{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Tutorial: Create iML1515 TGECKO model\n",
    "\n",
    "The f2xba modeling framework has been developed for the purpose of generating a variety of extended metabolic models using a consistent framework. It facilitates the sharing of configuration data and provides consistent support for model optimization and results analysis. In this context, the generation of a TGECKO model, a thermodynamics-constrained GECKO model, can be achieved with minimal effort by reusing the XBA, ECM, and TFA configuration files and workflows established in the previous tutorials.\n",
    "\n",
    "Peter Schubert, Heinrich-Heine University Duesseldorf, Institute for Computational Cell Biology (Prof. Dr. M. Lercher), January, 2025"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Initial setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 minimal media conditions created for simulation\n",
      "2347 records of proteomics loaded from data/Ecoli_Schmidt_proteomics.xlsx\n",
      "2232 records with confidence level above 43.0\n"
     ]
    }
   ],
   "source": [
    "# Required imports and model names\n",
    "import os\n",
    "import re\n",
    "import time\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cobra\n",
    "\n",
    "from f2xba import XbaModel, TfaModel, EcModel\n",
    "from f2xba import EcmOptimization, EcmResults\n",
    "from f2xba.utils.mapping_utils import load_parameter_file, write_parameter_file\n",
    "\n",
    "fba_model = 'iML1515'\n",
    "baseline_model = 'iML1515_predicted_fit_GECKO'\n",
    "target_model = 'iML1515_TGECKO'\n",
    "reference_cond = 'Glucose'\n",
    "\n",
    "# Create media conditions\n",
    "media_grs = {'Acetate': ['ac', 0.29], 'Glycerol': ['glyc', 0.47], 'Fructose': ['fru', 0.54], \n",
    "             'L-Malate': ['mal__L', 0.55], 'Glucose': ['glc__D', 0.66], 'Glucose 6-Phosphate': ['g6p', 0.78]}\n",
    "base_medium = ['ca2', 'cbl1', 'cl', 'co2', 'cobalt2', 'cu2', 'fe2', 'fe3', 'h2o', 'h', 'k', 'mg2', \n",
    "               'mn2', 'mobd', 'na1', 'nh4', 'ni2', 'o2', 'pi', 'sel', 'slnt', 'so4', 'tungs', 'zn2']\n",
    "conditions = {}\n",
    "exp_grs = {}\n",
    "for cond, (carbon_sid, exp_gr )in media_grs.items():\n",
    "    conditions[cond] = {f'EX_{sidx}_e': 1000.0 for sidx in base_medium}\n",
    "    conditions[cond][f'EX_{carbon_sid}_e'] = 1000.0\n",
    "    exp_grs[cond] = exp_gr\n",
    "print(f'{len(conditions)} minimal media conditions created for simulation')\n",
    "\n",
    "# Load proteomics\n",
    "fname = os.path.join('data', 'Ecoli_Schmidt_proteomics.xlsx')\n",
    "with pd.ExcelFile(fname) as xlsx:\n",
    "    df_mpmf = pd.read_excel(xlsx, sheet_name='proteomics', index_col=0)\n",
    "    print(f'{len(df_mpmf)} records of proteomics loaded from {fname}')\n",
    "min_confidence_level = 43.0\n",
    "df_mpmf = df_mpmf[df_mpmf['confidence'] > min_confidence_level]\n",
    "print(f'{len(df_mpmf)} records with confidence level above {min_confidence_level}')\n",
    "\n",
    "# Metabolite concentrations to be fixed with a margin (Buckstein, 2008)\n",
    "nt_concs_µmol_per_l = {\n",
    "    'atp_c': 3560, 'adp_c': 116, 'datp_c': 181, 'ctp_c': 325, 'dctp_c': 184,\n",
    "    'gtp_c': 1660, 'gdp_c': 203, 'dgtp_c': 92, 'utp_c': 667, 'udp_c': 54,\n",
    "    'dttp_c': 256, 'ppgpp_c': 113, 'accoa_c': 1390}\n",
    "factor = 2.0\n",
    "metabolite_molar_concs = {sid: (µmol_per_l * 1e-6/factor, µmol_per_l*1e-6*factor) \n",
    "                          for sid, µmol_per_l in  nt_concs_µmol_per_l.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create TGECKO model\n",
    "\n",
    "The generation of a TGECKO model is initiated with an XbaModel that has been configured with the XBA configuration file, which was previously utilized for the GECKO model. Subsequently, thermodynamics constraints are introduced using the TfaModel. Finally, the ECM configuration data is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading: SBML_models/iML1515.xml (last modified: Thu Dec  5 10:03:46 2024)\n",
      "3 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx (Thu Feb 12 15:26:06 2026)\n",
      "  22 gene product(s) removed from reactions (1494 gene products remaining)\n",
      "   2 attributes on reaction instances updated\n",
      "3 reactions with atom imbalances, e.g. [R_PUACGAMS, R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.atom_imbalances.\n",
