{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict expression modulation targets\n",
    "\n",
    "Cameo provides algorithms to search for genes or reactions that can be over or down regulated in order to achieve a given biological objective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cobra.io import read_sbml_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the E. coli core model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = read_sbml_model('data/e_coli_core.xml.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Succinate production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The production envelope looks like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cameo.flux_analysis.analysis import phenotypic_phase_plane\n",
    "production_envelope = phenotypic_phase_plane(model, \n",
    "                                             variables=[model.reactions.BIOMASS_Ecoli_core_w_GAM],\n",
    "                                             objective=model.metabolites.succ_e)\n",
    "production_envelope.plot(height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flux Scanning based on Enforced Objective Flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cameo.strain_design.deterministic.flux_variability_based import FSEOF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fseof = FSEOF(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = fseof.run(target=model.reactions.EX_succ_e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>ADK1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.4103</td>\n",
       "      <td>1.5273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENO</th>\n",
       "      <td>15.2389</td>\n",
       "      <td>15.7616</td>\n",
       "      <td>16.2124</td>\n",
       "      <td>16.5775</td>\n",
       "      <td>16.9427</td>\n",
       "      <td>17.3078</td>\n",
       "      <td>17.7044</td>\n",
       "      <td>18.6023</td>\n",
       "      <td>19.2933</td>\n",
       "      <td>19.6280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBA</th>\n",
       "      <td>7.7569</td>\n",
       "      <td>8.0365</td>\n",
       "      <td>8.2425</td>\n",
       "      <td>8.3610</td>\n",
       "      <td>8.4796</td>\n",
       "      <td>8.5981</td>\n",
       "      <td>8.7478</td>\n",
       "      <td>9.3931</td>\n",
       "      <td>9.8308</td>\n",
       "      <td>9.9109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRD7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4532</td>\n",
       "      <td>3.1753</td>\n",
       "      <td>4.8974</td>\n",
       "      <td>6.5867</td>\n",
       "      <td>7.7551</td>\n",
       "      <td>9.1278</td>\n",
       "      <td>10.8523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPD</th>\n",
       "      <td>16.4311</td>\n",
       "      <td>16.8387</td>\n",
       "      <td>17.1736</td>\n",
       "      <td>17.4220</td>\n",
       "      <td>17.6704</td>\n",
       "      <td>17.9187</td>\n",
       "      <td>18.1984</td>\n",
       "      <td>18.9767</td>\n",
       "      <td>19.5478</td>\n",
       "      <td>19.7620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPS</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4103</td>\n",
       "      <td>1.5273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUCCt3</th>\n",
       "      <td>1.4746</td>\n",
