Github complete FBA (and begun FVA) Momics using Pomics data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "theoretical-whale",
   "metadata": {},
   "source": [
    "# Import packages and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "integral-semiconductor",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Various packages I have used for different methods. (May not all still be necessary, but keeping in anyway). \n",
    "import cobra\n",
    "import matplotlib.pyplot as plt\n",
    "from cobra import sampling\n",
    "import numpy as np\n",
    "import csv\n",
    "from cobra.flux_analysis.loopless import add_loopless, loopless_solution\n",
    "from cobra import Model, Reaction, Metabolite\n",
    "import pandas as pd\n",
    "import gurobipy as gp\n",
    "from pandas import DataFrame\n",
    "import csv\n",
    "from cobra.util.solver import linear_reaction_coefficients\n",
    "from cobra import sampling\n",
    "from time import time\n",
    "import cobra.test\n",
    "from cobra.flux_analysis import (\n",
    "    single_gene_deletion, single_reaction_deletion, double_gene_deletion,\n",
    "    double_reaction_deletion)\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suburban-thread",
   "metadata": {},
   "source": [
    "# Import and dissect proteomics dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "czech-injection",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC0_dict:  2584\n",
      "FC100_dict:  2584\n",
      "FC500_dict:  2584\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>To</th>\n",
       "      <th>1_FC0_UNIMAN</th>\n",
       "      <th>2_FC0_UNIMAN</th>\n",
       "      <th>3_FC0_UNIMAN</th>\n",
       "      <th>1_FC100_UNIMAN</th>\n",
       "      <th>2_FC100_UNIMAN</th>\n",
       "      <th>3_FC100_UNIMAN</th>\n",
       "      <th>1_FC500_UNIMAN</th>\n",
       "      <th>2_FC500_UNIMAN</th>\n",
       "      <th>3_FC500_UNIMAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000151247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.338929</td>\n",
       "      <td>27.840268</td>\n",
       "      <td>40.798217</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000135406</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.669405</td>\n",
       "      <td>10.311900</td>\n",
       "      <td>9.692083</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000185885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>141.100707</td>\n",
       "      <td>67.453476</td>\n",
       "      <td>105.257364</td>\n",
       "      <td>303.739613</td>\n",
       "      <td>328.885556</td>\n",
       "      <td>466.076144</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000133265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.487784</td>\n",
       "      <td>30.054430</td>\n",
       "      <td>17.224477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000146926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.214768</td>\n",
       "      <td>26.830506</td>\n",
       "      <td>41.484865</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>2579</th>\n",
       "      <td>ENSG00000184672</td>\n",
       "      <td>1593.522699</td>\n",
       "      <td>1638.521402</td>\n",
       "      <td>1896.864108</td>\n",
       "      <td>1919.050812</td>\n",
       "      <td>1700.629152</td>\n",
       "      <td>1528.883639</td>\n",
       "      <td>1855.953411</td>\n",
       "      <td>1657.173839</td>\n",
       "      <td>1746.453669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2580</th>\n",
       "      <td>ENSG00000041357</td>\n",
       "      <td>2614.265573</td>\n",
       "      <td>2371.116841</td>\n",
       "      <td>2903.682742</td>\n",
       "      <td>2854.571929</td>\n",
       "      <td>2629.230894</td>\n",
       "      <td>2432.919882</td>\n",
       "      <td>2712.630778</td>\n",
       "      <td>2301.219410</td>\n",
       "      <td>2723.346094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>ENSG00000135069</td>\n",
       "      <td>1631.154876</td>\n",
       "      <td>1161.950609</td>\n",
       "      <td>1601.933527</td>\n",
       "      <td>1470.429285</td>\n",
       "      <td>1445.948679</td>\n",
       "      <td>1380.361747</td>\n",
       "      <td>1419.556933</td>\n",
       "      <td>1405.996717</td>\n",
       "      <td>1475.284532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2582</th>\n",
       "      <td>ENSG00000106028</td>\n",
       "      <td>4709.911145</td>\n",
       "      <td>4519.158264</td>\n",
       "      <td>5276.965994</td>\n",
       "      <td>5028.942079</td>\n",
       "      <td>4677.706556</td>\n",
       "      <td>4566.271020</td>\n",
       "      <td>4750.747272</td>\n",
       "      <td>4584.395249</td>\n",
       "      <td>5007.843829</td>\n",
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       "    <tr>\n",
       "      <th>2583</th>\n",
       "      <td>ENSG00000087470</td>\n",
       "      <td>4388.558632</td>\n",
       "      <td>4769.983427</td>\n",
       "      <td>5090.666084</td>\n",
       "      <td>5451.751457</td>\n",
       "      <td>4193.814991</td>\n",
       "      <td>4461.311469</td>\n",
       "      <td>4460.042778</td>\n",
       "      <td>4527.616253</td>\n",
       "      <td>5236.943212</td>\n",
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       "<p>2584 rows × 10 columns</p>\n",
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      "text/plain": [
       "                   To  1_FC0_UNIMAN  2_FC0_UNIMAN  3_FC0_UNIMAN  \\\n",
       "0     ENSG00000151247      0.000000      0.000000      0.000000   \n",
       "1     ENSG00000135406      0.000000      0.000000      0.000000   \n",
       "2     ENSG00000185885      0.000000      0.000000      0.000000   \n",
       "3     ENSG00000133265      0.000000      0.000000      0.000000   \n",
       "4     ENSG00000146926      0.000000      0.000000      0.000000   \n",
       "...               ...           ...           ...           ...   \n",
       "2579  ENSG00000184672   1593.522699   1638.521402   1896.864108   \n",
       "2580  ENSG00000041357   2614.265573   2371.116841   2903.682742   \n",
       "2581  ENSG00000135069   1631.154876   1161.950609   1601.933527   \n",
       "2582  ENSG00000106028   4709.911145   4519.158264   5276.965994   \n",
       "2583  ENSG00000087470   4388.558632   4769.983427   5090.666084   \n",
       "\n",
       "      1_FC100_UNIMAN  2_FC100_UNIMAN  3_FC100_UNIMAN  1_FC500_UNIMAN  \\\n",
       "0           0.000000        0.000000        0.000000       28.338929   \n",
       "1           0.000000        0.000000        0.000000        7.669405   \n",
       "2         141.100707       67.453476      105.257364      303.739613   \n",
       "3           0.000000        0.000000        0.000000       28.487784   \n",
       "4           0.000000        0.000000        0.000000       65.214768   \n",
       "...              ...             ...             ...             ...   \n",
       "2579     1919.050812     1700.629152     1528.883639     1855.953411   \n",
       "2580     2854.571929     2629.230894     2432.919882     2712.630778   \n",
       "2581     1470.429285     1445.948679     1380.361747     1419.556933   \n",
       "2582     5028.942079     4677.706556     4566.271020     4750.747272   \n",
       "2583     5451.751457     4193.814991     4461.311469     4460.042778   \n",
       "\n",
       "      2_FC500_UNIMAN  3_FC500_UNIMAN  \n",
       "0          27.840268       40.798217  \n",
       "1          10.311900        9.692083  \n",
       "2         328.885556      466.076144  \n",
       "3          30.054430       17.224477  \n",
       "4          26.830506       41.484865  \n",
       "...              ...             ...  \n",
       "2579     1657.173839     1746.453669  \n",
       "2580     2301.219410     2723.346094  \n",
       "2581     1405.996717     1475.284532  \n",
       "2582     4584.395249     5007.843829  \n",
       "2583     4527.616253     5236.943212  \n",
       "\n",
       "[2584 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensembl_proteomics = pd.read_csv('/Users/katemeeson/Dropbox (The University of Manchester)/UNIMAN Ovarian Cancer Project/July August modelling/ensembl_proteomics.csv')\n",
    "\n",
    "FC0_ensembl = ensembl_proteomics.iloc[:,0:3]\n",
    "FC100_ensembl = ensembl_proteomics.iloc[:,[3,4,5]]\n",
    "FC500_ensembl = ensembl_proteomics.iloc[:,[6,7,8]]\n",
    "\n",
    "FC0_transposed_ensembl = FC0_ensembl.transpose() #orientation for dataframe to dictionary conversion. \n",
    "FC100_transposed_ensembl = FC100_ensembl.transpose()\n",
    "FC500_transposed_ensembl = FC500_ensembl.transpose()\n",
    "\n",
    "FC0_dict_ensembl = FC0_transposed_ensembl.to_dict('list')\n",
    "FC100_dict_ensembl = FC100_transposed_ensembl.to_dict('list')\n",
    "FC500_dict_ensembl = FC500_transposed_ensembl.to_dict('list')\n",
    "\n",
    "print('FC0_dict: ', len(FC0_dict_ensembl))\n",
    "print('FC100_dict: ', len(FC100_dict_ensembl))\n",
    "print('FC500_dict: ', len(FC500_dict_ensembl))\n",
    "ensembl_proteomics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "solid-special",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-a7ff03e0463b>:5: UserWarning: DataFrame columns are not unique, some columns will be omitted.\n",
      "  FC0_list = FC0_ensembl.set_index('To').T.to_dict('list')\n",
      "<ipython-input-23-a7ff03e0463b>:6: UserWarning: DataFrame columns are not unique, some columns will be omitted.\n",
      "  FC100_list = FC100_ensembl.set_index('To').T.to_dict('list')\n",
      "<ipython-input-23-a7ff03e0463b>:7: UserWarning: DataFrame columns are not unique, some columns will be omitted.\n",
      "  FC500_list = FC500_ensembl.set_index('To').T.to_dict('list')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of detected proteins\n",
      "---------------------------\n",
      "FC0 =  2583\n",
      "FC100 =  2583\n",
      "FC500 =  2583\n"
     ]
    }
   ],
   "source": [
    "FC0_ensembl = ensembl_proteomics.iloc[:,[0,1,2,3]]\n",
    "FC100_ensembl = ensembl_proteomics.iloc[:,[0,4,5,6]]\n",
    "FC500_ensembl = ensembl_proteomics.iloc[:,[0,7,8,9]]\n",
    "\n",
    "#Convert lists of values to dictionaries. \n",
    "FC0_list = FC0_ensembl.set_index('To').T.to_dict('list')\n",
    "FC100_list = FC100_ensembl.set_index('To').T.to_dict('list')\n",
    "FC500_list = FC500_ensembl.set_index('To').T.to_dict('list')\n",
    "\n",
    "FC0 = {}  \n",
    "for key, val in FC0_list.items():\n",
    "    FC0[key] = np.average(val)\n",
    "        \n",
    "FC100 = {}\n",
    "for key, val in FC100_list.items():\n",
    "    FC100[key] = np.average(val)\n",
    "        \n",
    "FC500 = {}\n",
    "for key, val in FC500_list.items():\n",
    "    FC500[key] = np.average(val)\n",
    "        \n",
    "print('Number of detected proteins')\n",
    "print('---------------------------')\n",
    "print('FC0 = ', len(FC0))\n",
    "print('FC100 = ', len(FC100))\n",
    "print('FC500 = ', len(FC500))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "taken-dryer",
   "metadata": {},
   "source": [
    "# Import Human1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "solar-consortium",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Name</strong></td>\n",
       "                <td>HumanGEM</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x0138c0b610</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of metabolites</strong></td>\n",
       "                <td>8400</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of reactions</strong></td>\n",
       "                <td>13096</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of groups</strong></td>\n",
       "                <td>143</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Objective expression</strong></td>\n",
       "                <td>1.0*biomass_human - 1.0*biomass_human_reverse_fb2f2</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartments</strong></td>\n",
       "                <td>Cytosol, Extracellular, Lysosome, Endoplasmic reticulum, Mitochondria, Peroxisome, Golgi apparatus, Nucleus, Inner mitochondria</td>\n",
       "            </tr>\n",
       "          </table>"
      ],
      "text/plain": [
       "<Model HumanGEM at 0x138c0b610>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = cobra.io.read_sbml_model('Human-GEM-annotated.xml')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "designed-methodology",
   "metadata": {},
   "outputs": [],
   "source": [
    "#shortcuts to access reactions and metabolites\n",
    "x = model.reactions.get_by_id\n",
    "y = model.metabolites.get_by_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "restricted-brunei",
   "metadata": {},
   "source": [
    "## Getting an overview of the Human1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "virgin-steering",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactions: 13096\n",
      "Metabolites: 8400\n",