      "2 reactions with charge imbalances, e.g. [R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.charge_imbalances.\n",
      "extracting UniProt protein data from data/uniprot_organism_83333.tsv\n",
      "1494 proteins created\n",
      "1203 enzymes added with default stoichiometry\n",
      "1 table(s) with parameters loaded from data/iML1515_enzyme_composition_updated.xlsx (Thu Feb 12 15:17:11 2026)\n",
      "1203 enzyme compositions updated from data/iML1515_enzyme_composition_updated.xlsx\n",
      "2221 reactions catalyzed by 1203 enzymes\n",
      "default kcat values configured for ['metabolic', 'transporter'] reactions\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_kcats.xlsx (Thu Feb 12 15:26:06 2026)\n",
      "5357 kcat values updated from data/iML1515_predicted_fit_GECKO_kcats.xlsx\n",
      "   0 enzymes removed due to missing kcat values\n",
      "1877 constraints (+0); 2712 variables (+0); 1494 genes (-22); 5 parameters (+0)\n",
      ">>> BASELINE XBA model configured!\n",
      "\n",
      "4 table(s) with parameters loaded from data/iML1515_TFA_tfa_parameters.xlsx (Thu Feb 12 16:42:45 2026)\n",
      "  17 thermo data attributes updated\n",
      " 937 metabolites with TD data covering 1545 model species; (332 not supported)\n",
      "1897 reactions supported by TD data; (478 not supported)\n",
      "7410 constraints to add\n",
      "7410 constraint ids added to the model (9287 total constraints)\n",
      "3794 ∆rG'/∆rG'˚ variables to add\n",
      "3794 variable ids added to the model (6506 total variables)\n",
      "2470 forward/reverse use variables to add\n",
      "2470 variable ids added to the model (8976 total variables)\n",
      "1528 log concentration variables to add\n",
      "1528 variable ids added to the model (10504 total variables)\n",
      "1897 TD reactions split in forward/reverse, 0 opened reverse direction\n",
      "2470 fwd/rev reactions to couple with flux direction\n",
      "2470 attributes on reaction instances updated\n",
      "   2 RHS variables to add\n",
      "   2 variable ids added to the model (11079 total variables)\n",
      "3811 parameters\n",
      "   2 ∆Gr'˚ variables need relaxation.\n",
      "   2 attributes on parameter instances updated\n",
      "1528 species (930 metabolites) with TD data from total 1877 (1169)\n",
      "1897 metabolic/transporter reactions with TD data from total 2375\n",
      "9287 constraints (+7410); 11079 variables (+8367); 1494 genes (-22); 3811 parameters (+3806)\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx (Thu Feb 12 15:26:06 2026)\n",
      "modelled protein fraction of total protein mass 0.3733 g/g\n",
      "1494 protein constraints to add\n",
      "1494 constraint ids added to the model (10781 total constraints)\n",
      "protein constraint: 212.80 mg/gDW - modelled protein\n",
      "   1 constraint ids added to the model (10782 total constraints)\n",
      "1495 protein variables to add\n",
      "1495 variable ids added to the model (15160 total variables)\n",
      "10782 constraints (+8905); 15160 variables (+12448); 1494 genes (-22); 3817 parameters (+3812)\n",
      "model exported to SBML format: SBML_models/iML1515_TGECKO.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new TGECKO model\n",
    "xba_model = XbaModel(os.path.join('SBML_models', f'{fba_model}.xml'))\n",
    "xba_model.configure(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "\n",
    "tfa_model = TfaModel(xba_model)\n",
    "tfa_model.configure(os.path.join('data', f'{fba_model}_TFA_tfa_parameters.xlsx'))\n",
    "\n",
    "ec_model = EcModel(xba_model)\n",
    "ec_model.configure(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'))\n",
    "ec_model.export(os.path.join('SBML_models', f'{target_model}.xml'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## Step 3. Load and optimize TGECKO model (COBRApy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Set parameter LicenseID to value 2731209\n",
      "Academic license - for non-commercial use only - expires 2026-10-31\n",
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_TGECKO.xml (Thu Feb 12 16:44:58 2026)\n",
      "Thermodynamic use variables (V_FU_xxx and V_RU_xxx) as binary\n",
      "Thermodynamic constraints (C_F[FR]C_xxx, C_G[FR]C_xxx, C_SU_xxx) ≤ 0\n",
      "1897 TD reaction constraints\n",
      "total modeled protein: 212.80 mg/gDW, enzyme saturation level: 0.5\n",
      "13 metabolite concentrations constrained\n",
      "Duration: 31.6 s\n"