       "      <td>2.9491</td>\n",
       "      <td>4.4237</td>\n",
       "      <td>5.8983</td>\n",
       "      <td>7.3729</td>\n",
       "      <td>8.8475</td>\n",
       "      <td>10.3220</td>\n",
       "      <td>11.7966</td>\n",
       "      <td>13.2712</td>\n",
       "      <td>14.7457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THD2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1197</td>\n",
       "      <td>2.1438</td>\n",
       "      <td>2.9168</td>\n",
       "      <td>1.5352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TPI</th>\n",
       "      <td>7.7569</td>\n",
       "      <td>8.0365</td>\n",
       "      <td>8.2425</td>\n",
       "      <td>8.3610</td>\n",
       "      <td>8.4796</td>\n",
       "      <td>8.5981</td>\n",
       "      <td>8.7478</td>\n",
       "      <td>9.3931</td>\n",
       "      <td>9.8308</td>\n",
       "      <td>9.9109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX_succ_e</th>\n",
       "      <td>1.4746</td>\n",
       "      <td>2.9492</td>\n",
       "      <td>4.4237</td>\n",
       "      <td>5.8983</td>\n",
       "      <td>7.3729</td>\n",
       "      <td>8.8475</td>\n",
       "      <td>10.3220</td>\n",
       "      <td>11.7966</td>\n",
       "      <td>13.2712</td>\n",
       "      <td>14.7458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1        2        3        4        5        6        7   \\\n",
       "ADK1        0.0000   0.0000   0.0000  -0.0000  -0.0000  -0.0000  -0.0000   \n",
       "ENO        15.2389  15.7616  16.2124  16.5775  16.9427  17.3078  17.7044   \n",
       "FBA         7.7569   8.0365   8.2425   8.3610   8.4796   8.5981   8.7478   \n",
       "FRD7        0.0000   0.0000   0.0000   1.4532   3.1753   4.8974   6.5867   \n",
       "GAPD       16.4311  16.8387  17.1736  17.4220  17.6704  17.9187  18.1984   \n",
       "...            ...      ...      ...      ...      ...      ...      ...   \n",
       "PPS         0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   \n",
       "SUCCt3      1.4746   2.9491   4.4237   5.8983   7.3729   8.8475  10.3220   \n",
       "THD2        0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.1197   \n",
       "TPI         7.7569   8.0365   8.2425   8.3610   8.4796   8.5981   8.7478   \n",
       "EX_succ_e   1.4746   2.9492   4.4237   5.8983   7.3729   8.8475  10.3220   \n",
       "\n",
       "                8        9        10  \n",
       "ADK1       -0.0000   0.4103   1.5273  \n",
       "ENO        18.6023  19.2933  19.6280  \n",
       "FBA         9.3931   9.8308   9.9109  \n",
       "FRD7        7.7551   9.1278  10.8523  \n",
       "GAPD       18.9767  19.5478  19.7620  \n",
       "...            ...      ...      ...  \n",
       "PPS         0.0000   0.4103   1.5273  \n",
       "SUCCt3     11.7966  13.2712  14.7457  \n",
       "THD2        2.1438   2.9168   1.5352  \n",
       "TPI         9.3931   9.8308   9.9109  \n",
       "EX_succ_e  11.7966  13.2712  14.7458  \n",
       "\n",
       "[18 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CSgksK9rLkl62CC9UcNnCkrLEpXjVLOHJMoWkzhXXPK9VKSRZynptZplFC26xcA5YcUdk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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170ce9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "result.data_frame.T.plot()\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential flux variability analysis\n",