      "Genes: 3628\n",
      "{'c': 'Cytosol', 's': 'Extracellular', 'l': 'Lysosome', 'r': 'Endoplasmic reticulum', 'm': 'Mitochondria', 'p': 'Peroxisome', 'g': 'Golgi apparatus', 'n': 'Nucleus', 'c_i': 'Inner mitochondria'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Reactions:\", len(model.reactions))\n",
    "print(\"Metabolites:\", len(model.metabolites))\n",
    "print(\"Genes:\", len(model.genes))\n",
    "print(model.compartments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "present-indonesia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective\n",
      "=========\n",
      "1.0 biomass_human = 187.35362997658078\n",
      "\n",
      "Uptake\n",
      "------\n",
      " Metabolite          Reaction      Flux  C-Number C-Flux\n",
      "    m00350s EX_13_cis_retn[e]   0.01938        20  0.00%\n",
      "    m01417s EX_1a25dhvitd3[e]  0.003867        27  0.00%\n",
      "    m00674s         EX_2pg[e]    0.3261         3  0.00%\n",
      "    m01231s      EX_CE1617[e]  0.003867        20  0.00%\n",
      "    m02479s     EX_HC00900[e]     867.9         4  1.23%\n",
      "    m02965s     EX_HC02192[e]     246.1        26  2.26%\n",
      "    m02000s     EX_HC02193[e]      1000        26  9.20%\n",
      "    m02871s       EX_ahcys[e]      1000        14  4.95%\n",
      "    m01371s         EX_atp[e]      1000        10  3.54%\n",
      "    m02635s       EX_c4crn[e]      1000        11  3.89%\n",
      "    m02409s       EX_c8crn[e]     118.2        15  0.63%\n",
      "    m01430s      EX_crm_hs[e]     27.61        19  0.19%\n",
      "    m01644s        EX_dcmp[e]     532.6         9  1.70%\n",
      "    m01690s        EX_dhap[e]     0.773         3  0.00%\n",
      "    m01802s         EX_fad[e]   0.01551        27  0.00%\n",
      "    m01831s        EX_fald[e]     241.9         1  0.09%\n",
      "    m01841s         EX_fdp[e]     747.7         6  1.59%\n",
      "    m01973s       EX_glcur[e]  0.007756         6  0.00%\n",
      "glnasngln_s   EX_glnasngln[e]     37.61        14  0.19%\n",
      "    m02914s      EX_glyc3p[e]    0.8496         3  0.00%\n",
      "   hmcarn_s      EX_hmcarn[e]      1000        10  3.54%\n",
      "      hxa_s         EX_hxa[e]     997.9         6  2.12%\n",
      "    m02122s       EX_hxcoa[e]     2.138        27  0.02%\n",
      "    m02183s        EX_icit[e]   0.01812         6  0.00%\n",
      "leutyrtyr_s   EX_leutyrtyr[e]     14.29        24  0.12%\n",
      "    m00656s   EX_lpchol_hs[e]     23.38         9  0.07%\n",
      "lysargleu_s   EX_lysargleu[e]     51.34        18  0.33%\n",
      "lyslyslys_s   EX_lyslyslys[e]     3.015        18  0.02%\n",
      "metmetile_s   EX_metmetile[e]     11.27        16  0.06%\n",
      "    m01045s       EX_mlthf[e]     353.6        20  2.50%\n",
      "    m02518s     EX_n8aspmd[e]     297.6         9  0.95%\n",
      "    m02579s         EX_nh4[e]     738.3         0  0.00%\n",
      "    m02646s      EX_ocdcea[e]     118.1        18  0.75%\n",
      "    m02660s      EX_orot5p[e]     8.894        10  0.03%\n",
      "    m02750s     EX_pail_hs[e]     2.876        11  0.01%\n",
      "    m01620s      EX_pcreat[e]      1000         4  1.42%\n",
      "    m02685s       EX_pe_hs[e]     7.903         7  0.02%\n",
      "    m02696s         EX_pep[e]      1000         3  1.06%\n",
      "    m02715s    EX_pglyc_hs[e]     1.247         8  0.00%\n",
      "phethrlys_s   EX_phethrlys[e]     11.77        19  0.08%\n",
      "    m02411s      EX_pmtcrn[e]  0.007741        23  0.00%\n",
      "    m02808s       EX_ps_hs[e]    0.7874         8  0.00%\n",
      "    m00916s      EX_pser_L[e]    0.2265         3  0.00%\n",
      "    m03103s         EX_q10[e]  0.007756        59  0.00%\n",
      "    m02927s       EX_sphgn[e]     764.7        18  4.87%\n",
      "    m02964s    EX_tdechola[e]     753.9        26  6.94%\n",
      "    m02978s       EX_thbpt[e]  0.007756         9  0.00%\n",
      "trpglngln_s   EX_trpglngln[e]     70.96        21  0.53%\n",
      "valleuphe_s   EX_valleuphe[e]     26.39        20  0.19%\n",
      "    m03149s        EX_xtsn[e]     225.5        10  0.80%\n",
      "    m02328s         HMR_10027     146.4        31  1.61%\n",
      "    m02184s          HMR_9039     33.86         6  0.07%\n",
      "    m02993s          HMR_9044     45.24         4  0.06%\n",
      "    m03135s          HMR_9046      41.1         5  0.07%\n",
      "    m02630s          HMR_9048      1000         0  0.00%\n",
      "    m02353s          HMR_9051 0.0003146     94027  0.01%\n",
      "    m01975s          HMR_9063       336         5  0.59%\n",
      "    m02896s          HMR_9069     68.65         3  0.07%\n",
      "    m02042s          HMR_9103     865.1         0  0.00%\n",
      "    m02049s          HMR_9107  0.007756        34  0.00%\n",
      "    m01704s          HMR_9108      1000         9  3.18%\n",
      "    m01400s          HMR_9110  0.007756        16  0.00%\n",
      "    m01288s          HMR_9141      1000        15  5.31%\n",
      "    m02834s          HMR_9147   0.02709        20  0.00%\n",
      "    m01330s          HMR_9152  0.007756        29  0.00%\n",
      "    m01938s          HMR_9154  0.007756        28  0.00%\n",
      "    m02193s          HMR_9155      1000        10  3.54%\n",
      "    m02982s          HMR_9159  0.007756        12  0.00%\n",
      "    m02394s          HMR_9167  0.007756         8  0.00%\n",
      "    m02001s          HMR_9204  0.007741         0  0.00%\n",
      "    m00267s          HMR_9206     2.126        40  0.03%\n",
      "    m01415s          HMR_9215    0.0116        27  0.00%\n",
      "    m01279s          HMR_9253      1000         5  1.77%\n",
      "    m01280s          HMR_9254      1000        10  3.54%\n",
      "    m01334s          HMR_9262      1000        10  3.54%\n",
      "    m01361s          HMR_9269  0.007756        62  0.00%\n",
      "    m01397s          HMR_9271  0.003867        45  0.00%\n",
      "    m01398s          HMR_9272   0.05435        39  0.00%\n",
      "    m01450s          HMR_9285     4.573        27  0.04%\n",
      "    m01587s          HMR_9286     0.308         6  0.00%\n",
      "    m01666s          HMR_9297      1000        10  3.54%\n",
      "    m02038s          HMR_9348     8.541        10  0.03%\n",
      "    m02026s          HMR_9351      1.64        10  0.01%\n",
      "    m02041s          HMR_9354     508.5         0  0.00%\n",
      "    m02583s          HMR_9378    0.3533         6  0.00%\n",
      "    m02631s          HMR_9383      1000         0  0.00%\n",
      "    m02833s          HMR_9404   0.05809        20  0.00%\n",
      "    m03123s          HMR_9439       651         9  2.07%\n",
      "    m03130s          HMR_9440      1000         9  3.18%\n",
      "    m03154s          HMR_9450      1000        10  3.54%\n",
      "    m01286s          HMR_9456      1000        16  5.66%\n",
      "    m01287s          HMR_9457     439.3        16  2.49%\n",
      "    m02814s          HMR_9691  0.007756         8  0.00%\n",
      "    m03161s          HMR_9729      76.1         6  0.16%\n",
      "\n",
      "Secretion\n",
      "---------\n",
      " Metabolite        Reaction       Flux  C-Number C-Flux\n",
      " 3dhlchol_s  EX_3dhlchol[e]      -1000        24 12.53%\n",
      "    m02134s    EX_CE1401[e]      -1000         4  2.09%\n",
      "    m02402s   EX_HC02191[e]     -246.1        24  3.08%\n",
      "    m01155s      EX_ahdt[e]      -1000         9  4.70%\n",
      "alahisala_s EX_alahisala[e]     -24.01        12  0.15%\n",
      "    m01862s       EX_fum[e]     -803.5         4  1.68%\n",
      "    m01683s      EX_glcn[e]      -1000         6  3.13%\n",
      "glnhishis_s EX_glnhishis[e]     -203.7        17  1.81%\n",
      "hiscyscys_s EX_hiscyscys[e]     -423.4        12  2.65%\n",
      "histrphis_s EX_histrphis[e]     -61.07        23  0.73%\n",
      "    m02639s    EX_odecrn[e]     -118.1        25  1.54%\n",
      "    m02657s      EX_pcrn[e]   -0.04654        10  0.00%\n",
      "propropro_s EX_propropro[e]     -40.16        15  0.31%\n",
      "    m02929s   EX_sphings[e]     -789.7        18  7.42%\n",
      "    m02923s      EX_spmd[e]     -297.6         7  1.09%\n",
      "    m03148s       EX_xan[e]     -225.5         5  0.59%\n",
      "    m03150s       EX_xmp[e]      -1000        10  5.22%\n",
      "    m01759s    EX_xylu_D[e]      -1000         5  2.61%\n",
      "   temp001s       HMR_10024     -187.4         0  0.00%\n",
      "    m00235s       HMR_10047      -2.54         5  0.01%\n",
      "    m01965s        HMR_9034     -155.1         6  0.49%\n",
      "    m02352s        HMR_9055 -0.0003146     33247  0.01%\n",
      "    m02751s        HMR_9072      -1000         0  0.00%\n",
      "    m02046s        HMR_9078     -172.1         1  0.09%\n",
      "    m01983s        HMR_9085  -0.007756         3  0.00%\n",
      "    m01252s        HMR_9086  -0.007741         2  0.00%\n",
      "    m00970s        HMR_9091      -1000         4  2.09%\n",
      "    m02819s        HMR_9133      -1000         3  1.57%\n",
      "    m01830s        HMR_9146     -353.6        19  3.51%\n",
      "    m00266s        HMR_9205     -2.126        20  0.02%\n",
      "    m01249s        HMR_9242      -1000         2  1.04%\n",
      "    m01306s        HMR_9259     -21.79         5  0.06%\n",
      "    m01396s        HMR_9273   -0.05822        33  0.00%\n",
      "    m01619s        HMR_9290      -1000         4  2.09%\n",
      "    m02348s        HMR_9292      -1000         7  3.65%\n",
      "    m01668s        HMR_9296     -397.9         9  1.87%\n",
      "    m02159s        HMR_9358      -1000         5  2.61%\n",
      "    m02843s        HMR_9406      -1000         5  2.61%\n",
      "    m02961s        HMR_9418      -1000         2  1.04%\n",
      "    m02997s        HMR_9422       -766         5  2.00%\n",
      "    m03118s        HMR_9437      -1000         4  2.09%\n",
      "    m01989s        HMR_9463     -753.9        26 10.23%\n",
      "    m02659s        HMR_9690     -8.505         5  0.02%\n",
      "    m01883s        HMR_9697     -146.4        77  5.88%\n",
      "    m02022s        HMR_9703  -0.007741         0  0.00%\n",
      "    m02434s        HMR_9714  -0.007741        51  0.00%\n",
      "    m01410s        HMR_9809      -1000         4  2.09%\n",
      "    m02120s        HMR_9811      -1000         6  3.13%\n",
      "    m02642s        HMR_9813     -118.2         8  0.49%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "hired-exercise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No in-built constraints\n"
     ]
    }
   ],
   "source": [
    "#any in-built constraints? \n",
    "in_built_constraints = {} \n",
    "\n",
    "for r in model.reactions:\n",
    "    if r.bounds != (0.0, 0.0) and r.bounds != (-1000.0, 0.0) and r.bounds != (-1000.0, 1000.0) and r.bounds != (0.0, 1000.0):\n",
    "        in_built_constraints[r.id] = r.bounds\n",
    "        print(r.id)\n",
    "\n",
    "if len(in_built_constraints) == 0:\n",
    "    print('No in-built constraints')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "congressional-chuck",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AND rules:  653\n",
      "ANDOR rules:  129\n",
      "OR rules:  3972\n",
      "ONE GENE rules:  3282\n",
      "NO GENE rules:  5060\n",
      "Proportion of model not annotated:  38.63775198533904 %\n",
      "Proportion of model which IS annotated:  61.36224801466096 %\n",
      "Total Reactions =  13096\n",
      "13096\n"
     ]
    }
   ],
   "source": [
    "#what proportion of the model is annotated with reaction rules, and what are these rules? \n",
    "ANDs = []\n",
    "ORs = []\n",
    "ANDORs = []\n",
    "one_gene = []\n",
    "no_gene = []\n",
    "\n",
    "for r in model.reactions:\n",
    "    if 'and' in r.gene_reaction_rule and 'or' not in r.gene_reaction_rule:\n",
    "        ANDs.append(r.id)\n",
    "    if 'and' in r.gene_reaction_rule and 'or' in r.gene_reaction_rule:\n",
    "        ANDORs.append(r.id)\n",
    "    if 'or' in r.gene_reaction_rule and 'and' not in r.gene_reaction_rule:\n",
    "        ORs.append(r.id)\n",
    "    if len(r.gene_reaction_rule) == 0:\n",
    "        no_gene.append(r.id)\n",
    "    if len(r.gene_reaction_rule) != 0:\n",
    "        if 'or' in r.gene_reaction_rule:\n",
    "            continue\n",
    "        elif 'and' in r.gene_reaction_rule:\n",
    "            continue\n",
    "        else:\n",
    "            one_gene.append(r.id)\n",
    "\n",
    "print('AND rules: ', len(ANDs))\n",
    "print('ANDOR rules: ', len(ANDORs))\n",
    "print('OR rules: ', len(ORs))\n",
    "print('ONE GENE rules: ', len(one_gene))\n",
    "print('NO GENE rules: ', len(no_gene))\n",
    "print('Proportion of model not annotated: ', len(no_gene)/len(model.reactions)*100, '%')\n",
    "print('Proportion of model which IS annotated: ', 100-(len(no_gene)/len(model.reactions)*100), '%')\n",