     ]
    }
   ],
   "source": [
    "# Load TGECKO model using cobrapy\n",
    "start = time.time()\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "ecm = cobra.io.read_sbml_model(fname)\n",
    "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n",
    "\n",
    "eo = EcmOptimization(fname, ecm)\n",
    "sigma = eo.avg_enz_saturation\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')\n",
    "\n",
    "# Set nucleotide concentrations (optional)\n",
    "orig_concs = eo.set_tfa_metab_concentrations(metabolite_molar_concs)\n",
    "print(f'{len(orig_concs)} metabolite concentrations constrained')\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.341 h-1 vs. exp 0.290, diff:  0.051\n",
      "Glycerol                 : pred gr: 0.540 h-1 vs. exp 0.470, diff:  0.070\n",
      "Fructose                 : pred gr: 0.544 h-1 vs. exp 0.540, diff:  0.004\n",
      "L-Malate                 : pred gr: 0.526 h-1 vs. exp 0.550, diff: -0.024\n",
      "Glucose                  : pred gr: 0.613 h-1 vs. exp 0.660, diff: -0.047\n",
      "Glucose 6-Phosphate      : pred gr: 0.608 h-1 vs. exp 0.780, diff: -0.172\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein mass fractions:\n",
      "Acetate                  : r² = 0.2511, p = 1.25e-64 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.2292, p = 2.24e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.70e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.5130, p = 4.74e-50 ( 310 proteins log scale)\n",
      "Glycerol                 : r² = 0.5042, p = 1.89e-49 ( 314 proteins log scale)\n",
      "Fructose                 : r² = 0.5317, p = 7.62e-52 ( 305 proteins log scale)\n",
      "Glucose                  : r² = 0.5621, p = 1.98e-58 ( 317 proteins log scale)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x247.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 file(s) exported for \"Load reaction data\" into Escher maps\n",
      "1 file(s) exported for \"Load metabolite data\" into Escher maps\n",
      "Duration: 22.0 s\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using cobrapy and analyze results\n",
    "start = time.time()\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    with ecm as model:\n",
    "\n",
    "        model.medium = medium \n",
    "        solution = model.optimize()\n",
    "        if solution.status == 'optimal':\n",
    "            gr = solution.objective_value\n",
    "            pred_results[cond] = solution\n",
    "            print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, '\n",
    "                  f'diff: {gr - exp_grs[cond]:6.3f}')\n",
    "        else:    \n",
    "            print(f'{cond} ended with status {solution.status}')\n",
    "            \n",
    "er = EcmResults(eo, pred_results, df_mpmf)\n",
    "df_fluxes = er.collect_fluxes()\n",
    "df_net_fluxes = er.collect_fluxes(net=True)\n",
    "df_proteins = er.collect_protein_results()\n",
    "er.plot_grs(exp_grs, highlight=reference_cond)\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.plot_proteins(reference_cond)  \n",
    "df_species_conc = er.collect_species_conc()\n",
    "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', target_model))\n",
    "er.save_to_escher(df_species_conc[reference_cond], os.path.join('escher', target_model))\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>name</th>\n",
       "      <th>rank</th>\n",
       "      <th>mean mmol_per_l</th>\n",
       "      <th>stdev</th>\n",
       "      <th>Acetate</th>\n",
       "      <th>Glycerol</th>\n",
       "      <th>Fructose</th>\n",
       "      <th>L-Malate</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Glucose 6-Phosphate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>atp_c</th>\n",
       "      <td>ATP C10H12N5O13P3</td>\n",
       "      <td>1245</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>2.757564</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>7.120</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>7.120</td>\n",
       "      <td>1.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adp_c</th>\n",
       "      <td>ADP C10H12N5O10P2</td>\n",
       "      <td>1292</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>0.095304</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datp_c</th>\n",
       "      <td>DATP C10H12N5O12P3</td>\n",
       "      <td>1283</td>\n",
       "      <td>0.316750</td>\n",
       "      <td>0.110839</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.362000</td>\n",
