxwSo2Yvne0TZLnSu24SRJBscbzWw+UC8zU/qzH/Clt2ZJEsO3Yefz9oWHImz0aWb27\n4EytgAt1At4/8C3eP3AirEyM4QrpjwFQD41HAGxiPv/vJi+PoQAuNZlFPgdwG8dxnZsWDG9r+iwg\nhBDNdd3iEeVBtlp5LJo8EIs0BL74uwAiFWah/lZtcTOSfqi1F8vwap7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W1QtwtE1qJuT1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116508358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.spy(s, precision=0.01, markersize=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong><em>Optimal</em> solution with objective value 0.982</strong><br><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>fluxes</th>\n",
       "      <th>reduced_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DM_4crsol_c</th>\n",
       "      <td>2.1907e-04</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_5drib_c</th>\n",
       "      <td>2.2103e-04</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_aacald_c</th>\n",
       "      <td>0.0000e+00</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_amob_c</th>\n",
       "      <td>1.9647e-06</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM_mththf_c</th>\n",
       "      <td>4.4010e-04</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN2abcpp</th>\n",
       "      <td>0.0000e+00</td>\n",
       "      <td>-0.0083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN2t3pp</th>\n",
       "      <td>0.0000e+00</td>\n",
       "      <td>-0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN2tpp</th>\n",
       "      <td>3.3499e-04</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNabcpp</th>\n",
       "      <td>0.0000e+00</td>\n",
       "      <td>-0.0083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zn2tex</th>\n",
       "      <td>3.3499e-04</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2583 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<Solution 0.982 at 0x11948a978>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.optimize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes a solution cannot be found. For example, setting the lower bound of the objective function to a very high value that the model cannot achieve will trigger a warning when trying to optimize the model. Parameters reported from an infeasible model are not meaningful to interpret (except in rare occasions when you may want to figure out why a model is infeasible)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cobra/util/solver.py:419 \u001b[1;31mUserWarning\u001b[0m: solver status is 'infeasible'\n"
     ]
    }
   ],
   "source": [
    "infeasible_model = model.copy()\n",
    "infeasible_model.reactions.BIOMASS_Ec_iJO1366_core_53p95M.lower_bound = 100000\n",
    "sol = infeasible_model.optimize()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:cell-factory-course]",
   "language": "python",
   "name": "conda-env-cell-factory-course-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}