    "print('Total Reactions = ', len(model.reactions))\n",
    "print(len(ORs)+len(ANDs)+len(ANDORs)+len(one_gene)+len(no_gene))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "powered-staff",
   "metadata": {},
   "source": [
    "## What is the coverage of model reactions by our proteomics and vice versa?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "nutritional-column",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to match proteomics data to model reaction rules. \n",
    "\n",
    "def gene_expression_match(lis, data, lib): #lis = list (ANDs, ORs, ANDORs or ones), data = expression data,  \n",
    "    for r in lis:                          #lib = library you are making\n",
    "        ins= {}\n",
    "        notins = {}\n",
    "        for g in x(r).genes:\n",
    "            for key, val in data.items():\n",
    "                if key in str(g):\n",
    "                    ins[str(g)] = val\n",
    "                if key not in str(g):\n",
    "                    notins[str(g)] = 'NO DATA'\n",
    "        temp = {**notins, **ins}\n",
    "        lib[r] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "christian-exhaust",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making libraries containing reaction rules, with proteins annotated. \n",
    "\n",
    "FC0_ones = {}\n",
    "FC100_ones = {}\n",
    "FC500_ones = {}\n",
    "\n",
    "gene_expression_match(one_gene, FC0, FC0_ones)\n",
    "gene_expression_match(one_gene, FC100, FC100_ones)\n",
    "gene_expression_match(one_gene, FC500, FC500_ones)\n",
    "\n",
    "FC0_ANDs = {}\n",
    "FC100_ANDs = {}\n",
    "FC500_ANDs = {}\n",
    "\n",
    "gene_expression_match(ANDs, FC0, FC0_ANDs) \n",
    "gene_expression_match(ANDs, FC100, FC100_ANDs)\n",
    "gene_expression_match(ANDs, FC500, FC500_ANDs)\n",
    "\n",
    "FC0_ORs = {}\n",
    "FC100_ORs = {}\n",
    "FC500_ORs = {}\n",
    "\n",
    "gene_expression_match(ORs, FC0, FC0_ORs)\n",
    "gene_expression_match(ORs, FC100, FC100_ORs)\n",
    "gene_expression_match(ORs, FC500, FC500_ORs)\n",
    "\n",
    "FC0_ANDORs = {}\n",
    "FC100_ANDORs = {}\n",
    "FC500_ANDORs = {}\n",
    "\n",
    "gene_expression_match(ANDORs, FC0, FC0_ANDORs)\n",
    "gene_expression_match(ANDORs, FC100, FC100_ANDORs)\n",
    "gene_expression_match(ANDORs, FC500, FC500_ANDORs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "banned-kruger",
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions to assess coverage of model reactions by our proteomics data. \n",
    "\n",
    "def match_ANDs(a): # outputs 2 lists, one with reactions with all genes matched, and one where at least one gene is matched\n",
    "    \n",
    "    matched_ANDs = []\n",
    "    fully_matched_ANDs = []\n",
    "\n",
    "    for key, val in a.items():\n",
    "        n = 0\n",
    "        for k, v in val.items():\n",
    "            if type(v) != str:\n",
    "                n = n+1\n",
    "        if n == len(x(key).genes):\n",
    "            fully_matched_ANDs.append(key)\n",
    "        if n != 0:\n",
    "            matched_ANDs.append(key)\n",
    "        \n",
    "    return matched_ANDs, fully_matched_ANDs\n",
    "\n",
    "def match(lib):\n",
    "\n",
    "    matched = []\n",
    "\n",
    "    for key, val in lib.items():\n",
    "        n = 0\n",
    "        for k, v in val.items():\n",
    "            if type(v) != str:\n",
    "                n = n+1\n",
    "        if n != 0:\n",
    "            matched.append(key)\n",
    "    return matched\n",
    "\n",
    "\n",
    "def coverage(a, o, ao, on): # a = ANDs, o = ORs, ao = ANDORs, on = ones \n",
    "    \n",
    "    annotated_reactions = [] #checking how many reactions have a gene annotated\n",
    "    for r in model.reactions:\n",
    "        n = 0\n",
    "        for g in r.genes:\n",
    "            if len(str(g)) > 0:\n",
    "                n = n+1\n",
    "        if n > 1:\n",
    "            annotated_reactions.append(r.id)\n",
    "            \n",
    "    matched_ANDs, fully_matched_ANDs = match_ANDs(a)\n",
    "    matched_ORs = match(o)\n",
    "    matched_ANDORs = match(ao)\n",
    "    matched_ones = match(on)\n",
    "    \n",
    "    total_matched = [len(fully_matched_ANDs), len(matched_ORs), len(matched_ones), len(matched_ANDORs)]\n",
    "    total_partially_matched  = [len(matched_ANDs), len(matched_ORs), len(matched_ones), len(matched_ANDORs)]\n",
    "\n",
    "    print('Reactions in Model = ', len(model.reactions))\n",
    "    print('Models annotated with at least one gene = ', len(annotated_reactions))\n",
    "    print('Coverage with fully matched \"Ands\" = ', np.sum(total_matched))\n",
    "    print('Coverage with partially matched \"Ands\" = ', np.sum(total_partially_matched))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "expensive-brunei",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC0 Coverage\n",
      "---------------\n",
      "Reactions in Model =  13096\n",
      "Models annotated with at least one gene =  4754\n",
      "Coverage with fully matched \"Ands\" =  2538\n",
      "Coverage with partially matched \"Ands\" =  2876\n",
      "\n",
      "FC100 Coverage\n",
      "----------------\n",
      "Reactions in Model =  13096\n",
      "Models annotated with at least one gene =  4754\n",
      "Coverage with fully matched \"Ands\" =  2538\n",
      "Coverage with partially matched \"Ands\" =  2876\n",
      "\n",
      "FC500 Coverage\n",
      "----------------\n",
      "Reactions in Model =  13096\n",
      "Models annotated with at least one gene =  4754\n",
      "Coverage with fully matched \"Ands\" =  2538\n",
      "Coverage with partially matched \"Ands\" =  2876\n"
     ]
    }
   ],
   "source": [
    "print('FC0 Coverage')\n",
    "print('---------------')\n",
    "coverage(FC0_ANDs, FC0_ORs, FC0_ANDORs, FC0_ones)\n",
    "print('')\n",
    "print('FC100 Coverage')\n",
    "print('----------------')\n",
    "coverage(FC100_ANDs, FC100_ORs, FC100_ANDORs, FC100_ones)\n",
    "print('')\n",
    "print('FC500 Coverage')\n",
    "print('----------------')\n",
    "coverage(FC500_ANDs, FC500_ORs, FC500_ANDORs, FC500_ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "funky-gambling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max FC0 =  90748.11355666666\n",
      "Max FC100 =  73461.95941666666\n",
      "Max FC500 =  59900.516429999996\n"
     ]
    }
   ],
   "source": [
    "FC0_expression_vals = []\n",
    "for key, val in FC0.items():\n",
    "    FC0_expression_vals.append(val)\n",
    "    \n",
    "FC100_expression_vals = []\n",
    "for key, val in FC100.items():\n",
    "    FC100_expression_vals.append(val)\n",
    "    \n",
    "FC500_expression_vals = []\n",
    "for key, val in FC500.items():\n",
    "    FC500_expression_vals.append(val)\n",
    "    \n",
    "FC0_expression_vals.sort()\n",
    "FC100_expression_vals.sort()\n",
    "FC500_expression_vals.sort()\n",
    "\n",
    "print('Max FC0 = ', FC0_expression_vals[-1])\n",
    "print('Max FC100 = ', FC100_expression_vals[-1])\n",
    "print('Max FC500 = ', FC500_expression_vals[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interracial-notice",
   "metadata": {},
   "source": [
    "## Finding the number of core reactions in Human1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "after-louis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original FBA solution optimised for biomass production:  187.35362997658078\n",
      "Number of core reactions:  838\n"
     ]
    }
   ],
   "source": [
    "#Original FBA solution, when optimised for biomass production. \n",
    "model.objective = x('biomass_human')\n",
    "solution = model.optimize()\n",
    "original_biomass_solution = (solution['biomass_human'])\n",
    "print('Original FBA solution optimised for biomass production: ', original_biomass_solution)\n",
    "\n",
    "core_reactions = []\n",
    "\n",
    "for r in model.reactions:\n",
    "    if solution[r.id] != 0:\n",
    "        core_reactions.append(r.id) #if the FBA solution is never 0, reactions is labelled essential. \n",
    "\n",
    "print('Number of core reactions: ', len(core_reactions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "western-wings",
   "metadata": {},
   "source": [
    "## What is the coverage of the CORE reactions by our proteomics?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "sustained-collaboration",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximize\n",
      "1.0*biomass_human - 1.0*biomass_human_reverse_fb2f2\n",
      "Number of core reactions =  838\n",
      "Coverage of core reactions =  187\n"
     ]
    }
   ],
   "source": [
    "matched_ANDs, fully_matched_ANDs = match_ANDs(FC0_ANDs)\n",
    "matched_ORs = match(FC0_ORs)\n",
    "matched_ANDORs = match(FC0_ANDORs)\n",
    "matched_ones = match(FC0_ones)\n",
    "\n",
    "model.objective = 'biomass_human'\n",
    "print(model.objective)\n",
    "\n",
    "all_matched = matched_ANDs + matched_ANDORs + matched_ORs + matched_ones\n",
    "\n",
    "matched_core_reactions = []\n",
    "\n",
    "for r in core_reactions:\n",
    "    if r in all_matched:\n",
    "        matched_core_reactions.append(r)\n",
    "\n",
    "print('Number of core reactions = ', len(core_reactions))\n",
    "print('Coverage of core reactions = ', len(matched_core_reactions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "academic-clerk",
   "metadata": {},
   "source": [
    "## Scaling the proteomics dataset, according to highest expression value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sticky-action",
   "metadata": {},
   "source": [
    "### Scaling FC0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "certified-binary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " <--> 1.0 UTP ,  ENSG00000153201 :  90748.11355666666\n",
      " <--> 1.0 DNA ,  ENSG00000153201 :  90748.11355666666\n",
      "90748.11355666666\n"
     ]
    }
   ],
   "source": [
    "#printing the highest value of FC0 samples, and the corresponding reactions. \n",
    "#highest expression value will be used for scaling\n",
    "\n",
    "high = []\n",
    "\n",
    "for key, val in FC0.items():\n",
    "    high.append(val)\n",
    "    \n",
    "high.sort()\n",
    "\n",
    "for key, val in FC0.items():\n",
    "    if val == high[-1]:\n",
    "        for r in model.reactions:\n",
    "            if key in r.gene_reaction_rule and r.id in all_matched:\n",
    "                print(r.name, ', ', key, ': ', val)\n",
    "                \n",
    "print(high[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "demonstrated-nancy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "653\n",
      "3972\n",
      "3282\n",
      "129\n"
     ]
    }
   ],
   "source": [
    "def scale(x): # x = library\n",
    "    dic1 = {}\n",
    "    for key, val in x.items():\n",
    "        dic2 = {}\n",
    "        dic3 = {}\n",
    "        for k, v in val.items():\n",
    "            if type(v) != str:\n",
    "                dic2[k] = (v / 90748.11355666666) * 100\n",
    "            else:\n",
    "                dic3[k] = 'NO DATA'\n",
    "        dic1[key] = {**dic2, **dic3}\n",
    "    return dic1\n",
    "\n",
    "FC0_ANDs_scaled = scale(FC0_ANDs)\n",
    "FC0_ORs_scaled = scale(FC0_ORs)\n",
    "FC0_ones_scaled = scale(FC0_ones)\n",
    "FC0_ANDORs_scaled = scale(FC0_ANDORs)\n",
    "\n",
    "print(len(FC0_ANDs_scaled))\n",
    "print(len(FC0_ORs_scaled))\n",
    "print(len(FC0_ones_scaled))\n",
    "print(len(FC0_ANDORs_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "reduced-saskatchewan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dictionary with reaction ID's and the LOWEST expression value\n",
    "FC0_ANDs_constrain_full_matched = {}\n",
    "for key, val in FC0_ANDs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC0_ANDs_constrain_full_matched[key] = l[0]\n",
    "\n",
    "#same as above but not zeroing reactions with missing genes\n",
    "FC0_ANDs_constrain = {}\n",
    "for key, val in FC0_ANDs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            continue\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC0_ANDs_constrain[key] = l[0]\n",
    "    \n",
    "#Creating a dictionary with reaction ID's and the HIGHEST expression value\n",
    "FC0_ORs_constrain = {}\n",
    "for key, val in FC0_ORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort(reverse = True)\n",
    "        FC0_ORs_constrain[key] = l[0]\n",
    "        \n",
    "#constraining ANDORs to highest or lowest\n",
    "FC0_ANDORs_constrain_low = {}\n",
    "for key, val in FC0_ANDORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            continue\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC0_ANDORs_constrain_low[key] = l[0]\n",
    "        \n",
    "FC0_ANDORs_constrain_high = {}\n",
    "for key, val in FC0_ORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort(reverse = True)\n",
    "        FC0_ANDORs_constrain_high[key] = l[0]\n",
    "        \n",
    "#simplifying single gene ones\n",