    "\n",
    "Compares flux ranges of a reference model to a set of models that have been parameterized to lie on a grid of evenly spaced points in the n-dimensional production envelope (n being the number of reaction bounds to be varied)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cameo.strain_design.deterministic import DifferentialFVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up a model that represents a reference state (in this case a model with a constrained growth rate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.reactions.EX_o2_e.lower_bound = 0\n",
    "reference_model = model.copy()\n",
    "biomass_rxn = reference_model.reactions.BIOMASS_Ecoli_core_w_GAM\n",
    "biomass_rxn.lower_bound = 0.\n",
    "target = reference_model.metabolites.succ_e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the differential flux variability analysis strain design method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_fva = DifferentialFVA(design_space_model=model,\n",
    "                           reference_model=reference_model,\n",
    "                           objective=target,\n",
    "                           variables=[biomass_rxn],\n",
    "                           normalize_ranges_by=biomass_rxn,\n",
    "                           points=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run differential flux variability analysis (only on the surface of the production envelope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.26 s, sys: 28 ms, total: 1.29 s\n",
      "Wall time: 1.41 s\n"
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       "  \n",
       "\n",
       "\n",
       "\n",
       "  <div style=\"height: 500px; width: 100%;\" id=\"hsiqwsmnpn\"></div>\n",
       "\n",
       "  <script>\n",
       "   /* Closure to make sure data only applies to this map. */\n",
       "   (function() {\n",
       "       /* data from python */\n",
       "       var data = get_data();\n",
       "\n",
       "       function load_lib(url, callback) {\n",
       "           /** Dynamically load a JavaScript file */\n",
       "           var s = document.createElement('script');\n",
       "           s.src = url;\n",
       "           s.async = true;\n",
       "           s.onreadystatechange = s.onload = callback;\n",
       "           s.onerror = function() {\n",
       "               console.warn('Failed to load library ' + url);\n",
       "           };\n",
       "           document.getElementsByTagName('head')[0].appendChild(s);\n",
       "       }\n",
       "\n",
       "       function check_global_and_load(name, url, callback) {\n",
       "           /** Look for the name in the global scope. If it isn't present, then\n",
       "              load the library at url. Returns the object. */\n",
       "           if (window[name] !== undefined) {\n",
       "               callback(window[name]);\n",
       "           } else {\n",
       "               load_lib(url, function() {\n",
       "                   callback(window[name]);\n",
       "               });\n",
       "           }\n",
       "       }\n",
       "\n",
       "       function draw_escher(escher) {\n",
       "           /** Get data and draw */\n",
       "           var load = function(t_map_data, t_model_data, t_options) {\n",
