{
 "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 Nov 20 19:02:51 2025)\n",
      "  22 gene product(s) removed from reactions (1494 gene products remaining)\n",
      "   2 attributes on reaction instances updated\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 Nov 20 18:52:47 2025)\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 Nov 20 19:02:51 2025)\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",
      "5 table(s) with parameters loaded from data/iML1515_TFA_tfa_parameters.xlsx (Fri Nov 21 11:16:34 2025)\n",
      "  16 thermo data attributes updated\n",
      " 938 metabolites with TD data covering 1546 model species; (331 not supported)\n",
      "1899 reactions supported by TD data; (476 not supported)\n",
      "7221 constraints to add\n",
      "7221 constraint ids added to the model (9098 total constraints)\n",
      "3668 ∆rG'/∆rG'˚ variables to add\n",
      "3668 variable ids added to the model (6380 total variables)\n",
      "2407 forward/reverse use variables to add\n",
      "2407 variable ids added to the model (8787 total variables)\n",
      "1525 log concentration variables to add\n",
      "1525 variable ids added to the model (10312 total variables)\n",
      "1834 TD reactions split in forward/reverse, 0 opened reverse direction\n",
      "2407 fwd/rev reactions to couple with flux direction\n",
      "2407 attributes on reaction instances updated\n",
      "   2 RHS variables to add\n",
      "   2 variable ids added to the model (10887 total variables)\n",
      "3682 parameters\n",
      "   3 ∆Gr'˚ variables need relaxation.\n",
      "   3 attributes on parameter instances updated\n",
      "1525 species (929 metabolites) with TD data from total 1877 (1169)\n",
      "1834 metabolic/transporter reactions with TD data from total 2375\n",
      "9098 constraints (+7221); 10887 variables (+8175); 1494 genes (-22); 3682 parameters (+3677)\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx (Thu Nov 20 19:02:51 2025)\n",
      "modeled protein fraction of total protein mass 0.3733 g/g\n",
      "1494 protein constraints to add\n",
      "1494 constraint ids added to the model (10592 total constraints)\n",
      "protein constraint: 212.80 mg/gDW - modeled protein\n",
      "   1 constraint ids added to the model (10593 total constraints)\n",
      "1495 protein variables to add\n",
      "1495 variable ids added to the model (14968 total variables)\n",
      "10593 constraints (+8716); 14968 variables (+12256); 1494 genes (-22); 3688 parameters (+3683)\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 (Fri Nov 21 11:40:58 2025)\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",
      "1834 TD reaction constraints\n",
      "total modeled protein: 212.80 mg/gDW, enzyme saturation level: 0.5\n",
      "13 metabolite concentrations constrained\n",
      "Duration: 31.3 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 = 2.25e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.70e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8698, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.5130, p = 4.74e-50 ( 310 proteins log scale)\n",
      "Glycerol                 : r² = 0.4923, p = 5.47e-48 ( 315 proteins log scale)\n",
      "Fructose                 : r² = 0.5382, p = 6.21e-53 ( 306 proteins log scale)\n",
      "Glucose                  : r² = 0.5659, p = 1.16e-58 ( 315 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: 22.3 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>1240</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adp_c</th>\n",
       "      <td>ADP C10H12N5O10P2</td>\n",
       "      <td>1270</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.2320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datp_c</th>\n",
       "      <td>DATP C10H12N5O12P3</td>\n",
       "      <td>1269</td>\n",
       "      <td>0.2715</td>\n",
       "      <td>1.402020e-01</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.0905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctp_c</th>\n",
       "      <td>CTP C9H12N3O14P3</td>\n",
       "      <td>1248</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dctp_c</th>\n",
       "      <td>DCTP C9H12N3O13P3</td>\n",
       "      <td>1284</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>1.520235e-17</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.092</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  1240           1.7800  0.000000e+00    1.780   \n",
       "adp_c    ADP C10H12N5O10P2  1270           0.2320  0.000000e+00    0.232   \n",
       "datp_c  DATP C10H12N5O12P3  1269           0.2715  1.402020e-01    0.362   \n",
       "ctp_c     CTP C9H12N3O14P3  1248           0.6500  0.000000e+00    0.650   \n",
