01_Constuct eciYW1475_TurNup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37c78f2a",
   "metadata": {},
   "source": [
    "# Import related functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e59daa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import cobra\n",
    "import pandas as pd\n",
    "import re\n",
    "sys.path.append(r'./script/')\n",
    "from ECMpy_function import *\n",
    "from AutoPACMEN_function import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088cf94c",
   "metadata": {},
   "source": [
    "# Input and output files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad910775",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "path exists\n"
     ]
    }
   ],
   "source": [
    "# Initial parameter\n",
    "obj='Biomass'\n",
    "fluxes_outfile = './analysis/ECMpy_solution_%s_pfba.csv'%obj \n",
    "use_substrate='R2399'# substrate ID\n",
    "concentration=10 #\n",
    "# The total protein fraction in cell.\n",
    "\n",
    "ptot = 0.4653\n",
    "# The approximated average saturation of enzyme.\n",
    "\n",
    "sigma = 0.5\n",
    "# Lowerbound  of enzyme concentration constraint. \n",
    "\n",
    "lowerbound = 0   \n",
    "enz_ratio=0.1\n",
    "\n",
    "# Input files\n",
    "autopacmen_folder = \"./iDL1450_get_data/\"\n",
    "gap_fill= 'mean'#'mean'#'median'\n",
    "project_name = \"iDL1450%s\"%gap_fill\n",
    "organism = \"Thermothelomyces thermophilus\"\n",
    "create_file(autopacmen_folder)\n",
    "reaction_gene_path = \"./data/reaction_gene_subunit_num.json\"\n",
    "\n",
    "protein_kcat_database_path = \"none\"\n",
    "bigg_metabolites_file = \"./data/bigg_models_metabolites.txt\"#date:20221008 http://bigg.ucsd.edu/static/namespace/bigg_models_metabolites.txt\n",
    "brenda_textfile_path = \"./data/brenda_download.txt\"#date:20221008 https://www.brenda-enzymes.org/brenda_download/file_download.php\n",
    "\n",
    "sbml_path=\"./model/iYW1475.xml\"\n",
    "\n",
    "c13reaction_file='./reaction_kcat/C13data.csv'#C13 data\n",
    "file_list=[gene_abundance_file1,gene_abundance_file2,gene_abundance_file3,gene_abundance_file4]\n",
    "EC_max_file='./data/EC_kcat_max.json'\n",
    "brenda_json_path = \"%skcat_database_brenda.json\"%autopacmen_folder\n",
    "brenda_output_json_path = \"%skcat_database_brenda_for_model.json\"%autopacmen_folder\n",
    "bigg_id_name_mapping_path = \"%sbigg_id_name_mapping.json\"%autopacmen_folder\n",
    "sabio_rk_output_path = \"%skcat_database_sabio_rk.json\"%autopacmen_folder\n",
    "combined_output_path = \"%skcat_database_combined.json\"%autopacmen_folder\n",
    "reaction_kcat_MW_file=\"%s/reaction_kcat_MW.csv\"%autopacmen_folder\n",
    "uniprot_data_file='%s/%s_protein_id_mass_mapping.json'%(autopacmen_folder,project_name)\n",
    "\n",
    "\n",
    "modelgene2uniprot_file='./analysis/modelgene2uniprot.txt'\n",
    "paxdbgene2uniprot_file='./analysis/paxdbgene2uniprot.txt'\n",
    "ecModel_output_file=\"./model/ecMTM_TurNup24_1.json\" \n",
    "\n",
    "json_output_file = './model/ecMTM_TurNup_adj_enz24_1.json'\n",
    "reaction_kcat_MW_outfile=\"./get_reaction_kcat/kcat/reaction_change_by_enzuse_TurNup24_1.csv\"\n",
    "reaction_kcat_MW_round2_outfile='./get_reaction_kcat/kcat/reaction_change_by_C13_TurNup24_1.csv'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b28562",
   "metadata": {},