    "\n",
    "FC0_ones_constrain = {}\n",
    "for key, val in FC0_ones_scaled.items():\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            FC0_ones_constrain[key] = 0\n",
    "        else:\n",
    "            FC0_ones_constrain[key] = v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fiscal-entry",
   "metadata": {},
   "source": [
    "### Scaling FC100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "wanted-transcription",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " <--> 1.0 UTP ,  ENSG00000153201 :  73461.95941666666\n",
      " <--> 1.0 DNA ,  ENSG00000153201 :  73461.95941666666\n",
      "73461.95941666666\n"
     ]
    }
   ],
   "source": [
    "high = []\n",
    "\n",
    "for key, val in FC100.items():\n",
    "    high.append(val)\n",
    "    \n",
    "high.sort()\n",
    "\n",
    "for key, val in FC100.items():\n",
    "    if val == high[-1]:\n",
    "        for r in model.reactions:\n",
    "            if key in r.gene_reaction_rule and r.id in all_matched:\n",
    "                print(r.name, ', ', key, ': ', val) \n",
    "                \n",
    "print(high[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "binding-observation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "653\n",
      "3972\n",
      "3282\n",
      "129\n"
     ]
    }
   ],
   "source": [
    "def scale(x): # x = library\n",
    "    dic1 = {}\n",
    "    for key, val in x.items():\n",
    "        dic2 = {}\n",
    "        dic3 = {}\n",
    "        for k, v in val.items():\n",
    "            if type(v) != str:\n",
    "                dic2[k] = (v / 73461.95941666666) * 100\n",
    "            else:\n",
    "                dic3[k] = 'NO DATA'\n",
    "        dic1[key] = {**dic2, **dic3}\n",
    "    return dic1\n",
    "\n",
    "FC100_ANDs_scaled = scale(FC100_ANDs)\n",
    "FC100_ORs_scaled = scale(FC100_ORs)\n",
    "FC100_ones_scaled = scale(FC100_ones)\n",
    "FC100_ANDORs_scaled = scale(FC100_ANDORs)\n",
    "\n",
    "print(len(FC100_ANDs_scaled))\n",
    "print(len(FC100_ORs_scaled))\n",
    "print(len(FC100_ones_scaled))\n",
    "print(len(FC100_ANDORs_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "alert-bridal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dictionary with reaction ID's and the LOWEST expression value\n",
    "FC100_ANDs_constrain_full_matched = {}\n",
    "for key, val in FC100_ANDs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC100_ANDs_constrain_full_matched[key] = l[0]\n",
    "\n",
    "#same as above but not zeroing reactions with missing genes\n",
    "FC100_ANDs_constrain = {}\n",
    "for key, val in FC100_ANDs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            continue\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC100_ANDs_constrain[key] = l[0]\n",
    "    \n",
    "#Creating a dictionary with reaction ID's and the HIGHEST expression value\n",
    "FC100_ORs_constrain = {}\n",
    "for key, val in FC100_ORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort(reverse = True)\n",
    "        FC100_ORs_constrain[key] = l[0]\n",
    "        \n",
    "#constraining ANDORs to highest or lowest (cant b arsed to figure out a better way currently :))\n",
    "FC100_ANDORs_constrain_low = {}\n",
    "for key, val in FC100_ANDORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            continue\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC100_ANDORs_constrain_low[key] = l[0]\n",
    "        \n",
    "FC100_ANDORs_constrain_high = {}\n",
    "for key, val in FC100_ORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort(reverse = True)\n",
    "        FC100_ANDORs_constrain_high[key] = l[0]\n",
    "        \n",
    "#simplifying single gene ones\n",
    "\n",
    "FC100_ones_constrain = {}\n",
    "for key, val in FC100_ones_scaled.items():\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            FC100_ones_constrain[key] = 0\n",
    "        else:\n",
    "            FC100_ones_constrain[key] = v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "following-bandwidth",
   "metadata": {},
   "source": [
    "### Scaling FC500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "certified-marketplace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59900.516429999996\n"
     ]
    }
   ],
   "source": [
    "high = []\n",
    "\n",
    "for key, val in FC500.items():\n",
    "    high.append(val)\n",
    "    \n",
    "high.sort()\n",
    "\n",
    "for key, val in FC500.items():\n",
    "    if val == high[-1]:\n",
    "        for r in model.reactions:\n",
    "            if key in r.gene_reaction_rule and r.id in all_matched:\n",
    "                print(r.name, ', ', key, ': ', val)\n",
    "                \n",
    "print(high[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "induced-lithuania",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "653\n",
      "3972\n",
      "3282\n",
      "129\n"
     ]
    }
   ],
   "source": [
    "def scale(x): # x = library\n",
    "    dic1 = {}\n",
    "    for key, val in x.items():\n",
    "        dic2 = {}\n",
    "        dic3 = {}\n",
    "        for k, v in val.items():\n",
    "            if type(v) != str:\n",
    "                dic2[k] = (v / 59900.516429999996) * 100\n",
    "            else:\n",
    "                dic3[k] = 'NO DATA'\n",
    "        dic1[key] = {**dic2, **dic3}\n",
    "    return dic1\n",
    "\n",
    "FC500_ANDs_scaled = scale(FC500_ANDs)\n",
    "FC500_ORs_scaled = scale(FC500_ORs)\n",
    "FC500_ones_scaled = scale(FC500_ones)\n",
    "FC500_ANDORs_scaled = scale(FC500_ANDORs)\n",
    "\n",
    "print(len(FC500_ANDs_scaled))\n",
    "print(len(FC500_ORs_scaled))\n",
    "print(len(FC500_ones_scaled))\n",
    "print(len(FC500_ANDORs_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "olive-siemens",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dictionary with reaction ID's and the LOWEST expression value\n",
    "FC500_ANDs_constrain_full_matched = {}\n",
    "for key, val in FC500_ANDs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC500_ANDs_constrain_full_matched[key] = l[0]\n",
    "\n",
    "#same as above but not zeroing reactions with missing genes\n",
    "FC500_ANDs_constrain = {}\n",
    "for key, val in FC500_ANDs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            continue\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC500_ANDs_constrain[key] = l[0]\n",
    "    \n",
    "#Creating a dictionary with reaction ID's and the HIGHEST expression value\n",
    "FC500_ORs_constrain = {}\n",
    "for key, val in FC500_ORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort(reverse = True)\n",
    "        FC500_ORs_constrain[key] = l[0]\n",
    "        \n",
    "#constraining ANDORs to highest or lowest (cant b arsed to figure out a better way currently :))\n",
    "FC500_ANDORs_constrain_low = {}\n",
    "for key, val in FC500_ANDORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            continue\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort()\n",
    "        FC500_ANDORs_constrain_low[key] = l[0]\n",
    "        \n",
    "FC500_ANDORs_constrain_high = {}\n",
    "for key, val in FC500_ORs_scaled.items():\n",
    "    l = []\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            l.append(0)\n",
    "        else:\n",
    "            l.append(v)\n",
    "    if len(l) != 0:\n",
    "        l.sort(reverse = True)\n",
    "        FC500_ANDORs_constrain_high[key] = l[0]\n",
    "        \n",
    "#simplifying single gene ones\n",
    "\n",
    "FC500_ones_constrain = {}\n",
    "for key, val in FC500_ones_scaled.items():\n",
    "    for k, v in val.items():\n",
    "        if v == 'NO DATA':\n",
    "            FC500_ones_constrain[key] = 0\n",
    "        else:\n",
    "            FC500_ones_constrain[key] = v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transparent-partner",
   "metadata": {},
   "source": [
    "### Comparing dictionaries of annotated reaction rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "according-distribution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC0_ORs_constrain:  3972\n",
      "FC100_ORs_constrain:  3972\n",
      "FC500_ORs_constrain:  3972\n",
      "FC0_ANDs_constrain:  489\n",
      "FC100_ANDs_constrain:  489\n",
      "FC500_ANDs_constrain:  489\n",
      "FC0_ANDORs_constrain_low:  100\n",
      "FC100_ANDORs_constrain_low:  100\n",
      "FC500_ANDORs_constrain_low:  100\n",
      "FC0_ANDORs_constrain_high:  3972\n",
      "FC100_ANDORs_constrain_high:  3972\n",
      "FC500_ANDORs_constrain_high:  3972\n"
     ]
    }
   ],
   "source": [
    "print('FC0_ORs_constrain: ', len(FC0_ORs_constrain))\n",
    "print('FC100_ORs_constrain: ', len(FC100_ORs_constrain))\n",
    "print('FC500_ORs_constrain: ', len(FC500_ORs_constrain))\n",
    "print('FC0_ANDs_constrain: ', len(FC0_ANDs_constrain))\n",
    "print('FC100_ANDs_constrain: ', len(FC100_ANDs_constrain))\n",
    "print('FC500_ANDs_constrain: ', len(FC500_ANDs_constrain))\n",
    "print('FC0_ANDORs_constrain_low: ', len(FC0_ANDORs_constrain_low))\n",
    "print('FC100_ANDORs_constrain_low: ', len(FC100_ANDORs_constrain_low))\n",
    "print('FC500_ANDORs_constrain_low: ', len(FC500_ANDORs_constrain_low))\n",
    "print('FC0_ANDORs_constrain_high: ', len(FC0_ANDORs_constrain_high))\n",
    "print('FC100_ANDORs_constrain_high: ', len(FC100_ANDORs_constrain_high))\n",
    "print('FC500_ANDORs_constrain_high: ', len(FC500_ANDORs_constrain_high))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retired-curve",
   "metadata": {},
   "source": [
    "# Which of the core reactions had a significant change correlating with Myc expression?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "million-gambling",
   "metadata": {},
   "source": [
    "## Print flux through all reactions of model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "everyday-ideal",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux (zero myc)</th>\n",
       "      <th>flux (low myc)</th>\n",
       "      <th>flux (high myc)</th>\n",
       "      <th>ratio high/zero flux</th>\n",
       "      <th>standard deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [reaction, flux (zero myc), flux (low myc), flux (high myc), ratio high/zero flux, standard deviation]\n",
       "Index: []"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating empty df to append with only those reactions with significant change in flux\n",
    "\n",
    "column_names = ['reaction', 'flux (zero myc)', 'flux (low myc)', 'flux (high myc)', 'ratio high/zero flux', 'standard deviation']\n",
    "df = pd.DataFrame(columns = column_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "other-appendix",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13096"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reactions = model.reactions\n",
    "len(reactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "demanding-dividend",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating function to scan model reactions and annotate empty df with reactions plus SD. \n",
    "\n",
    "def df_fluxes(reaction): #reaction = reaction id\n",
    "    x = model.reactions.get_by_id\n",
    "    y = model.metabolites.get_by_id\n",
    "    if reaction in FC0_ORs_constrain: \n",
    "        rxn_flux = list()\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ORs_constrain[reaction]) #setting bounds to be equal to protein expression value from proteomics dictionaries. \n",
    "        x(reaction).lower_bound = (FC0_ORs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ORs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ORs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ORs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ORs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        \n",
    "        df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]), standard_dev]\n",
    "        \n",
    "        \n",
    "    elif reaction in FC0_ANDs_constrain:\n",
    "        rxn_flux = list()\n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ANDs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ANDs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ANDs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ANDs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ANDs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ANDs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        \n",
    "        df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]),  standard_dev]\n",
    "    \n",
    "    \n",
    "    elif reaction in FC0_ANDORs_constrain_low:\n",
    "        rxn_flux = list()\n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ANDORs_constrain_low[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ANDORs_constrain_low[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ANDORs_constrain_low[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ANDORs_constrain_low[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ANDORs_constrain_low[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ANDORs_constrain_low[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        \n",
