{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Tutorial: Create iML1515 TRBA model\n",
    "\n",
    "The generation of a TRBA model, a thermodynamics-constrained RBA model, can be achieved in a manner analogous to the generation of a TGECKO model. This process can be executed with minimal effort by reusing the XBA, RBA, and TFA configuration files and workflows that have been established in the preceding 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": [
    "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, RbaModel\n",
    "from f2xba import RbaOptimization, RbaResults\n",
    "from f2xba.utils.mapping_utils import load_parameter_file, write_parameter_file\n",
    "\n",
    "fba_model = 'iML1515'\n",
    "baseline_model = 'iML1515_RBA'\n",
    "target_model = 'iML1515_TRBA'\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 TRBA model\n",
    "\n",
    "The generation of a TRBA model is initiated with an XbaModel that has been configured with the XBA configuration file, which was previously utilized for the RBA model. Subsequently, thermodynamics constraints are introduced using the TfaModel. Finally, the RBA configuration data is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading: SBML_models/iML1515.xml (last modified: Thu Dec  5 10:03:46 2024)\n",
      "6 table(s) with parameters loaded from data/iML1515_RBA_xba_parameters.xlsx (Thu Feb 12 15:27:14 2026)\n",
      "  22 gene product(s) removed from reactions (1494 gene products remaining)\n",
      "  23 gene products added to the model (1517 total gene products)\n",
      "  43 constraint ids added to the model (1920 total constraints)\n",
      "  22 variable ids added to the model (2734 total variables)\n",
      "   3 attributes on reaction instances updated\n",
      "25 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",
      "24 reactions with charge imbalances, e.g. [R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, R_FMETTRS, ...], check XbaModel.charge_imbalances.\n",
      "extracting nucleotide sequence from data/ncbi/chromosome_U00096.3_fasta.txt\n",
      "chromosome     : 4641652 nucleotides, 4308 mRNAs, 22 rRNAs, 86 tRNAs\n",
      "extracting UniProt protein data from data/uniprot_organism_83333.tsv\n",
      "1517 proteins created\n",
      "140 cofactors used in proteins could not be mapped (are not considered). These correspond to 18 CHEBI ids, which could be added to the parameter spreadsheet (chebi2sid):\n",
      "{'49883': '[4Fe-4S] cluster', '60539': 'Mo-bis(molybdopterin guanine dinucleotide)', '30413': 'heme', '190135': '[2Fe-2S] cluster', '21137': '[3Fe-4S] cluster', '24875': 'Fe cation', '25213': 'a metal cation', '60240': 'a divalent metal cation', '87746': 'prenylated FMN', '18408': 'adenosylcob(III)alamin', '23378': 'Cu cation', '30408': 'iron-sulfur cluster', '79027': 'L-topaquinone', '61717': 'heme c', '60342': 'dipyrromethane', '62814': 'heme d cis-diol', '71302': 'Mo-molybdopterin', '28115': 'methylcob(III)alamin'}\n",
      " 479 cofactors mapped to species ids\n",
      "1224 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",
      "2243 reactions catalyzed by 1224 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",
      "1920 constraints (+43); 2734 variables (+22); 1517 genes (+1); 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; (375 not supported)\n",
      "1897 reactions supported by TD data; (500 not supported)\n",
      "7410 constraints to add\n",
      "7410 constraint ids added to the model (9330 total constraints)\n",
      "3794 ∆rG'/∆rG'˚ variables to add\n",
      "3794 variable ids added to the model (6528 total variables)\n",
      "2470 forward/reverse use variables to add\n",
      "2470 variable ids added to the model (8998 total variables)\n",
      "1528 log concentration variables to add\n",
      "1528 variable ids added to the model (10526 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 (11101 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 1920 (1212)\n",
      "1897 metabolic/transporter reactions with TD data from total 2397\n",
      "9330 constraints (+7453); 11101 variables (+8389); 1517 genes (+1); 3811 parameters (+3806)\n",
      "8 table(s) with parameters loaded from data/iML1515_RBA_rba_parameters.xlsx (Thu Feb 12 15:27:14 2026)\n",
      "   2 compartment(s) added\n",
      "  94 gene products added to the model (1611 total gene products)\n",
      "  94 gene product(s) added for process machineries\n",
      "  94 protein(s) created with UniProt information\n",
      "140 cofactors used in proteins could not be mapped (are not considered). These correspond to 18 CHEBI ids, which could be added to the parameter spreadsheet (chebi2sid):\n",
      "{'49883': '[4Fe-4S] cluster', '60539': 'Mo-bis(molybdopterin guanine dinucleotide)', '30413': 'heme', '190135': '[2Fe-2S] cluster', '21137': '[3Fe-4S] cluster', '24875': 'Fe cation', '25213': 'a metal cation', '60240': 'a divalent metal cation', '87746': 'prenylated FMN', '18408': 'adenosylcob(III)alamin', '23378': 'Cu cation', '30408': 'iron-sulfur cluster', '79027': 'L-topaquinone', '61717': 'heme c', '60342': 'dipyrromethane', '62814': 'heme d cis-diol', '71302': 'Mo-molybdopterin', '28115': 'methylcob(III)alamin'}\n",