   "source": [
    "### Calculate f-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14943d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5563635421646973, 0.5540772163224917, 0.558660486869679, 0.5486668910172237]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=cobra.io.read_sbml_model(sbml_path)\n",
    "# Proteome data\n",
    "gene_abundance_file1 = './data/danbaizu/wt1.csv'\n",
    "gene_abundance_file2 = './data/danbaizu/wt2.csv'\n",
    "gene_abundance_file3 = './data/danbaizu/wt3.csv'\n",
    "gene_abundance_file4 = './data/danbaizu/wt4.csv'\n",
    "file_list=[gene_abundance_file1,gene_abundance_file2,gene_abundance_file3,gene_abundance_file4]\n",
    "f_list=[]\n",
    "for g_a_file in file_list:\n",
    "    f=calculate_f_special_new(model,g_a_file)\n",
    "    f_list.append(f)\n",
    "f_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d76b8e1",
   "metadata": {},
   "source": [
    "# Constructing Enzyme-Constrained Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53de06f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upperbound is:0.128\n",
      "0.128\n",
      "IN FLUXES         OUT FLUXES     OBJECTIVES\n",
      "----------------  -------------  --------------\n",
      "glc__D_e  5.56    co2_e   10.5   Biomass  0.083\n",
      "h_e       5.14    etoh_e   9.78\n",
      "nh4_e     0.621   h2o_e    2.22\n",
      "o2_e      0.563\n",
      "pi_e      0.0151\n",
      "so4_e     0.0012\n",
      "Enzyme cost total is:0.128\n",
      "0.08297404584306473\n"
     ]
    }
   ],
   "source": [
    "#The enzyme mass fraction \n",
    "f = round(np.mean(f_list), 2)\n",
    "# print(\"f is:\" + str(f))\n",
    "ptot = 0.4653\n",
    "\n",
    "upperbound = round(ptot * f * sigma, 3)\n",
    "# upperbound = 0.1\n",
    "print(\"upperbound is:\" + str(upperbound))\n",
    "\n",
    "reaction_kcat_MW_file=\"./get_reaction_kcat/kcat/TurNup.csv\"# kcat data predicted by TurNup method\n",
    "\n",
    "trans_model2enz_json_model_split_isoenzyme(sbml_path, reaction_kcat_MW_file, f, ptot, sigma, lowerbound, upperbound, ecModel_output_file)\n",
    "\n",
    "enz_model=get_enzyme_constraint_model(ecModel_output_file)\n",
    "enz_model.objective=obj\n",
    "enz_model.summary()\n",
    "[ori_obj_id,ori_substrate_id_list,ori_sub_concentration,ori_ATPM]=get_model_substrate_obj(enz_model)\n",
    "for eachsubid in ori_substrate_id_list:\n",
    "    if re.search('_reverse',eachsubid):\n",
    "        r_id_new=eachsubid.split('_reverse')[0]\n",
    "        enz_model.reactions.get_by_id(eachsubid).bounds = (0, 0) \n",
    "        enz_model.reactions.get_by_id(r_id_new).bounds = (0, 0)  \n",
    "    else:\n",
    "        r_id_new=eachsubid+'_reverse'\n",
    "        enz_model.reactions.get_by_id(eachsubid).bounds = (0, 0) \n",
    "        enz_model.reactions.get_by_id(r_id_new).bounds = (0, 0) \n",
    "        \n",
    "enz_model.reactions.get_by_id(use_substrate).bounds = (-10, 0)\n",
    "enz_model.reactions.get_by_id(use_substrate+'_reverse').bounds = (0, 0)\n",
    "\n",
    "enz_model_pfba_solution = cobra.flux_analysis.pfba(enz_model)\n",
    "\n",
    "enz_model_pfba_solution = get_fluxes_detail_in_model(enz_model,enz_model_pfba_solution,fluxes_outfile,ecModel_output_file)\n",
    "print(enz_model_pfba_solution.fluxes[obj])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7548935",