    "        df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]), standard_dev]\n",
    "            \n",
    "        \n",
    "    elif reaction in FC0_ANDORs_constrain_high:\n",
    "        rxn_flux = list()\n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ANDORs_constrain_high[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ANDORs_constrain_high[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ANDORs_constrain_high[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ANDORs_constrain_high[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ANDORs_constrain_high[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ANDORs_constrain_high[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        \n",
    "        df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]), standard_dev]\n",
    "        \n",
    "    else:\n",
    "        print('no data available')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "undefined-federation",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no data available\n",
      "no data available\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-62-a0686e39570f>:33: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]), standard_dev]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no data available\n",
      "no data available\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/sklearn-env/lib/python3.9/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.\n",
      "  warn(f\"Solver status is '{status}'.\", UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-62-a0686e39570f>:33: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]), standard_dev]\n"
     ]
    },
    {
     "name": "stdout",
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    {
     "name": "stderr",
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     "text": [
      "<ipython-input-62-a0686e39570f>:95: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]), standard_dev]\n"
     ]
    },
    {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-62-a0686e39570f>:64: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  df.loc[reaction,:]=[reaction, rxn_flux[0], rxn_flux[1], rxn_flux[2], (rxn_flux[2]/rxn_flux[0]),  standard_dev]\n"
     ]
    },
    {
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      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n",
      "no data available\n"
     ]
    }
   ],
   "source": [
    "for reaction in reactions:\n",
    "    df_fluxes(reaction.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "coordinated-opera",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux (zero myc)</th>\n",
       "      <th>flux (low myc)</th>\n",
       "      <th>flux (high myc)</th>\n",
       "      <th>ratio high/zero flux</th>\n",
       "      <th>standard deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HMR_3905</th>\n",
       "      <td>HMR_3905</td>\n",
       "      <td>2.014114</td>\n",
       "      <td>2.177677</td>\n",
       "      <td>2.768681</td>\n",
       "      <td>1.37464</td>\n",
       "      <td>0.324108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4099</th>\n",
       "      <td>HMR_4099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4137</th>\n",
       "      <td>HMR_4137</td>\n",
       "      <td>0.039202</td>\n",
       "      <td>0.024945</td>\n",
       "      <td>0.12539</td>\n",
       "      <td>3.198568</td>\n",
       "      <td>0.044373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4281</th>\n",
       "      <td>HMR_4281</td>\n",
       "      <td>17.558968</td>\n",
       "      <td>23.564306</td>\n",
       "      <td>25.023457</td>\n",
       "      <td>1.42511</td>\n",
       "      <td>3.23027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4388</th>\n",
       "      <td>HMR_4388</td>\n",
       "      <td>17.558968</td>\n",
       "      <td>23.564306</td>\n",
       "      <td>25.023457</td>\n",
       "      <td>1.42511</td>\n",
       "      <td>3.23027</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10110</th>\n",
       "      <td>HMR_10110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10115</th>\n",
       "      <td>HMR_10115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10117</th>\n",
       "      <td>HMR_10117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10125</th>\n",
       "      <td>HMR_10125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10126</th>\n",
       "      <td>HMR_10126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4561 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            reaction flux (zero myc) flux (low myc) flux (high myc)  \\\n",
       "HMR_3905    HMR_3905        2.014114       2.177677        2.768681   \n",
       "HMR_4099    HMR_4099             0.0            0.0             0.0   \n",
       "HMR_4137    HMR_4137        0.039202       0.024945         0.12539   \n",
       "HMR_4281    HMR_4281       17.558968      23.564306       25.023457   \n",
       "HMR_4388    HMR_4388       17.558968      23.564306       25.023457   \n",
       "...              ...             ...            ...             ...   \n",
       "HMR_10110  HMR_10110             0.0            0.0             0.0   \n",
       "HMR_10115  HMR_10115             0.0            0.0             0.0   \n",
       "HMR_10117  HMR_10117             0.0            0.0             0.0   \n",
       "HMR_10125  HMR_10125             0.0            0.0             0.0   \n",
       "HMR_10126  HMR_10126             0.0            0.0             0.0   \n",
       "\n",
       "          ratio high/zero flux standard deviation  \n",
       "HMR_3905               1.37464           0.324108  \n",
       "HMR_4099                   NaN                0.0  \n",
       "HMR_4137              3.198568           0.044373  \n",
       "HMR_4281               1.42511            3.23027  \n",
       "HMR_4388               1.42511            3.23027  \n",
       "...                        ...                ...  \n",
       "HMR_10110                  NaN                0.0  \n",
       "HMR_10115                  NaN                0.0  \n",
       "HMR_10117                  NaN                0.0  \n",
       "HMR_10125                  NaN                0.0  \n",
       "HMR_10126                  NaN                0.0  \n",
       "\n",
       "[4561 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "present-institute",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving df so it doesn't get lost. \n",
    "df.to_csv('flux calculations, no significance analysis, zeros included.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "boxed-eleven",
   "metadata": {},
   "source": [
    "## Remove reactions where all flux values are zero. As in we have no data for these reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "exempt-apparel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux (zero myc)</th>\n",
       "      <th>flux (low myc)</th>\n",
       "      <th>flux (high myc)</th>\n",
       "      <th>ratio high/zero flux</th>\n",
       "      <th>standard deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HMR_3905</th>\n",
       "      <td>HMR_3905</td>\n",
       "      <td>2.014114</td>\n",
       "      <td>2.177677</td>\n",
       "      <td>2.768681</td>\n",
       "      <td>1.37464</td>\n",
       "      <td>0.324108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4137</th>\n",
       "      <td>HMR_4137</td>\n",
       "      <td>0.039202</td>\n",
       "      <td>0.024945</td>\n",
       "      <td>0.12539</td>\n",
       "      <td>3.198568</td>\n",
       "      <td>0.044373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4281</th>\n",
       "      <td>HMR_4281</td>\n",
       "      <td>17.558968</td>\n",
       "      <td>23.564306</td>\n",
       "      <td>25.023457</td>\n",
       "      <td>1.42511</td>\n",
       "      <td>3.23027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4388</th>\n",
       "      <td>HMR_4388</td>\n",
       "      <td>17.558968</td>\n",
       "      <td>23.564306</td>\n",
       "      <td>25.023457</td>\n",
       "      <td>1.42511</td>\n",
       "      <td>3.23027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4283</th>\n",
       "      <td>HMR_4283</td>\n",
       "      <td>0.838896</td>\n",
       "      <td>1.308147</td>\n",
       "      <td>1.12152</td>\n",
       "      <td>1.3369</td>\n",
       "      <td>0.192903</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3HPVSTETCOAhcm</th>\n",
       "      <td>3HPVSTETCOAhcm</td>\n",
       "      <td>2.800339</td>\n",
       "      <td>3.292048</td>\n",
       "      <td>3.163819</td>\n",
       "      <td>1.129798</td>\n",
       "      <td>0.208257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3HPVSTETCOAhcx</th>\n",
       "      <td>3HPVSTETCOAhcx</td>\n",
       "      <td>0.546704</td>\n",
       "      <td>0.442266</td>\n",
       "      <td>0.709254</td>\n",
       "      <td>1.297327</td>\n",
       "      <td>0.109855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOXFVShc</th>\n",
       "      <td>DEOXFVShc</td>\n",
       "      <td>0.546704</td>\n",
       "      <td>0.442266</td>\n",
       "      <td>0.709254</td>\n",
       "      <td>1.297327</td>\n",
       "      <td>0.109855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFVSCOAhc</th>\n",
       "      <td>PROFVSCOAhc</td>\n",
       "      <td>0.546704</td>\n",
       "      <td>0.442266</td>\n",
       "      <td>0.709254</td>\n",
       "      <td>1.297327</td>\n",
       "      <td>0.109855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYOOm3i</th>\n",
       "      <td>CYOOm3i</td>\n",
       "      <td>0.228425</td>\n",
       "      <td>0.290933</td>\n",
       "      <td>0.381707</td>\n",
       "      <td>1.671036</td>\n",
       "      <td>0.062931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2057 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      reaction flux (zero myc) flux (low myc) flux (high myc)  \\\n",
       "HMR_3905              HMR_3905        2.014114       2.177677        2.768681   \n",
       "HMR_4137              HMR_4137        0.039202       0.024945         0.12539   \n",
       "HMR_4281              HMR_4281       17.558968      23.564306       25.023457   \n",
       "HMR_4388              HMR_4388       17.558968      23.564306       25.023457   \n",
       "HMR_4283              HMR_4283        0.838896       1.308147         1.12152   \n",
       "...                        ...             ...            ...             ...   \n",
       "3HPVSTETCOAhcm  3HPVSTETCOAhcm        2.800339       3.292048        3.163819   \n",
       "3HPVSTETCOAhcx  3HPVSTETCOAhcx        0.546704       0.442266        0.709254   \n",
       "DEOXFVShc            DEOXFVShc        0.546704       0.442266        0.709254   \n",
       "PROFVSCOAhc        PROFVSCOAhc        0.546704       0.442266        0.709254   \n",
       "CYOOm3i                CYOOm3i        0.228425       0.290933        0.381707   \n",
       "\n",
       "               ratio high/zero flux standard deviation  \n",
       "HMR_3905                    1.37464           0.324108  \n",
       "HMR_4137                   3.198568           0.044373  \n",
       "HMR_4281                    1.42511            3.23027  \n",
       "HMR_4388                    1.42511            3.23027  \n",
       "HMR_4283                     1.3369           0.192903  \n",
       "...                             ...                ...  \n",
       "3HPVSTETCOAhcm             1.129798           0.208257  \n",
       "3HPVSTETCOAhcx             1.297327           0.109855  \n",
       "DEOXFVShc                  1.297327           0.109855  \n",
       "PROFVSCOAhc                1.297327           0.109855  \n",
       "CYOOm3i                    1.671036           0.062931  \n",
       "\n",
       "[2057 rows x 6 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing rows where all three flux values are zero. \n",
    "\n",
    "column_names = ['reaction', 'flux (zero myc)', 'flux (low myc)', 'flux (high myc)', 'ratio high/zero flux', 'standard deviation']\n",
    "no_zeros = pd.DataFrame(columns = column_names)\n",
    "\n",
    "for n in range(4561):\n",
    "    if df.iloc[n,:][1] !=0:\n",
    "        if df.iloc[n,:][2] !=0:\n",
    "            if df.iloc[n,:][3] !=0:\n",
    "                standard_dev = np.std(df.iloc[n,:][[1,2,3]])\n",
    "                no_zeros.loc[df.iloc[n,:][0],:]=[df.iloc[n,:][0], df.iloc[n,:][1], df.iloc[n,:][2], df.iloc[n,:][3], (df.iloc[n,:][3]/df.iloc[n,:][1]), standard_dev]\n",
    "                \n",
    "no_zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "agricultural-aerospace",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving df so it doesn't get lost. \n",