       "               escher.Builder(t_map_data, t_model_data, data.builder_embed_css,\n",
       "                              escher.libs.d3_select('#' + data.id), t_options);\n",
       "           }\n",
       "\n",
       "           // for the static site, use the url attributes for the map and model\n",
       "           // information\n",
       "           if (data.static_site_index !== null) {\n",
       "               escher.static.load_map_model_from_url(data.map_download_url, data.model_download_url,\n",
       "                                                     data.static_site_index, data.options, load);\n",
       "           } else {\n",
       "               load(data.map_data, data.model_data, data.options);\n",
       "           }\n",
       "       }\n",
       "\n",
       "       // Load dependencies and draw\n",
       "       if (typeof define === 'function' && define.amd) {\n",
       "           // require.js is available: use it to load escher\n",
       "           require.config({\n",
       "             paths: {\n",
       "               escher: '//unpkg.com/escher-vis@1.6.0/js/dist/escher.min',\n",
       "             },\n",
       "             // Override urlArgs that adds ?v= to the end of the escher URL and\n",
       "             // messes up unpkg links. Honestly, I'm not sure if this will lead\n",
       "             // to downstream bugs in the notebook, but they really shouldn't be\n",
       "             // doing this cache-busting anyway.\n",
       "             urlArgs: null,\n",
       "           })\n",
       "           require([ 'escher' ], function(escher) {\n",
       "               draw_escher(escher);\n",
       "           })\n",
       "       } else {\n",
       "           // require.js not available: dynamically load escher\n",
       "           check_global_and_load('escher', '//unpkg.com/escher-vis@1.6.0/js/dist/escher.min.js', function(escher) {\n",
       "               draw_escher(escher);\n",
       "           })\n",
       "       }\n",
       "\n",
       "       // Put this at the end so Chrome doesn't cut off the code above.\n",
       "       function get_data() {\n",
       "           /** Return the data passed in by the Python module. */\n",
       "           return {\n",
       "               id: JSON.parse('\"hsiqwsmnpn\"'),\n",
       "               map_download_url: JSON.parse('\"//escher.github.io/1-0-0/5/maps/\"'),\n",
       "               model_download_url: JSON.parse('\"//escher.github.io/1-0-0/5/models/\"'),\n",
       "               options: JSON.parse('{\"menu\": \"zoom\", \"enable_keys\": false, \"enable_editing\": false, \"scroll_behavior\": \"none\", \"fill_screen\": false, \"ignore_bootstrap\": true, \"never_ask_before_quit\": true, \"reaction_data\": {\"ACKr\": 69.09337446382155, \"ACt2r\": 69.09337446382156, \"ATPM\": 58.18178441130317, \"ATPS4r\": 25.715626422965414, \"BIOMASS_Ecoli_core_w_GAM\": 0.7883370502646895, \"CO2t\": 68.953606355781, \"DM_succ_e\": 67.45054000000039, \"ENO\": 146.58691038864825, \"FBA\": 74.20406927436835, \"FORti\": 50.01838118113272, \"FUM\": 67.4505400000003, \"GAPD\": 147.84542455904526, \"GLCpts\": 75.04086342229213, \"GLUDy\": 2.779574862947979, \"MDH\": 66.08959221157905, \"NADH16\": 67.45054000000042, \"NH4t\": 2.9946832007626702, \"PFK\": 69.42656927436856, \"PFL\": 69.1283811811326, \"PGI\": 