       "dctp_c   DCTP C9H12N3O13P3  1284           0.0920  1.520235e-17    0.092   \n",
       "\n",
       "        Glycerol  Fructose  L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                                                 \n",
       "atp_c      1.780    1.7800     1.780    1.780               1.7800  \n",
       "adp_c      0.232    0.2320     0.232    0.232               0.2320  \n",
       "datp_c     0.362    0.0905     0.362    0.362               0.0905  \n",
       "ctp_c      0.650    0.6500     0.650    0.650               0.6500  \n",
       "dctp_c     0.092    0.0920     0.092    0.092               0.0920  "
      ]
     },
     "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 Nov 20 19:10:13 2025)\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>0.852647</td>\n",
       "      <td>0.490353</td>\n",
       "      <td>1112</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>iML1515_TGECKO</td>\n",
       "      <td>0.869849</td>\n",
       "      <td>0.565869</td>\n",
       "      <td>999</td>\n",
       "      <td>315</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  0.852647  0.490353          1112   \n",
       "8                iML1515_TGECKO  0.869849  0.565869           999   \n",
       "\n",
       "    log proteins  \n",
       "No                \n",
       "1            299  \n",
       "2            322  \n",
       "3            308  \n",
       "4            308  \n",
       "5            313  \n",
       "6            425  \n",
       "8            315  "
      ]
     },
     "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 (Fri Nov 21 11:40:58 2025)\n",
      "MILP Model of iML1515_TFA_GECKO\n",
      "14968 variables, 10593 constraints, 53961 non-zero matrix coefficients\n",
      "1834 TD reaction constraints\n",
      "13 metabolite concentrations constrained\n",
      "Duration: 15.9 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 = 0.4610, p = 6.54e-44 ( 315 proteins log scale)\n",
      "Glycerol                 : r² = 0.4923, p = 5.47e-48 ( 315 proteins log scale)\n",
      "Fructose                 : r² = 0.5382, p = 6.21e-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: 6.2 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>368</td>\n",
       "      <td>3.5600</td>\n",
       "      <td>2.757564e+00</td>\n",
       "      <td>7.1200</td>\n",
       "      <td>7.1200</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>445</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>8.985321e-02</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.0580</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>464</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>1.520235e-17</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctp_c</th>\n",
       "      <td>CTP C9H12N3O14P3</td>\n",
       "      <td>446</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>3.040471e-17</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>463</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>1.520235e-17</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   368           3.5600  2.757564e+00   7.1200   \n",
       "adp_c    ADP C10H12N5O10P2   445           0.1740  8.985321e-02   0.0580   \n",
       "datp_c  DATP C10H12N5O12P3   464           0.0905  1.520235e-17   0.0905   \n",
       "ctp_c     CTP C9H12N3O14P3   446           0.1625  3.040471e-17   0.1625   \n",
       "dctp_c   DCTP C9H12N3O13P3   463           0.0920  1.520235e-17   0.0920   \n",
       "\n",
       "        Glycerol  Fructose  L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                                                 \n",
       "atp_c     7.1200    1.7800    1.7800   1.7800               1.7800  \n",
       "adp_c     0.0580    0.2320    0.2320   0.2320               0.2320  \n",
       "datp_c    0.0905    0.0905    0.0905   0.0905               0.0905  \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": {
      "text/html": [
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       "<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>516</td>\n",
       "      <td>0.015653</td>\n",
       "      <td>0.038317</td>\n",
       "      <td>0.093868</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>491</td>\n",
       "      <td>0.032806</td>\n",
       "      <td>0.080332</td>\n",
       "      <td>0.196783</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   516         0.015653  0.038317  0.093868   0.00001   0.00001   \n",
       "ac_p  Acetate   491         0.032806  0.080332  0.196783   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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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