   "metadata": {},
   "source": [
    "# Calibration kcat according Enzyme usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a09efa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Need changing reaction: \n",
      "R1036\n",
      "Changed reaction: \n",
      "['R1036']\n",
      "0.128\n",
      "Enzyme cost total is:0.12799999999999997\n",
      "Enzyme cost total is:0.12799999999999992\n",
      "ecGEM max glucose uptake rates:10.89155237955375\n",
      "ecGEM max growth rates:0.17917603317114514\n",
      "Round 1: 0.07664756140109978\n",
      "\n",
      "Need changing reaction: \n",
      "R2160\n",
      "Changed reaction: \n",
      "['R1036', 'R2160']\n",
      "0.128\n",
      "Enzyme cost total is:0.12799999999999992\n",
      "Enzyme cost total is:0.1280000000000001\n",
      "ecGEM max glucose uptake rates:10.749192538227003\n",
      "ecGEM max growth rates:0.18591312025932422\n",
      "Round 2: 0.1738360670787642\n",
      "\n",
      "Need changing reaction: \n",
      "R2158\n",
      "Changed reaction: \n",
      "['R1036', 'R2160']\n",
      "0.128\n",
      "Enzyme cost total is:0.1280000000000001\n",
      "Enzyme cost total is:0.1280000000000001\n",
      "ecGEM max glucose uptake rates:10.749192538227003\n",
      "ecGEM max growth rates:0.18591312025932422\n",
      "Round 3: 0.1738360670787642\n",
      "\n",
      "Need changing reaction: \n",
      "R2157\n",
      "Changed reaction: \n",
      "['R1036', 'R2160', 'R2157']\n",
      "0.128\n",
      "Enzyme cost total is:0.1280000000000001\n",
      "Enzyme cost total is:0.12800000000000006\n",
      "ecGEM max glucose uptake rates:6.81268975809663\n",
      "ecGEM max growth rates:0.19639027408044485\n",
      "Round 4: 0.18120121867473377\n",
      "\n",
      "Need changing reaction: \n",
      "R25\n",
      "Changed reaction: \n",
      "['R1036', 'R2160', 'R2157', 'R25']\n",
      "0.128\n",
      "Enzyme cost total is:0.12800000000000006\n",
      "Enzyme cost total is:0.12800000000000006\n",
      "ecGEM max glucose uptake rates:7.177559219550898\n",
      "ecGEM max growth rates:0.20819921589877563\n",
      "Round 5: 0.19060987213074665\n",
      "\n",
      "Need changing reaction: \n",
      "R105_reverse\n",
      "Changed reaction: \n",
      "['R1036', 'R2160', 'R2157', 'R25']\n",
      "0.128\n",
      "Enzyme cost total is:0.12800000000000009\n",
      "Enzyme cost total is:0.12800000000000006\n",
      "ecGEM max glucose uptake rates:7.177559219550898\n",
      "ecGEM max growth rates:0.20819921589877563\n",
      "Round 6: 0.19060987213074665\n",
      "\n",
      "Need changing reaction: \n",
      "R50\n",
      "Changed reaction: \n",
      "['R1036', 'R2160', 'R2157', 'R25', 'R50']\n",
      "0.128\n",
      "Enzyme cost total is:0.12800000000000009\n",
      "Enzyme cost total is:0.12800000000000003\n",
      "ecGEM max glucose uptake rates:12.814860337862642\n",
      "ecGEM max growth rates:0.22710216917748235\n",
      "Round 7: 0.1960607838422557\n",
      "\n",
      "Need changing reaction: \n",
      "R43_num2\n",
      "Changed reaction: \n",
      "['R1036', 'R2160', 'R2157', 'R25', 'R50', 'R43_num2']\n",
      "0.128\n",
      "Enzyme cost total is:0.12800000000000009\n",
      "Enzyme cost total is:0.128\n",
      "ecGEM max glucose uptake rates:14.727848213602446\n",