    "no_zeros.to_csv('flux calculations, no significance analysis, zeros removed.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-layout",
   "metadata": {},
   "source": [
    "## Add column to df for significance calculations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "passing-metabolism",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux (zero myc)</th>\n",
       "      <th>flux (low myc)</th>\n",
       "      <th>flux (high myc)</th>\n",
       "      <th>ratio high/zero flux</th>\n",
       "      <th>standard deviation</th>\n",
       "      <th>Significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HMR_3905</th>\n",
       "      <td>HMR_3905</td>\n",
       "      <td>2.014114</td>\n",
       "      <td>2.177677</td>\n",
       "      <td>2.768681</td>\n",
       "      <td>1.37464</td>\n",
       "      <td>0.324108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4137</th>\n",
       "      <td>HMR_4137</td>\n",
       "      <td>0.039202</td>\n",
       "      <td>0.024945</td>\n",
       "      <td>0.12539</td>\n",
       "      <td>3.198568</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4281</th>\n",
       "      <td>HMR_4281</td>\n",
       "      <td>17.558968</td>\n",
       "      <td>23.564306</td>\n",
       "      <td>25.023457</td>\n",
       "      <td>1.42511</td>\n",
       "      <td>3.23027</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4388</th>\n",
       "      <td>HMR_4388</td>\n",
       "      <td>17.558968</td>\n",
       "      <td>23.564306</td>\n",
       "      <td>25.023457</td>\n",
       "      <td>1.42511</td>\n",
       "      <td>3.23027</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4283</th>\n",
       "      <td>HMR_4283</td>\n",
       "      <td>0.838896</td>\n",
       "      <td>1.308147</td>\n",
       "      <td>1.12152</td>\n",
       "      <td>1.3369</td>\n",
       "      <td>0.192903</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3HPVSTETCOAhcm</th>\n",
       "      <td>3HPVSTETCOAhcm</td>\n",
       "      <td>2.800339</td>\n",
       "      <td>3.292048</td>\n",
       "      <td>3.163819</td>\n",
       "      <td>1.129798</td>\n",
       "      <td>0.208257</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3HPVSTETCOAhcx</th>\n",
       "      <td>3HPVSTETCOAhcx</td>\n",
       "      <td>0.546704</td>\n",
       "      <td>0.442266</td>\n",
       "      <td>0.709254</td>\n",
       "      <td>1.297327</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEOXFVShc</th>\n",
       "      <td>DEOXFVShc</td>\n",
       "      <td>0.546704</td>\n",
       "      <td>0.442266</td>\n",
       "      <td>0.709254</td>\n",
       "      <td>1.297327</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFVSCOAhc</th>\n",
       "      <td>PROFVSCOAhc</td>\n",
       "      <td>0.546704</td>\n",
       "      <td>0.442266</td>\n",
       "      <td>0.709254</td>\n",
       "      <td>1.297327</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYOOm3i</th>\n",
       "      <td>CYOOm3i</td>\n",
       "      <td>0.228425</td>\n",
       "      <td>0.290933</td>\n",
       "      <td>0.381707</td>\n",
       "      <td>1.671036</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2057 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      reaction flux (zero myc) flux (low myc) flux (high myc)  \\\n",
       "HMR_3905              HMR_3905        2.014114       2.177677        2.768681   \n",
       "HMR_4137              HMR_4137        0.039202       0.024945         0.12539   \n",
       "HMR_4281              HMR_4281       17.558968      23.564306       25.023457   \n",
       "HMR_4388              HMR_4388       17.558968      23.564306       25.023457   \n",
       "HMR_4283              HMR_4283        0.838896       1.308147         1.12152   \n",
       "...                        ...             ...            ...             ...   \n",
       "3HPVSTETCOAhcm  3HPVSTETCOAhcm        2.800339       3.292048        3.163819   \n",
       "3HPVSTETCOAhcx  3HPVSTETCOAhcx        0.546704       0.442266        0.709254   \n",
       "DEOXFVShc            DEOXFVShc        0.546704       0.442266        0.709254   \n",
       "PROFVSCOAhc        PROFVSCOAhc        0.546704       0.442266        0.709254   \n",
       "CYOOm3i                CYOOm3i        0.228425       0.290933        0.381707   \n",
       "\n",
       "               ratio high/zero flux standard deviation  Significance  \n",
       "HMR_3905                    1.37464           0.324108           NaN  \n",
       "HMR_4137                   3.198568           0.044373           NaN  \n",
       "HMR_4281                    1.42511            3.23027           NaN  \n",
       "HMR_4388                    1.42511            3.23027           NaN  \n",
       "HMR_4283                     1.3369           0.192903           NaN  \n",
       "...                             ...                ...           ...  \n",
       "3HPVSTETCOAhcm             1.129798           0.208257           NaN  \n",
       "3HPVSTETCOAhcx             1.297327           0.109855           NaN  \n",
       "DEOXFVShc                  1.297327           0.109855           NaN  \n",
       "PROFVSCOAhc                1.297327           0.109855           NaN  \n",
       "CYOOm3i                    1.671036           0.062931           NaN  \n",
       "\n",
       "[2057 rows x 7 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_zeros_significance_col = no_zeros.reindex(columns = no_zeros.columns.tolist() + ['Significance'])\n",
    "no_zeros_significance_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fossil-jackson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flux (zero myc)    2.014114\n",
       "flux (low myc)     2.177677\n",
       "flux (high myc)    2.768681\n",
       "Name: HMR_3905, dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_zeros_significance_col.iloc[0,:][[1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "specialized-crisis",
   "metadata": {},
   "outputs": [],
   "source": [
    "#apply signif_calc to table of flux ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "beneficial-substitute",
   "metadata": {},
   "outputs": [],
   "source": [
    "def signif_calc(n,reaction):\n",
    "    standard_dev = np.std(no_zeros_significance_col.iloc[n,:][[1,2,3]])\n",
    "    \n",
    "    if (no_zeros_significance_col.iloc[n,:][3] - standard_dev) > (no_zeros_significance_col.iloc[n,:][1] + standard_dev):\n",
    "        no_zeros_significance_col.loc[reaction,'Significance']='Significant'       \n",
    "    elif (no_zeros_significance_col.iloc[n,:][3] + standard_dev) < (no_zeros_significance_col.iloc[n,:][1] - standard_dev):\n",
    "        no_zeros_significance_col.loc[reaction,'Significance']='Significant'\n",
    "    else:\n",
    "        no_zeros_significance_col.loc[reaction,'Significance']='Insignificant'\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "conscious-yemen",
   "metadata": {},
   "outputs": [],
   "source": [
    "for num in range(2057):\n",
    "    signif_calc(num, no_zeros_significance_col.iloc[num,:][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "integrated-genetics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant\n",
      "Insignificant\n",
      "Significant\n",
      "Significant\n",
      "Insignificant\n",
      "Insignificant\n",
      "Significant\n",
      "Significant\n",
      "Insignificant\n",
      "Insignificant\n"
     ]
    }
   ],
   "source": [
    "for n in range(10):\n",
    "    print(no_zeros_significance_col.iloc[n,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "binding-laptop",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of filtered_dataframe:  1470\n",
      "Number of significant values:  1470\n",
      "Number of insignificant values:  587\n",
      "\n",
      "\n",
      "First 20 of filtered dataframe...\n",
      "\n",
      "\n",
      "                  reaction flux (zero myc) flux (low myc) flux (high myc)  \\\n",
      "HMR_3905          HMR_3905        2.014114       2.177677        2.768681   \n",
      "HMR_4281          HMR_4281       17.558968      23.564306       25.023457   \n",
      "HMR_4388          HMR_4388       17.558968      23.564306       25.023457   \n",
      "HMR_4379          HMR_4379         3.34058       4.245707        4.230956   \n",
      "HMR_4301          HMR_4301         3.34058       4.245707        4.230956   \n",
      "...                    ...             ...            ...             ...   \n",
      "ARGN1ASPMDte  ARGN1ASPMDte        1.164762       1.718659        2.807302   \n",
      "BGAL1l              BGAL1l         0.72816       0.845671        0.924816   \n",
      "BGAL2l              BGAL2l         0.72816       0.845671        0.924816   \n",
      "NEU11l              NEU11l         0.72816       0.845671        0.924816   \n",
      "CYOOm3i            CYOOm3i        0.228425       0.290933        0.381707   \n",
      "\n",
      "             ratio high/zero flux standard deviation  \n",
      "HMR_3905                  1.37464           0.324108  \n",
      "HMR_4281                  1.42511            3.23027  \n",
      "HMR_4388                  1.42511            3.23027  \n",
      "HMR_4379                 1.266533           0.423247  \n",
      "HMR_4301                 1.266533           0.423247  \n",
      "...                           ...                ...  \n",
      "ARGN1ASPMDte             2.410195           0.682307  \n",
      "BGAL1l                   1.270072           0.080792  \n",
      "BGAL2l                   1.270072           0.080792  \n",
      "NEU11l                   1.270072           0.080792  \n",
      "CYOOm3i                  1.671036           0.062931  \n",
      "\n",
      "[1470 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "column_names = ['reaction', 'flux (zero myc)', 'flux (low myc)', 'flux (high myc)', 'ratio high/zero flux', 'standard deviation']\n",
    "significant_proteins = pd.DataFrame(columns = column_names)\n",
    "\n",
    "significant_values = []\n",
    "insignificant_values = []\n",
    "\n",
    "for n in range(2057):\n",
    "    if no_zeros_significance_col.iloc[n,6] == 'Significant':\n",
    "        significant_values.append(x)\n",
    "        standard_dev = np.std(no_zeros_significance_col.iloc[n,:][[1,2,3]])\n",
    "        significant_proteins.loc[no_zeros_significance_col.iloc[n,:][0],:]=[no_zeros_significance_col.iloc[n,:][0], no_zeros_significance_col.iloc[n,:][1], no_zeros_significance_col.iloc[n,:][2], no_zeros_significance_col.iloc[n,:][3], (no_zeros_significance_col.iloc[n,:][3]/no_zeros_significance_col.iloc[n,:][1]), standard_dev]\n",
    "    if no_zeros_significance_col.iloc[n,6] == 'Insignificant':\n",
    "        insignificant_values.append(x)\n",
    "\n",
    "print('Size of filtered_dataframe: ', len(significant_proteins))\n",
    "print('Number of significant values: ', len(significant_values))\n",
    "print('Number of insignificant values: ', len(insignificant_values))\n",
    "print('\\n')\n",
    "print('First 20 of filtered dataframe...')\n",
    "print('\\n')\n",
    "print(significant_proteins.iloc[:,0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "parallel-mailing",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving df so it doesn't get lost. \n",
    "significant_proteins.to_csv('flux calculations, significance analysis.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sought-arrival",
   "metadata": {},
   "source": [
    "## Find overlap between significantly regulated reactions and core reactions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "union-alias",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69\n",
      "['HMR_4365', 'HMR_4368', 'HMR_4371', 'HMR_4373', 'HMR_5395', 'HMR_5396', 'HMR_4404', 'HMR_4501', 'HMR_4568', 'HMR_4710', 'HMR_4002', 'HMR_4004', 'HMR_4012', 'HMR_4496', 'HMR_4615', 'HMR_4695', 'HMR_7144', 'HMR_4030', 'HMR_4635', 'HMR_3931', 'HMR_4485', 'HMR_6622', 'HMR_4083', 'HMR_7161', 'HMR_7887', 'HMR_7889', 'HMR_8443', 'HMR_8444', 'HMR_3829', 'HMR_6725', 'HMR_3877', 'HMR_4300', 'HMR_5145', 'HMR_4456', 'HMR_4652', 'HMR_6912', 'HMR_6914', 'HMR_6916', 'HMR_6918', 'HMR_0188', 'HMR_1020', 'HMR_3144', 'HMR_3146', 'HMR_0598', 'HMR_6643', 'HMR_6650', 'HMR_6651', 'HMR_6657', 'HMR_6658', 'HMR_6507', 'HMR_4201', 'HMR_5406', 'HMR_0155', 'HMR_0173', 'HMR_0179', 'HMR_5076', 'HMR_5077', 'HMR_5078', 'HMR_5079', 'HMR_5088', 'HMR_5934', 'HMR_6007', 'HMR_6384', 'HMR_6628', 'HMR_8075', 'HMR_3825', 'HMR_6328', 'r1432', 'r1433']\n"
     ]
    }
   ],
   "source": [
    "overlap = []\n",
    "reactions_ids = significant_proteins.iloc[:,0]\n",
    "\n",
    "for rxn in core_reactions:\n",
    "    if rxn in reactions_ids:\n",
    "        overlap.append(rxn)\n",
    "        \n",
    "print(len(overlap))\n",
    "print(overlap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "sought-copyright",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux (zero myc)</th>\n",
       "      <th>flux (low myc)</th>\n",