74.86840660081393, \"PGK\": 147.84542455904523, \"PGM\": 146.58691038864828, \"PIt2r\": 2.9000555068087124, \"PPC\": 68.50028620000796, \"PTAr\": 69.09337446382118, \"RPE\": -0.5666566717301775, \"SUCCt3\": 65.85804000000046, \"TALA\": -0.1410334982923121, \"THD2\": 4.8692044231578375, \"TKT1\": -0.14103349829231213, \"TKT2\": -0.42562317343786493, \"TPI\": 74.20406927436841, \"max\": 828.17233193739, \"min\": -828.17233193739}, \"metabolite_data\": null, \"gene_data\": null, \"reaction_scale\": [{\"type\": \"min\", \"color\": \"#D7191C\", \"size\": 24}, {\"type\": \"value\", \"value\": -224.68664432896196, \"color\": \"#D7191C\", \"size\": 21}, {\"type\": \"value\", \"value\": -112.34332216448098, \"color\": \"#FDAE61\", \"size\": 16}, {\"type\": \"value\", \"value\": 0, \"color\": \"#FFFFBF\", \"size\": 8}, {\"type\": \"value\", \"value\": 112.34332216448098, \"color\": \"#ABD9E9\", \"size\": 16}, {\"type\": \"value\", \"value\": 224.68664432896196, \"color\": \"#2C7BB6\", \"size\": 21}, {\"type\": \"max\", \"color\": \"#2C7BB6\", \"size\": 24}]}'),\n",
       "               builder_embed_css: JSON.parse('null'),\n",
       "               map_data: JSON.parse('[{\"map_name\":\"iJO1366.Central metabolism\",\"map_id\":\"c50080dc494c0686982966090cefc2de\",\"map_description\":\"Central metabolism (iJO1366)\\\\nLast Modified Mon Oct 12 2015 17:39:00 GMT-0700 (PDT)\",\"homepage\":\"https://escher.github.io\",\"schema\":\"https://escher.github.io/escher/jsonschema/1-0-0#\"},{\"reactions\":{\"2075839\":{\"name\":\"Malate dehydrogenase\",\"bigg_id\":\"MDH\",\"reversibility\":true,\"label_x\":3019.1480390625,\"label_y\":3883.49973125,\"gene_reaction_rule\":\"b3236\",\"genes\":[{\"bigg_id\":\"b3236\",\"name\":\"mdh\"}],\"metabolites\":[{\"coefficient\":-1,\"bigg_id\":\"mal__L_c\"},{\"coefficient\":1,\"bigg_id\":\"h_c\"},{\"coefficient\":-1,\"bigg_id\":\"nad_c\"},{\"coefficient\":1,\"bigg_id\":\"oaa_c\"},{\"coefficient\":1,\"bigg_id\":\"nadh_c\"}],\"segments\":{\"675\":{\"from_node_id\":\"2076049\",\"to_node_id\":\"2076047\",\"b1\":null,\"b2\":null},\"676\":{\"from_node_id\":\"2076047\",\"to_node_id\":\"2076048\",\"b1\":null,\"b2\":null},\"677\":{\"from_node_id\":\"2075291\",\"to_node_id\":\"2076049\",\"b1\":{\"y\":3678.094009828363,\"x\":3014.9175703125},\"b2\":{\"y\":3828.010534354759,\"x\":2996.16659375}},\"678\":{\"from_node_id\":\"2075292\",\"to_node_id\":\"2076049\",\"b1\":{\"y\":3815.769549805468,\"x\":3009.1480390625},\"b2\":{\"y\":3840.3213018166402,\"x\":2996.16659375}},\"679\":{\"from_node_id\":\"2076048\",\"to_node_id\":\"2075293\",\"b1\":{\"y\":3948.7830375230687,\"x\":2997.086199423056},\"b2\":{\"y\":4033.7927183060624,\"x\":3008.0275189101862}},\"680\":{\"from_node_id\":\"2076048\",\"to_node_id\":\"2075294\",\"b1\":{\"y\":3945.298592927578,\"x\":2997.010450627502},\"b2\":{\"y\":3991.887863925259,\"x\":3004.890257300006}},\"681\":{\"from_node_id\":\"2076048\",\"to_node_id\":\"2075295\",\"b1\":{\"y\":3962.535907464812,\"x\":2997.385174856572},\"b2\":{\"y\":4013.2860087368736,\"x\":2994.6002755635736}}}},\"2075840\":{\"name\":\"Nitrite Reductase (Ubiquinole-8, periplasm)\",\"bigg_id\":\"NTRIR3pp\",\"reversibility\":false,\"label_x\":7909.30788847041,\"label_y\":542.0664407649147,\"gene_reaction_rule\":\"b4070 and b4071 and b4072 and