      "ecGEM max growth rates:0.26493306649003195\n",
      "Round 8: 0.20139393262925906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Initial model and result\n",
    "fluxes_infile_ori = './analysis/ECMpy_solution_%s_pfba.csv'%obj\n",
    "fluxes_outfile = fluxes_infile_ori\n",
    "need_change_reaction_list=[]\n",
    "changed_reaction_list=[]\n",
    "round_num=1\n",
    "\n",
    "\n",
    "reaction_kcat_mw = pd.read_csv(reaction_kcat_MW_file, index_col=0)\n",
    "\n",
    "#Model adjustment\n",
    "enz_model_obj_max=0\n",
    "concentration = 3.0676\n",
    "# Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila [J]. Biotechnology and Bioengineering, 2022, 119(7): 1926-37.\n",
    "# Beyond growth rate 0.25: Elucidation of the metabolic mechanism for malate production in Myceliophthora thermophila via 13C metabolic flux analysis [Z]. Research Square. 2022.10.21203/rs.3.rs-2123109/v1\n",
    "while (enz_model_obj_max<0.2573):\n",
    "\n",
    " \n",
    " \n",
    "    [enz_model,reaction_kcat_mw,need_change_reaction_list, changed_reaction_list]=change_enz_model_by_enz_usage(enz_ratio,\\\n",
    "                                                    ecModel_output_file,fluxes_outfile,EC_max_file,\\\n",
    "                                                    reaction_kcat_mw,need_change_reaction_list,changed_reaction_list,f, \\\n",
    "                                                    ptot, sigma, lowerbound, upperbound, json_output_file)\n",
    "   \n",
    "    #Using only the provided substrate\n",
    "  \n",
    "    [ori_obj_id,ori_substrate_id_list,ori_sub_concentration,ori_ATPM]=get_model_substrate_obj(enz_model)\n",
    "    for eachsubid in ori_substrate_id_list:\n",
    "        if re.search('_reverse',eachsubid):\n",
    "            r_id_new=eachsubid.split('_reverse')[0]\n",
    "            enz_model.reactions.get_by_id(eachsubid).bounds = (0, 0) \n",
    "            enz_model.reactions.get_by_id(r_id_new).bounds = (0, 0)  \n",
    "        else:\n",
    "            r_id_new=eachsubid+'_reverse'\n",
    "            enz_model.reactions.get_by_id(eachsubid).bounds = (0, 0) \n",
    "            enz_model.reactions.get_by_id(r_id_new).bounds = (0, 0) \n",
    "    enz_model.reactions.get_by_id(use_substrate).bounds = (-concentration, 0)\n",
    "    enz_model.reactions.get_by_id(use_substrate+'_reverse').bounds = (0, 0)\n",
    "  \n",
    "  \n",
    "    \n",
    "    enz_model_obj=enz_model.slim_optimize()\n",
    "\n",
    "    enz_model_pfba_solution = cobra.flux_analysis.pfba(enz_model)\n",
    "    fluxes_outfile = './analysis/ECMpy_solution_adjround%s.csv'%(round_num)\n",
    "    enz_model_pfba_solution = get_fluxes_detail_in_model(enz_model,enz_model_pfba_solution,fluxes_outfile,json_output_file)\n",
    "    \n",
    "    \n",
    "\n",
    "   # μmax condition substrate concentration\n",
    "  \n",
    "    obj='Biomass'\n",
    "    substrate='R2399'\n",
    "    enz_model.reactions.get_by_id(substrate).bounds=(-1000,0)\n",
    "    enz_model.reactions.get_by_id('R2399_reverse').bounds=(0,0)\n",
    "    \n",
    "    enz_model.objective=obj\n",
    "    enz_model_solution = cobra.flux_analysis.pfba(enz_model)\n",
    "    print(\"ecGEM max glucose uptake rates:\"+str(-enz_model_solution.fluxes[substrate]))\n",
    "    print(\"ecGEM max growth rates:\"+str(enz_model_solution.fluxes[obj]))\n",