       "      <th>flux (high myc)</th>\n",
       "      <th>ratio high/zero flux</th>\n",
       "      <th>standard deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [reaction, flux (zero myc), flux (low myc), flux (high myc), ratio high/zero flux, standard deviation]\n",
       "Index: []"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['reaction', 'flux (zero myc)', 'flux (low myc)', 'flux (high myc)', 'ratio high/zero flux', 'standard deviation']\n",
    "mycregulated_core_reactions_df = pd.DataFrame(columns = column_names)\n",
    "mycregulated_core_reactions_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "tutorial-basin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1470"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(significant_proteins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "surrounded-rebound",
   "metadata": {},
   "outputs": [],
   "source": [
    "for reaction in overlap:\n",
    "    for n in range(1470):\n",
    "        if reaction in significant_proteins.iloc[n,:][0]:\n",
    "            mycregulated_core_reactions_df.loc[significant_proteins.iloc[n,:][0],:]=[significant_proteins.iloc[n,:][0], significant_proteins.iloc[n,:][1], significant_proteins.iloc[n,:][2], significant_proteins.iloc[n,:][3], significant_proteins.iloc[n,:][4], significant_proteins.iloc[n,:][5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "acoustic-heading",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>flux (zero myc)</th>\n",
       "      <th>flux (low myc)</th>\n",
       "      <th>flux (high myc)</th>\n",
       "      <th>ratio high/zero flux</th>\n",
       "      <th>standard deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HMR_4373</th>\n",
       "      <td>HMR_4373</td>\n",
       "      <td>-20.135465</td>\n",
       "      <td>72.161496</td>\n",
       "      <td>73.746971</td>\n",
       "      <td>-3.662541</td>\n",
       "      <td>43.887678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4004</th>\n",
       "      <td>HMR_4004</td>\n",
       "      <td>10.439975</td>\n",
       "      <td>9.712226</td>\n",
       "      <td>3.817166</td>\n",
       "      <td>0.36563</td>\n",
       "      <td>2.965411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4002</th>\n",
       "      <td>HMR_4002</td>\n",
       "      <td>10.439975</td>\n",
       "      <td>9.712226</td>\n",
       "      <td>3.817166</td>\n",
       "      <td>0.36563</td>\n",
       "      <td>2.965411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_6384</th>\n",
       "      <td>HMR_6384</td>\n",
       "      <td>2.131566</td>\n",
       "      <td>1.024609</td>\n",
       "      <td>0.939435</td>\n",
       "      <td>0.440725</td>\n",
       "      <td>0.543015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_3877</th>\n",
       "      <td>HMR_3877</td>\n",
       "      <td>14.719804</td>\n",
       "      <td>10.698804</td>\n",
       "      <td>7.781332</td>\n",
       "      <td>0.52863</td>\n",
       "      <td>2.844536</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_3931</th>\n",
       "      <td>HMR_3931</td>\n",
       "      <td>0.053229</td>\n",
       "      <td>0.175816</td>\n",
       "      <td>0.274221</td>\n",
       "      <td>5.151715</td>\n",
       "      <td>0.090399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r1433</th>\n",
       "      <td>r1433</td>\n",
       "      <td>0.053229</td>\n",
       "      <td>0.175816</td>\n",
       "      <td>0.274221</td>\n",
       "      <td>5.151715</td>\n",
       "      <td>0.090399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_5934</th>\n",
       "      <td>HMR_5934</td>\n",
       "      <td>0.00456</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.061935</td>\n",
       "      <td>13.581431</td>\n",
       "      <td>0.027587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_0598</th>\n",
       "      <td>HMR_0598</td>\n",
       "      <td>0.008672</td>\n",
       "      <td>0.043181</td>\n",
       "      <td>0.173972</td>\n",
       "      <td>20.060186</td>\n",
       "      <td>0.071197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_7161</th>\n",
       "      <td>HMR_7161</td>\n",
       "      <td>0.004479</td>\n",
       "      <td>0.010597</td>\n",
       "      <td>0.103254</td>\n",
       "      <td>23.050825</td>\n",
       "      <td>0.04519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          reaction flux (zero myc) flux (low myc) flux (high myc)  \\\n",
       "HMR_4373  HMR_4373      -20.135465      72.161496       73.746971   \n",
       "HMR_4004  HMR_4004       10.439975       9.712226        3.817166   \n",
       "HMR_4002  HMR_4002       10.439975       9.712226        3.817166   \n",
       "HMR_6384  HMR_6384        2.131566       1.024609        0.939435   \n",
       "HMR_3877  HMR_3877       14.719804      10.698804        7.781332   \n",
       "...            ...             ...            ...             ...   \n",
       "HMR_3931  HMR_3931        0.053229       0.175816        0.274221   \n",
       "r1433        r1433        0.053229       0.175816        0.274221   \n",
       "HMR_5934  HMR_5934         0.00456       0.002333        0.061935   \n",
       "HMR_0598  HMR_0598        0.008672       0.043181        0.173972   \n",
       "HMR_7161  HMR_7161        0.004479       0.010597        0.103254   \n",
       "\n",
       "         ratio high/zero flux standard deviation  \n",
       "HMR_4373            -3.662541          43.887678  \n",
       "HMR_4004              0.36563           2.965411  \n",
       "HMR_4002              0.36563           2.965411  \n",
       "HMR_6384             0.440725           0.543015  \n",
       "HMR_3877              0.52863           2.844536  \n",
       "...                       ...                ...  \n",
       "HMR_3931             5.151715           0.090399  \n",
       "r1433                5.151715           0.090399  \n",
       "HMR_5934            13.581431           0.027587  \n",
       "HMR_0598            20.060186           0.071197  \n",
       "HMR_7161            23.050825            0.04519  \n",
       "\n",
       "[69 rows x 6 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mycregulated_core_reactions_df = mycregulated_core_reactions_df.sort_values(['ratio high/zero flux'], ascending=[True])\n",
    "mycregulated_core_reactions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "dressed-collector",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycregulated_core_reactions_df.to_csv('flux through myc-regulated core reactions.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "spatial-ridge",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.6625413160724527\n",
      "0.36562975592607944\n",
      "0.36562975592607944\n",
      "0.4407254332398629\n",
      "0.5286301403117841\n",
      "0.8060003659819336\n",
      "0.8060003659819336\n",
      "1.1259723612606929\n",
      "1.1259723612606929\n",
      "1.1771173608558712\n",
      "1.2317970309554476\n",
      "1.2665332963507538\n",
      "1.334129570804433\n",
      "1.337959465734844\n",
      "1.3417078461187721\n",
      "1.3417078461187721\n",
      "1.3532904212293304\n",
      "1.3532904212293304\n",
      "1.3532904212293304\n",
      "1.3690149739253559\n",
      "1.4918963491328734\n",
      "1.4938746447509643\n",
      "1.6102269489151722\n",
      "1.6340163500360199\n",
      "1.6710362107339753\n",
      "1.7111550627275607\n",
      "1.7296954689320139\n",
      "1.8085358546888488\n",
      "1.8145376289149941\n",
      "1.8145376289149941\n",
      "1.8145376289149941\n",
      "1.9256486101363772\n",
      "1.9928034883438819\n",
      "1.9928034883438819\n",
      "1.9928034883438819\n",
      "1.9928034883438819\n",
      "2.0491700769437533\n",
      "2.0568500488038333\n",
      "2.0800417824618727\n",
      "2.1498618532154374\n",
      "2.1498618532154374\n",
      "2.180170639875752\n",
      "2.180170639875752\n",
      "2.303385708568018\n",
      "2.318607741894401\n",
      "2.3639007945214927\n",
      "2.5443156207283084\n",
      "2.567337291793215\n",
      "2.567337291793215\n",
      "2.567337291793215\n",
      "2.933378156576426\n",
      "2.933378156576426\n",
      "3.330313319556349\n",
      "3.330313319556349\n",
      "3.501108342503053\n",
      "3.501108342503053\n",
      "3.501108342503053\n",
      "3.501108342503053\n",
      "4.226628012629885\n",
      "5.133864297274938\n",
      "5.133864297274938\n",
      "5.133864297274938\n",
      "5.133864297274938\n",
      "5.133864297274938\n",
      "5.151715251923344\n",
      "5.151715251923344\n",
      "13.581431045240473\n",
      "20.060186304413346\n",
      "23.05082534297806\n"
     ]
    }
   ],
   "source": [
    "for n in range(69):\n",
    "    print(mycregulated_core_reactions_df.iloc[n,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colored-compiler",
   "metadata": {},
   "source": [
    "## upregulated:downregulated ratio of core reactions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "reserved-spencer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of significantly upregulated core reactions:  63\n",
      "Number of significantly downregulated core reactions:  7\n"
     ]
    }
   ],
   "source": [
    "core_reactions_upregulated = []\n",
    "core_reactions_downregulated = []\n",
    "\n",
    "for n in range(69):\n",
    "    if (mycregulated_core_reactions_df.iloc[n,4]) > 1:\n",
    "        core_reactions_upregulated.append((mycregulated_core_reactions_df.iloc[n,0]))\n",
    "    if (mycregulated_core_reactions_df.iloc[n,4]) < 0: #there is one reaction with around -3 flux which is incorrectly assigned to downregulated\n",
    "        core_reactions_upregulated.append((mycregulated_core_reactions_df.iloc[n,0]))\n",
    "    if (mycregulated_core_reactions_df.iloc[n,4]) < 1:\n",
    "        core_reactions_downregulated.append((mycregulated_core_reactions_df.iloc[n,0]))\n",
    "        \n",
    "print('Number of significantly upregulated core reactions: ', len(core_reactions_upregulated))\n",
    "print('Number of significantly downregulated core reactions: ', len(core_reactions_downregulated))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-diameter",
   "metadata": {},
   "source": [
    "##### Plot the flux through these myc-regulated core reactions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "failing-closing",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function for plotting reaction flux, and annotating standard deviation. \n",
    "\n",
    "def std_flux_plot(reaction): #reaction = reaction id\n",
    "    x = model.reactions.get_by_id\n",
    "    y = model.metabolites.get_by_id\n",
    "    if reaction in FC0_ORs_constrain: \n",
    "        rxn_flux = list()\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ORs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ORs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ORs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ORs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ORs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ORs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        print(reaction, ': ', 'flux; ', rxn_flux, ', ', 'standard deviation; ', standard_dev)\n",
    "        q = ['zero', 'intermediate', 'high']\n",
    "        e = np.array([standard_dev, standard_dev, standard_dev])\n",
    "        plt.errorbar(q, rxn_flux, e, color='red')\n",
    "        plt.xlabel('RKO Myc expression')\n",
    "        plt.ylabel(reaction)\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[1] + standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]-standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[1] - standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "        \n",
    "        \n",
    "    elif reaction in FC0_ANDs_constrain:\n",
    "        rxn_flux = list()\n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ANDs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ANDs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ANDs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ANDs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ANDs_constrain[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ANDs_constrain[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        print(reaction, ': ', 'flux; ', rxn_flux, ', ', 'standard deviation; ', standard_dev)\n",
    "        q = ['zero', 'intermediate', 'high']\n",
    "        e = np.array([standard_dev, standard_dev, standard_dev])\n",
    "        plt.errorbar(q, rxn_flux, e, color='red')\n",
    "        plt.xlabel('RKO Myc expression')\n",
    "        plt.ylabel(reaction)\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[1] + standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]-standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[1] - standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "    \n",
    "    \n",
    "    elif reaction in FC0_ANDORs_constrain_low:\n",