       "      <td>0.362000</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctp_c</th>\n",
       "      <td>CTP C9H12N3O14P3</td>\n",
       "      <td>1274</td>\n",
       "      <td>0.450821</td>\n",
       "      <td>0.218190</td>\n",
       "      <td>0.251642</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.251642</td>\n",
       "      <td>0.251642</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dctp_c</th>\n",
       "      <td>DCTP C9H12N3O13P3</td>\n",
       "      <td>1296</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  rank  mean mmol_per_l     stdev   Acetate  \\\n",
       "sid                                                                     \n",
       "atp_c    ATP C10H12N5O13P3  1245         3.560000  2.757564  1.780000   \n",
       "adp_c    ADP C10H12N5O10P2  1292         0.145000  0.095304  0.232000   \n",
       "datp_c  DATP C10H12N5O12P3  1283         0.316750  0.110839  0.090500   \n",
       "ctp_c     CTP C9H12N3O14P3  1274         0.450821  0.218190  0.251642   \n",
       "dctp_c   DCTP C9H12N3O13P3  1296         0.092000  0.000000  0.092000   \n",
       "\n",
       "        Glycerol  Fructose  L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                                                 \n",
       "atp_c      7.120  1.780000  1.780000    7.120                1.780  \n",
       "adp_c      0.058  0.232000  0.232000    0.058                0.058  \n",
       "datp_c     0.362  0.362000  0.362000    0.362                0.362  \n",
       "ctp_c      0.650  0.251642  0.251642    0.650                0.650  \n",
       "dctp_c     0.092  0.092000  0.092000    0.092                0.092  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selected species concentrations\n",
    "df_species_conc.loc[list(metabolite_molar_concs)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Track progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Feb 12 15:31:07 2026)\n",
      "1 table(s) with parameters written to protein_predictions.xlsx\n"
     ]
    },
    {
     "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>model</th>\n",
       "      <th>lin r2</th>\n",
       "      <th>log r2</th>\n",
       "      <th>lin proteins</th>\n",
       "      <th>log proteins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iML1515_default_GECKO</td>\n",
       "      <td>0.033244</td>\n",
       "      <td>0.190225</td>\n",
       "      <td>1018</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iML1515_modified_GECKO</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.201613</td>\n",
       "      <td>999</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iML1515_predicted_GECKO</td>\n",
       "      <td>0.078803</td>\n",
       "      <td>0.268769</td>\n",
       "      <td>999</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iML1515_manual_adjust_GECKO</td>\n",
       "      <td>0.205413</td>\n",
       "      <td>0.333470</td>\n",
       "      <td>999</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>iML1515_predicted_fit_GECKO</td>\n",
       "      <td>0.869850</td>\n",
       "      <td>0.549757</td>\n",
       "      <td>999</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>iML1515_RBA</td>\n",
       "      <td>0.855003</td>\n",
       "      <td>0.479941</td>\n",
       "      <td>1112</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>iML1515_TGECKO</td>\n",
       "      <td>0.869850</td>\n",
       "      <td>0.562074</td>\n",
       "      <td>999</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          model    lin r2    log r2  lin proteins  \\\n",
       "No                                                                  \n",
       "1         iML1515_default_GECKO  0.033244  0.190225          1018   \n",
       "2        iML1515_modified_GECKO  0.135981  0.201613           999   \n",
       "3       iML1515_predicted_GECKO  0.078803  0.268769           999   \n",
       "4   iML1515_manual_adjust_GECKO  0.205413  0.333470           999   \n",
       "5   iML1515_predicted_fit_GECKO  0.869850  0.549757           999   \n",
       "6                   iML1515_RBA  0.855003  0.479941          1112   \n",
       "8                iML1515_TGECKO  0.869850  0.562074           999   \n",
       "\n",
       "    log proteins  \n",
       "No                \n",
       "1            299  \n",
       "2            322  \n",
       "3            308  \n",