    "    enz_model_sub=-enz_model_solution.fluxes[substrate]\n",
    "    enz_model_obj_max=enz_model_solution.fluxes[obj]\n",
    "\n",
    "\n",
    "    print('Round %s: '%round_num+str(enz_model_obj))\n",
    "    round_num=round_num+1\n",
    "    if round_num>10:\n",
    "        break\n",
    "    print()\n",
    "reaction_kcat_mw.to_csv(reaction_kcat_MW_outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec94fa37",
   "metadata": {},
   "source": [
    "# Calibration kcat according 13C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a166ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.128\n",
      "通量为： -1.60059142458337e-11\n",
      "R6_num1 ECMpy_c13_reaction_flux: 0.009956481984000001 flux: 3.0676\n",
      "0.128\n",
      "通量为： -1.60059142458337e-11\n",
      "R7 ECMpy_c13_reaction_flux: 0.113010559296 flux: 1.4289\n",
      "0.128\n",
      "通量为： 5.319665982439708\n",
      "R1036 ECMpy_c13_reaction_flux: 0.06682355402351355 flux: 1.936\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R1044_num1 ECMpy_c13_reaction_flux: 0.0025007938508800004 flux: 1.936\n",
      "0.128\n",
      "通量为： 10.66067912683804\n",
      "R50 ECMpy_c13_reaction_flux: 0.158462757914232 flux: 4.1708\n",
      "0.128\n",
      "通量为： 10.66067912683804\n",
      "R51 ECMpy_c13_reaction_flux: 0.09369485888000001 flux: 4.1708\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R42_num2 ECMpy_c13_reaction_flux: 0.016192371078400002 flux: 3.9965\n",
      "0.128\n",
      "通量为： 10.559987752468738\n",
      "R40 ECMpy_c13_reaction_flux: 0.011152988908800003 flux: 3.9965\n",
      "0.128\n",
      "通量为： 10.402400111536174\n",
      "R36_num2 ECMpy_c13_reaction_flux: 0.039763349945600004 flux: 3.8319\n",
      "0.128\n",
      "通量为： 0.13123741782926515\n",
      "R55 ECMpy_c13_reaction_flux: 0.059693464153599995 flux: 1.2271\n",
      "0.128\n",
      "通量为： 0.13123741782926515\n",
      "R60 ECMpy_c13_reaction_flux: 0.012909565510400001 flux: 1.2271\n",
      "0.128\n",
      "通量为： 0.13123741782926515\n",
      "R56_num3 ECMpy_c13_reaction_flux: 0.015368825817600002 flux: 1.2271\n",
      "0.128\n",
      "通量为： 0.06354501461251\n",
      "R54 ECMpy_c13_reaction_flux: 0.010249147852800001 flux: 0.7284\n",
      "0.128\n",
      "通量为： 0.06769240321675515\n",
      "R58_reverse ECMpy_c13_reaction_flux: 0.101578533568 flux: 0.4987\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R46 ECMpy_c13_reaction_flux: 7.7383910528e-05 flux: 2.3042\n",
      "0.128\n",
      "通量为： 0.14200178205581585\n",
      "R114_num2 ECMpy_c13_reaction_flux: 0.0461175248128 flux: 2.0911\n",
      "0.128\n",
      "通量为： 0.14200178205581585\n",
      "R1033_num1 ECMpy_c13_reaction_flux: 0.00115078947264 flux: 1.3615\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R117_num1 ECMpy_c13_reaction_flux: 0.005239800005120001 flux: 1.3615\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R118 ECMpy_c13_reaction_flux: 0.00015968106060800001 flux: 0.8886\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R111 ECMpy_c13_reaction_flux: 0.0013208855705600002 flux: 0.8886\n",
      "0.128\n",
      "通量为： 0.03643227723838602\n",
      "R104 ECMpy_c13_reaction_flux: 0.252000002176 flux: 0.4597\n",
      "0.128\n",
      "通量为： 0.0\n",
      "R108 ECMpy_c13_reaction_flux: 0.00118576501888 flux: 1.415\n",
      "0.128\n",