b4073\",\"genes\":[{\"bigg_id\":\"b4070\",\"name\":\"nrfA\"},{\"bigg_id\":\"b4071\",\"name\":\"nrfB\"},{\"bigg_id\":\"b4072\",\"name\":\"nrfC\"},{\"bigg_id\":\"b4073\",\"name\":\"nrfD\"}],\"metabolites\":[{\"coefficient\":2,\"bigg_id\":\"h2o_p\"},{\"coefficient\":1,\"bigg_id\":\"nh4_p\"},{\"coefficient\":-1,\"bigg_id\":\"no2_p\"},{\"coefficient\":3,\"bigg_id\":\"q8_c\"},{\"coefficient\":-3,\"bigg_id\":\"q8h2_c\"},{\"coefficient\":-2,\"bigg_id\":\"h_p\"}],\"segments\":{\"682\":{\"from_node_id\":\"2076052\",\"to_node_id\":\"2076051\",\"b1\":null,\"b2\":null},\"683\":{\"from_node_id\":\"2076051\",\"to_node_id\":\"2076050\",\"b1\":null,\"b2\":null},\"684\":{\"from_node_id\":\"2075296\",\"to_node_id\":\"2076052\",\"b1\":{\"y\":570.5693103826193,\"x\":7663.71393359375},\"b2\":{\"y\":572.8209134831445,\"x\":7759.170323406532}},\"685\":{\"from_node_id\":\"2075297\",\"to_node_id\":\"2076052\",\"b1\":{\"y\":566.4849670569738,\"x\":7831.4065919529285},\"b2\":{\"y\":576.142835824231,\"x\":7900.712901044808}},\"686\":{\"from_node_id\":\"2075298\",\"to_node_id\":\"2076052\",\"b1\":{\"y\":556.4883424688724,\"x\":7843.409319920852},\"b2\":{\"y\":576.3633552837393,\"x\":7910.108934278935}},\"687\":{\"from_node_id\":\"2076050\",\"to_node_id\":\"2075299\",\"b1\":{\"y\":579.3529971420026,\"x\":8037.493493996242},\"b2\":{\"y\":569.5644518094982,\"x\":8090.560108211032}},\"688\":{\"from_node_id\":\"2076050\",\"to_node_id\":\"2075300\",\"b1\":{\"y\":595.0407642693121,\"x\":8800.274575301122},\"b2\":{\"y\":2548.8462553831623,\"x\":9245.56647265625}},\"689\":{\"from_node_id\":\"2076050\",\"to_node_id\":\"2075301\",\"b1\":{\"y\":579.5186120585186,\"x\":8044.550119579862},\"b2\":{\"y\":572.2628638359038,\"x\":8109.789224739774}}}},\"2075841\":{\"name\":\"NADPH Quinone Reductase (2-Demethylmenaquinone-8)\",\"bigg_id\":\"NADPHQR4\",\"reversibility\":false,\"label_x\":7366.89071484375,\"label_y\":5631.57130859375,\"gene_reaction_rule\":\"b3028\",\"genes\":[{\"bigg_id\":\"b3028\",\"name\":\"mdaB\"}],\"metabolites\":[{\"coefficient\":-1,\"bigg_id\":\"2dmmq8_c\"},{\"coefficient\":1,\"bigg_id\":\"2dmmql8_c\"},{\"coefficient\":-1,\"bigg_id\":\"h_c\"},{\"coefficient\":1,\"bigg_id\":\"nadp_c\"},{\"coefficient\":-1,\"bigg_id\":\"nadph_c\"}],\"segments\":{\"690\":{\"from_node_id\":\"2076053\",\"to_node_id\":\"2076054\",\"b1\":null,\"b2\":null},\"691\":{\"from_node_id\":\"2076054\",\"to_node_id\":\"2076055\",\"b1\":null,\"b2\":null},\"692\":{\"from_node_id\":\"2075302\",\"to_node_id\":\"2076053\",\"b1\":{\"y\":5945.593855767566,\"x\":7356.8902265625},\"b2\":{\"y\":5901.79569969902,\"x\":7348.266203125}},\"693\":{\"from_node_id\":\"2075303\",\"to_node_id\":\"2076053\",\"b1\":{\"y\":5733.177138013302,\"x\":7348.266203125},\"b2\":{\"y\":5651.3596980446155,\"x\":7352.578703125}},\"694\":{\"from_node_id\":\"2075304\",\"to_node_id\":\"2076053\",\"b1\":{\"y\":5725.838020167849,\"x\":7352.578703125},\"b2\":{\"y\":5662.740872847229,\"x\":7352.578703125}},\"695\":{\"from_node_id\":\"2076055\",\"to_node_id\":\"2075305\",\"b1\":{\"y\":5369.223053076616,\"x\":7350.422453125},\"b2\":{\"y\":5349.599421713721,\"x\":7350.42196484375}},\"696\":{\"from_node_id\":\"2076055\",\"to_node_id\":\"2075306\",\"b1\":{\"y\":5574.065930628465,\"x\":7352.578703125},\"b2\":{\"y\":5523.58821277197,\"x\":7352.578703125}}}},\"2075842\":{\"name\":\"NADH 