       "4            308  \n",
       "5            313  \n",
       "6            428  \n",
       "8            317  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "number = 8\n",
    "xy = np.array([[df_mpmf.at[gene, reference_cond], df_proteins.at[gene, reference_cond]] \n",
    "                for gene in df_proteins.index if gene in df_mpmf.index])\n",
    "log10_x, log10_y = er.get_log10_xy(xy)\n",
    "lin_pearson_r, _ = scipy.stats.pearsonr(xy[:, 0], xy[:, 1])\n",
    "log_pearson_r, _ = scipy.stats.pearsonr(log10_x, log10_y)\n",
    "\n",
    "predictions = load_parameter_file('protein_predictions.xlsx')\n",
    "data = [[number, target_model, lin_pearson_r**2,log_pearson_r**2, len(xy), len(log10_x)]]\n",
    "cols = ['No', 'model', 'lin r2', 'log r2', 'lin proteins', 'log proteins']\n",
    "df = pd.DataFrame(data, columns=cols).set_index('No')\n",
    "if number in predictions[reference_cond].index:\n",
    "    predictions[reference_cond].drop(index=number, inplace=True)\n",
    "predictions[reference_cond\n",
    "] = pd.concat((predictions[reference_cond], df)).sort_index()\n",
    "write_parameter_file('protein_predictions.xlsx', predictions)\n",
    "predictions[reference_cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## (Alternative) gurobipy - model optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_TGECKO.xml (Thu Feb 12 16:44:58 2026)\n",
      "MILP Model of iML1515_TFA_GECKO\n",
      "15160 variables, 10782 constraints, 54697 non-zero matrix coefficients\n",
      "1897 TD reaction constraints\n",
      "13 metabolite concentrations constrained\n",
      "Duration: 17.4 s\n"
     ]
    }
   ],
   "source": [
    "# Load TGECKO model using gurobipy\n",
    "start = time.time()\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "eo = EcmOptimization(fname)\n",
    "\n",
    "# Set nucleotide concentrations (optional)\n",
    "orig_concs = eo.set_tfa_metab_concentrations(metabolite_molar_concs)\n",
    "print(f'{len(orig_concs)} metabolite concentrations constrained')\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.341 h-1 vs. exp 0.290, diff:  0.051\n",
      "Glycerol                 : pred gr: 0.540 h-1 vs. exp 0.470, diff:  0.070\n",
      "Fructose                 : pred gr: 0.544 h-1 vs. exp 0.540, diff:  0.004\n",
      "L-Malate                 : pred gr: 0.526 h-1 vs. exp 0.550, diff: -0.024\n",
      "Glucose                  : pred gr: 0.613 h-1 vs. exp 0.660, diff: -0.047\n",
      "Glucose 6-Phosphate      : pred gr: 0.608 h-1 vs. exp 0.780, diff: -0.172\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein mass fractions:\n",
      "Acetate                  : r² = 0.2511, p = 1.25e-64 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.2292, p = 2.25e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.70e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.4372, p = 3.25e-41 ( 317 proteins log scale)\n",
      "Glycerol                 : r² = 0.4935, p = 3.77e-48 ( 315 proteins log scale)\n",
      "Fructose                 : r² = 0.5394, p = 4.14e-53 ( 306 proteins log scale)\n",
      "Glucose                  : r² = 0.5621, p = 1.98e-58 ( 317 proteins log scale)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x247.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 file(s) exported for \"Load reaction data\" into Escher maps\n",
      "1 file(s) exported for \"Load metabolite data\" into Escher maps\n",
      "Duration: 6.8 s\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using gurobipy and analyze results\n",
    "start = time.time()\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    eo.medium = medium\n",
    "    solution = eo.optimize()\n",
    "\n",
    "    if solution.status == 'optimal':\n",
    "        pred_results[cond] = solution\n",
    "        gr = solution.objective_value\n",
    "        print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, diff: {gr - exp_grs[cond]:6.3f}')\n",
    "    else:    \n",
    "        print(f'{cond} ended with status {solution.status}')\n",
    "\n",
    "er = EcmResults(eo, pred_results, df_mpmf)\n",
    "df_fluxes = er.collect_fluxes()\n",
    "df_net_fluxes = er.collect_fluxes(net=True)\n",
    "df_proteins = er.collect_protein_results()\n",
    "er.plot_grs(exp_grs, highlight=reference_cond)\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.plot_proteins(reference_cond)  \n",
    "df_species_conc = er.collect_species_conc()\n",