      "通量为： 0.32034619619089266\n",
      "R2308 ECMpy_c13_reaction_flux: 0.0111143931328 flux: 2.1536\n",
      "need changing reaction: \n",
      "['R6_num1', 'R7', 'R1044_num1', 'R42_num2', 'R55', 'R60', 'R56_num3', 'R54', 'R58_reverse', 'R46', 'R114_num2', 'R1033_num1', 'R117_num1', 'R118', 'R111', 'R104', 'R108', 'R2308']\n",
      "changed reaction: \n",
      "['R6_num1', 'R7', 'R1044_num1', 'R42_num2', 'R55', 'R56_num3', 'R54', 'R46', 'R114_num2', 'R1033_num1', 'R117_num1', 'R118', 'R111', 'R104', 'R108']\n",
      "0.128\n",
      "IN FLUXES          OUT FLUXES    OBJECTIVES\n",
      "-----------------  ------------  --------------\n",
      "h_e       12.2     h2o_e  12.6   Biomass  0.262\n",
      "o2_e       7.37    co2_e   7.78\n",
      "glc__D_e   3.06\n",
      "nh4_e      1.96\n",
      "pi_e       0.0475\n",
      "so4_e      0.0038\n"
     ]
    }
   ],
   "source": [
    "\n",
    "c13_percentage=0.1\n",
    "# kcat_database_combined_file='./data/EC_kcat_max.json'##max kcat for EC number selected from BRENDA and SABIO-RK database(use autoPACMEN)\n",
    "model_name=sbml_path#初始的xml文件\n",
    "f=0.55 # Enzyme mass fraction\n",
    "ptot=0.4653 # total protein content in cells\n",
    "sigma=0.5 #approximate average saturation of the enzyme\n",
    "lowerbound= 0 \n",
    "upperbound=round(ptot * f * sigma, 3)\n",
    "\n",
    "\n",
    "json_round2_output_file='./model/ecMTM.json'#Adjusting kcat based on C13 and outputting the adjusted model\n",
    "\n",
    "\n",
    "enz_model=get_enz_model_use_c13(reaction_kcat_MW_outfile, ecModel_output_file, c13reaction_file, c13_percentage, \\\n",
    "                                EC_max_file,model_name, f, ptot, sigma, lowerbound, \\\n",
    "                                upperbound, json_round2_output_file,reaction_kcat_MW_round2_outfile)\n",
    "enz_model.reactions.get_by_id('R2399_reverse').bounds=(0,3.06)\n",
    "enz_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0fd7c67",
   "metadata": {},
   "source": [
    "# Obtain the metabolic flux of this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d257a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.128\n"
     ]
    }
   ],
   "source": [
    "ecModel_output_file='./model/ecMTM.json'\n",
    "enz_model=get_enzyme_constraint_model(ecModel_output_file)\n",
    "enz_model.reactions.get_by_id('R2399_reverse').bounds=(0,3.06)\n",
    "enz_model.reactions.get_by_id(\"R1975_num1\").bounds=(2.8,2.8)\n",
    "enz_model.reactions.get_by_id(\"R1975_num2\").bounds=(0,0)\n",
    "enz_model.reactions.get_by_id(\"R1975_num3\").bounds=(0,0)\n",
    "enz_model.reactions.get_by_id(\"R1975_num4\").bounds=(0,0)\n",
    "enz_model.reactions.get_by_id(\"R1975_num5\").bounds=(0,0)\n",
    "solution = enz_model.optimize()\n",
    "reac=dict(solution.fluxes[solution.fluxes> 0.1])\n",
    "genelist=[]\n",
    "reactionlist=[]\n",
    "fluxlist=[]\n",
    "for i in reac:\n",
    "    reID=str(enz_model.reactions.get_by_id(i))\n",
    "    reID=str(enz_model.reactions.get_by_id(i)).split(':')[0]\n",
    "    reID= reID.split('_')[0]\n",
    "    reactionlist.append(reID)\n",
    "    fluxlist.append(reac[i])\n",
    "df=pd.DataFrame({'reaction':reactionlist,'flux':fluxlist})\n",
    "\n",
    "df.to_csv(\"./flux/flux for TN.csv\",index=0)\n"
   ]
  }
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