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b2288\",\"genes\":[{\"bigg_id\":\"b2281\",\"name\":\"nuoI\"},{\"bigg_id\":\"b2280\",\"name\":\"nuoJ\"},{\"bigg_id\":\"b2276\",\"name\":\"nuoN\"},{\"bigg_id\":\"b2278\",\"name\":\"nuoL\"},{\"bigg_id\":\"b2284\",\"name\":\"nuoF\"},{\"bigg_id\":\"b2282\",\"name\":\"nuoH\"},{\"bigg_id\":\"b2286\",\"name\":\"nuoC\"},{\"bigg_id\":\"b2288\",\"name\":\"nuoA\"},{\"bigg_id\":\"b2277\",\"name\":\"nuoM\"},{\"bigg_id\":\"b2285\",\"name\":\"nuoE\"},{\"bigg_id\":\"b2287\",\"name\":\"nuoB\"},{\"bigg_id\":\"b2279\",\"name\":\"nuoK\"},{\"bigg_id\":\"b2283\",\"name\":\"nuoG\"}],\"metabolites\":[{\"coefficient\":-4,\"bigg_id\":\"h_c\"},{\"coefficient\":1,\"bigg_id\":\"mql8_c\"},{\"coefficient\":-1,\"bigg_id\":\"nadh_c\"},{\"coefficient\":-1,\"bigg_id\":\"mqn8_c\"},{\"coefficient\":3,\"bigg_id\":\"h_p\"},{\"coefficient\":1,\"bigg_id\":\"nad_c\"}],\"segments\":{\"697\":{\"from_node_id\":\"2076056\",\"to_node_id\":\"2076058\",\"b1\":null,\"b2\":null},\"698\":{\"from_node_id\":\"2076058\",\"to_node_id\":\"2076057\",\"b1\":null,\"b2\":null},\"699\":{\"from_node_id\":\"2075307\",\"to_node_id\":\"2076056\",\"b1\":{\"y\":3336.3643253924592,\"x\":6063.50985546875},\"b2\":{\"y\":3296.511941523988,\"x\":6063.50985546875}},\"700\":{\"from_node_id\":\"2075308\",\"to_node_id\":\"2076056\",\"b1\":{\"y\":3326.995464399346,\"x\":6063.50985546875},\"b2\":{\"y\":3293.701283226054,\"x\":6063.50985546875}},\"701\":{\"from_node_id\":\"2075309\",\"to_node_id\":\"2076056\",\"b1\":{\"y\":4012.282061836384,\"x\":6137.92294140625},\"b2\":{\"y\":3462.1687810118524,\"x\":6063.50985546875}},\"702\":{\"from_node_id\":\"2076057\",\"to_node_id\":\"2075310\",\"b1\":{\"y\":3221.3121249289447,\"x\":6063.50985546875},\"b2\":{\"y\":3179.0316034089824,\"x\":6063.50985546875}},\"703\":{\"from_node_id\":\"2076057\",\"to_node_id\":\"2075311\",\"b1\":{\"y\":3211.68559187029,\"x\":6063.50985546875},\"b2\":{\"y\":3157.786909880134,\"x\":6069.95223828125}},\"704\":{\"from_node_id\":\"2076057\",\"to_node_id\":\"2075312\",\"b1\":{\"y\":3057.8700717847105,\"x\":6067.8218671875},\"b2\":{\"y\":2859.7976762615344,\"x\":6112.10262890625}}}},\"2075843\":{\"name\":\"L-malate transport out via proton antiport (periplasm)\",\"bigg_id\":\"MALt3pp\",\"reversibility\":false,\"label_x\":4609.3746015625,\"label_y\":2629.5182859375,\"gene_reaction_rule\":\"\",\"genes\":[],\"metabolites\":[{\"coefficient\":1,\"bigg_id\":\"mal__L_p\"},{\"coefficient\":1,\"bigg_id\":\"h_c\"},{\"coefficient\":-1,\"bigg_id\":\"h_p\"},{\"coefficient\":-1,\"bigg_id\":\"mal__L_c\"}],\"segments\":{\"705\":{\"from_node_id\":\"2076061\",\"to_node_id\":\"2076059\",\"b1\":null,\"b2\":null},\"706\":{\"from_node_id\":\"2076059\",\"to_node_id\":\"2076060\",\"b1\":null,\"b2\":null},\"707\":{\"from_node_id\":\"2075291\",\"to_node_id\":\"2076061\",\"b1\":{\"y\":2614.15744609375,\"x\":2898.78006482508},\"b2\":{\"y\":2646.7302,\"x\":4258.118188783462}},\"708\":{\"from_node_id\":\"2075313\",\"to_node_id\":\"2076061\",\"b1\":{\"y\":2576.053198046875,\"x\":4466.610186412674},\"b2\":{\"y\":2646.7302,\"x\":4482.757044595677}},\"709\":{\"from_node_id\":\"2076060\",\"to_node_id\":\"2075314\",\"b1\":{\"y\":2646.7302,\"x\":4785.478718977237},\"b2\":{\"y\":2700.0983640625,\"x\":4801.056946069955}},\"710\":{\"from_node_id\":\"2076060\",\"to_node_id\":\"2075315\",\"b1\":{\"y\":2646.7302,\"x\":4775.921069752865},\"b2\":{\"y\":2648.1725828125,\"x\":4903.3397168845495}}}},\"2075844\":{\"name\":\"D-lactate 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