    "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', target_model))\n",
    "er.save_to_escher(df_species_conc[reference_cond], os.path.join('escher', target_model))\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>name</th>\n",
       "      <th>rank</th>\n",
       "      <th>mean mmol_per_l</th>\n",
       "      <th>stdev</th>\n",
       "      <th>Acetate</th>\n",
       "      <th>Glycerol</th>\n",
       "      <th>Fructose</th>\n",
       "      <th>L-Malate</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Glucose 6-Phosphate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>atp_c</th>\n",
       "      <td>ATP C10H12N5O13P3</td>\n",
       "      <td>436</td>\n",
       "      <td>1.78000</td>\n",
       "      <td>2.432377e-16</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adp_c</th>\n",
       "      <td>ADP C10H12N5O10P2</td>\n",
       "      <td>482</td>\n",
       "      <td>0.23200</td>\n",
       "      <td>3.040471e-17</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.2320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datp_c</th>\n",
       "      <td>DATP C10H12N5O12P3</td>\n",
       "      <td>471</td>\n",
       "      <td>0.31675</td>\n",
       "      <td>1.108394e-01</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.3620</td>\n",
       "      <td>0.3620</td>\n",
       "      <td>0.3620</td>\n",
       "      <td>0.3620</td>\n",
       "      <td>0.3620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctp_c</th>\n",
       "      <td>CTP C9H12N3O14P3</td>\n",
       "      <td>490</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dctp_c</th>\n",
       "      <td>DCTP C9H12N3O13P3</td>\n",
       "      <td>508</td>\n",
       "      <td>0.09200</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  rank  mean mmol_per_l         stdev  Acetate  \\\n",
       "sid                                                                        \n",
       "atp_c    ATP C10H12N5O13P3   436          1.78000  2.432377e-16   1.7800   \n",
       "adp_c    ADP C10H12N5O10P2   482          0.23200  3.040471e-17   0.2320   \n",
       "datp_c  DATP C10H12N5O12P3   471          0.31675  1.108394e-01   0.0905   \n",
       "ctp_c     CTP C9H12N3O14P3   490          0.16250  0.000000e+00   0.1625   \n",
       "dctp_c   DCTP C9H12N3O13P3   508          0.09200  0.000000e+00   0.0920   \n",
       "\n",
       "        Glycerol  Fructose  L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                                                 \n",
       "atp_c     1.7800    1.7800    1.7800   1.7800               1.7800  \n",
       "adp_c     0.2320    0.2320    0.2320   0.2320               0.2320  \n",
       "datp_c    0.3620    0.3620    0.3620   0.3620               0.3620  \n",
       "ctp_c     0.1625    0.1625    0.1625   0.1625               0.1625  \n",
       "dctp_c    0.0920    0.0920    0.0920   0.0920               0.0920  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_species_conc.loc[list(metabolite_molar_concs)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rank</th>\n",
       "      <th>mean mmol_per_l</th>\n",
       "      <th>stdev</th>\n",
       "      <th>Acetate</th>\n",
       "      <th>Glycerol</th>\n",
       "      <th>Fructose</th>\n",
       "      <th>L-Malate</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Glucose 6-Phosphate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ac_c</th>\n",
       "      <td>Acetate</td>\n",
       "      <td>610</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.003208</td>\n",
       "      <td>0.007869</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ac_p</th>\n",
       "      <td>Acetate</td>\n",
       "      <td>935</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  rank  mean mmol_per_l     stdev   Acetate  Glycerol  Fructose  \\\n",
       "sid                                                                            \n",
       "ac_c  Acetate   610         0.001320  0.003208  0.007869   0.00001   0.00001   \n",
       "ac_p  Acetate   935         0.000036  0.000063  0.000165   0.00001   0.00001   \n",
       "\n",
       "      L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                           \n",
       "ac_c   0.00001  0.00001              0.00001  \n",
       "ac_p   0.00001  0.00001              0.00001  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_species_conc.loc[['ac_c', 'ac_p']].head()"
   ]
  }
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