    "        rxn_flux = list()\n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ANDORs_constrain_low[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ANDORs_constrain_low[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ANDORs_constrain_low[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ANDORs_constrain_low[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ANDORs_constrain_low[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ANDORs_constrain_low[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        print(reaction, ': ', 'flux; ', rxn_flux, ', ', 'standard deviation; ', standard_dev)\n",
    "        q = ['zero', 'intermediate', 'high']\n",
    "        e = np.array([standard_dev, standard_dev, standard_dev])\n",
    "        plt.errorbar(q, rxn_flux, e, color='red')\n",
    "        plt.xlabel('RKO Myc expression')\n",
    "        plt.ylabel(reaction)\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[1] + standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]-standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[1] - standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "            \n",
    "        \n",
    "    elif reaction in FC0_ANDORs_constrain_high:\n",
    "        rxn_flux = list()\n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC0_ANDORs_constrain_high[reaction])\n",
    "        x(reaction).lower_bound = (FC0_ANDORs_constrain_high[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC100_ANDORs_constrain_high[reaction])\n",
    "        x(reaction).lower_bound = (FC100_ANDORs_constrain_high[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        x(reaction).lower_bound = 0\n",
    "        x(reaction).upper_bound = 0\n",
    "        x(reaction).upper_bound = (FC500_ANDORs_constrain_high[reaction])\n",
    "        x(reaction).lower_bound = (FC500_ANDORs_constrain_high[reaction])\n",
    "        model.objective = x(reaction)\n",
    "        solution = model.optimize()\n",
    "        rxn_flux.append(solution.fluxes[reaction])\n",
    "        \n",
    "        standard_dev = np.std(rxn_flux)\n",
    "        print(reaction, ': ', 'flux; ', rxn_flux, ', ', 'standard deviation; ', standard_dev)\n",
    "        q = ['zero', 'intermediate', 'high']\n",
    "        e = np.array([standard_dev, standard_dev, standard_dev])\n",
    "        plt.errorbar(q, rxn_flux, e, color='red')\n",
    "        plt.xlabel('RKO Myc expression')\n",
    "        plt.ylabel(reaction)  \n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[1] + standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "        \n",
    "        if (rxn_flux[2] - standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]-standard_dev) > (rxn_flux[0] + standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[1] - standard_dev):\n",
    "            print('sig diff between high and low Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and low Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[2]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between high and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between high and zero Myc expression rxn flux')\n",
    "            \n",
    "        if (rxn_flux[1]+standard_dev) < (rxn_flux[0] - standard_dev):\n",
    "            print('sig diff between low and zero Myc expression rxn flux')\n",
    "        else:\n",
    "            print('no sig diff between low and zero Myc expression rxn flux')\n",
    "            \n",
    "            \n",
    "    else:\n",
    "        print('reaction cannot be annotated :(')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "armed-divorce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HMR_4373',\n",
       " 'HMR_4365',\n",
       " 'HMR_4371',\n",
       " 'HMR_6328',\n",
       " 'HMR_4652',\n",
       " 'HMR_4568',\n",
       " 'HMR_4300',\n",
       " 'HMR_4368',\n",
       " 'HMR_4012',\n",
       " 'HMR_4083',\n",
       " 'HMR_7144',\n",
       " 'HMR_8444',\n",
       " 'HMR_8443',\n",
       " 'HMR_4456',\n",
       " 'HMR_4496',\n",
       " 'HMR_3829',\n",
       " 'HMR_5406',\n",
       " 'HMR_6725',\n",
       " 'HMR_6914',\n",
       " 'HMR_6916',\n",
       " 'HMR_4201',\n",
       " 'HMR_6507',\n",
       " 'HMR_4615',\n",
       " 'r1432',\n",
       " 'HMR_6622',\n",
       " 'HMR_0188',\n",
       " 'HMR_4635',\n",
       " 'HMR_7889',\n",
       " 'HMR_7887',\n",
       " 'HMR_4030',\n",
       " 'HMR_1020',\n",
       " 'HMR_3146',\n",
       " 'HMR_3144',\n",
       " 'HMR_4404',\n",
       " 'HMR_4501',\n",
       " 'HMR_6658',\n",
       " 'HMR_6657',\n",
       " 'HMR_6918',\n",
       " 'HMR_5145',\n",
       " 'HMR_6912',\n",
       " 'HMR_3825',\n",
       " 'HMR_0155',\n",
       " 'HMR_0173',\n",
       " 'HMR_0179',\n",
       " 'HMR_4695',\n",
       " 'HMR_4485',\n",
       " 'HMR_8075',\n",
       " 'HMR_6007',\n",
       " 'HMR_6650',\n",
       " 'HMR_6651',\n",
       " 'HMR_6628',\n",
       " 'HMR_6643',\n",
       " 'HMR_4710',\n",
       " 'HMR_5077',\n",
       " 'HMR_5079',\n",
       " 'HMR_5078',\n",
       " 'HMR_5076',\n",
       " 'HMR_5088',\n",
       " 'HMR_3931',\n",
       " 'r1433',\n",
       " 'HMR_5934',\n",
       " 'HMR_0598',\n",
       " 'HMR_7161']"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_reactions_upregulated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "olympic-ensemble",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Reaction identifier</strong></td><td>HMR_4373</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Name</strong></td><td>-1.0 1,3-bisphospho-D-glycerate + -1.0 H+ + -1.0 NADH <--> 1.0 GAP + 1.0 NAD+ + 1.0 Pi</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x013193ed90</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Stoichiometry</strong></td>\n",
       "                <td>\n",
       "                    <p style='text-align:right'>m00247c + m02039c + m02553c --> m01939c + m02552c + m02751c</p>\n",
       "                    <p style='text-align:right'>1,3-bisphospho-D-glycerate + H+ + NADH --> GAP + NAD+ + Pi</p>\n",
       "                </td>\n",
       "            </tr><tr>\n",
       "                <td><strong>GPR</strong></td><td>ENSG00000105679 or ENSG00000111640</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Lower bound</strong></td><td>73.74697107710173</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Upper bound</strong></td><td>73.74697107710173</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<Reaction HMR_4373 at 0x13193ed90>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x('HMR_4373')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "cloudy-champagne",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HMR_4373 :  flux;  [64.36479018397809, 72.16149591018542, 73.74697107710173] ,  standard deviation;  4.100510401797927\n",
      "no sig diff between high and low Myc expression rxn flux\n",
      "sig diff between high and zero Myc expression rxn flux\n",
      "no sig diff between low and zero Myc expression rxn flux\n",
      "no sig diff between high and low Myc expression rxn flux\n",
      "no sig diff between high and zero Myc expression rxn flux\n",
      "no sig diff between low and zero Myc expression rxn flux\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std_flux_plot('HMR_4373')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accessory-preparation",
   "metadata": {},
   "source": [
    "## Alternative methods for finding core reactions\n",
    "### e.g. simulate knockouts, set bounds to zero, remove reaction from model (then add back in in loop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "honest-latitude",
   "metadata": {},
   "source": [
    "# Running FVA on model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "interpreted-satellite",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Name</strong></td>\n",
       "                <td>HumanGEM</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x013bdd3400</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of metabolites</strong></td>\n",
       "                <td>8400</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of reactions</strong></td>\n",
       "                <td>13096</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of groups</strong></td>\n",
       "                <td>143</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Objective expression</strong></td>\n",
       "                <td>1.0*biomass_human - 1.0*biomass_human_reverse_fb2f2</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartments</strong></td>\n",
       "                <td>Cytosol, Extracellular, Lysosome, Endoplasmic reticulum, Mitochondria, Peroxisome, Golgi apparatus, Nucleus, Inner mitochondria</td>\n",
       "            </tr>\n",
       "          </table>"
      ],
      "text/plain": [
       "<Model HumanGEM at 0x13bdd3400>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = cobra.io.read_sbml_model('Human-GEM-annotated.xml')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "weekly-animal",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.objective = x('biomass_human')\n",
    "FVA = cobra.flux_analysis.flux_variability_analysis(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "entitled-phrase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FVA_reindexed.iloc[0,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "diagnostic-lightning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minimum</th>\n",
       "      <th>maximum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HMR_3905</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_3907</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4097</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4099</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_4108</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10128</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10129</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10130</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10131</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMR_10132</th>\n",
       "      <td>0.0</td>\n",
       "      <td>386.536029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13096 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           minimum      maximum\n",
       "HMR_3905       0.0  1000.000000\n",
       "HMR_3907       0.0  1000.000000\n",
       "HMR_4097       0.0  1000.000000\n",
       "HMR_4099       0.0  1000.000000\n",
       "HMR_4108       0.0  1000.000000\n",
       "...            ...          ...\n",
       "HMR_10128      0.0  1000.000000\n",
       "HMR_10129      0.0  1000.000000\n",
       "HMR_10130      0.0  1000.000000\n",
       "HMR_10131      0.0  1000.000000\n",
       "HMR_10132      0.0   386.536029\n",
       "\n",
       "[13096 rows x 2 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "correct-scholar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HMR_3905'"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reaction_ids = []\n",
    "\n",
    "for r in model.reactions:\n",
    "    reaction_ids.append(r.id)\n",
    "    \n",
    "reaction_ids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "completed-program",
   "metadata": {},
   "outputs": [],
   "source": [
    "FVA_reindexed = FVA.reindex(columns = FVA.columns.tolist() + ['Reaction ID'])\n",
    "\n",
    "for n in range(13096):\n",
    "    FVA_reindexed.iloc[n,2] = reaction_ids[n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "polar-drilling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0.0\n",
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     ]
    }
   ],
   "source": [
    "for value in FVA_reindexed['minimum']:\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "focal-description",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "core_reactions_FVA\n",
    "\n",
    "for n in range(13096):\n",
    "     if reaction in FVA_reindexed.iloc[:,0] != -1000:\n",
    "            if reaction in FVA_reindexed.iloc[:,0] != 0:\n",
    "                if reaction in FVA_reindexed.iloc[:,1] != 1000:\n",
    "                    core_reactions_FVA.append(FVA_reindexed.iloc[:,2])\n",
    "            \n",
    "print(len(core_reactions_FVA))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wrapped-rainbow",
   "metadata": {},
   "source": [
    "## Calculating core reactions using FVA. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alive-settlement",
   "metadata": {},
   "source": [
    "# different ideas: optimise for growth in the std_flux_plot instead of target reaction? \n",
    "## Scrap model.objective = x(reaction) line in code. and keep solution.fluxes. \n",
    "## Try andors high first, what is the difference in flux. \n",
    "## Remove reactions where we don't have any data? \n",
    "## Could try percentiles for proteomics, e.g. if data is between 10 - 20, set bounds 10 - 20. \n",
    "## Nothing else? Move onto FVA and flux sampling. "
   ]
  }
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