      " 487 cofactor(s) mapped to species ids for added protein\n",
      "11101 reactions -> 13521 iso-reactions, including pseudo reactions\n",
      "   4 density constraints added\n",
      "  73 targets in 7 target groups\n",
      "   5 targets in 8 target groups\n",
      "   5 compartments\n",
      "9330 species\n",
      "13190 reactions\n",
      " 331 medium metabolites (24 > 0.0 mmol/l)\n",
      "   1 macromolecules (4 components) in dna \n",
      "  25 macromolecules (4 components) in rna \n",
      "1611 macromolecules (48 components) in protein \n",
      "   5 dummy proteins added\n",
      "3946 enzymes\n",
      "   7 processes and 6 processing maps\n",
      "5536 functions,  167 aggregates\n",
      ">>> RBA model created\n",
      "update XbaModel with RBA parameters\n",
      "   1 attributes on compartment instances updated\n",
      "   0 reactions removed\n",
      "1642 macromoledules to add\n",
      "1642 constraint ids added to the model (10972 total constraints)\n",
      "5391 RBA constraints to add\n",
      "5391 constraint ids added to the model (16363 total constraints)\n",
      "5380 fwd/rev reactions to couple with enzyme efficiency constraints\n",
      "5380 attributes on reaction instances updated\n",
      "1643 processing reactions to add\n",
      "1643 variable ids added to the model (15164 total variables)\n",
      "5703 parameter values calulated based on 1.00 h-1 growth rate and medium\n",
      "3792 enzyme concentration variables to add\n",
      "3792 variable ids added to the model (18956 total variables)\n",
      "   7 process machine concentration variables to add\n",
      "   7 variable ids added to the model (18963 total variables)\n",
      "  27 macromolecule concentration target variables to add\n",
      "  27 variable ids added to the model (18990 total variables)\n",
      "   1 metabolites concentration target variable to add\n",
      "   1 variable ids added to the model (18991 total variables)\n",
      "   5 density target and slack variables to add\n",
      "   5 variable ids added to the model (18996 total variables)\n",
      "   3 Flux/variable bounds need to be updated.\n",
      "   3 attributes on reaction instances updated\n",
      "   1 objectives removed\n",
      "Dummy FBA objective configured: maximize V_TSMC\n",
      "3828 variables (reactions) require srefs id for initial assignment\n",
      "3828 attributes on reaction instances updated\n",
      "   5 function definitions added to XBA model\n",
      "   1 parameters removed following cleanup: ['R_ATPM_lower_bound']\n",
      "XbaModel updated with RBA configuration\n",
      "model exported to SBML format: SBML_models/iML1515_TRBA.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load GEM and extend the model with data from parameters file\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",
    "rba_model = RbaModel(xba_model)\n",
    "rba_model.configure(os.path.join('data', f'{baseline_model}_rba_parameters.xlsx'))\n",
    "rba_model.export(os.path.join('SBML_models', f'{target_model}.xml'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## Step 3. Load and optimize TRBA model (COBRApy)\n",
    "\n",
    "It should be noted that the gurobipy interface, which will be utilized in the forthcoming examples, has been demonstrated to exhibit a substantial increase in processing speed."
   ]
  },
  {
   "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_TRBA.xml (Thu Feb 12 16:48:48 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",
      "3792 enzymes, 7 process machines, 1897 TD reaction constraints\n",
      "RBA enzyme efficiency constraints configured (C_EF_xxx, C_ER_xxx) ≤ 0\n",
      "average saturation level: 0.5, importer Km: 1.0 mmol/l\n",
      "1611 genes: (492) transporter, (1025) metabolic, (94) process machines\n",
      "13 metabolite concentrations constrained\n",
      "Duration: 68.2 s\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "rbam = cobra.io.read_sbml_model(fname)\n",
    "\n",
    "# Load SBML model with RbaOptimization (required for optimization under COBRApy)\n",
    "ro = RbaOptimization(fname, rbam)\n",
    "sigma = ro.avg_enz_saturation\n",
    "importer_km = ro.importer_km\n",
    "print(f'average saturation level: {sigma}, importer Km: {importer_km} mmol/l')\n",
    "all_genes = set(ro.m_dict['fbcGeneProducts']['label'].values)\n",
    "tx_genes, metab_genes = ro.get_tx_metab_genes()\n",
    "pm_genes = all_genes.difference(tx_genes.union(metab_genes))\n",
    "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic, '\n",
    "      f'({len(pm_genes)}) process machines')\n",
    "\n",
    "# Set nucleotide concentrations (optional)\n",
    "for sid, (lb, ub) in metabolite_molar_concs.items():\n",
    "    rbam.reactions.get_by_id('V_LC_' + sid).bounds = (np.log10(lb), np.log10(ub))\n",
    "print(f'{len(metabolite_molar_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: 0.414 h-1 vs. exp 0.290, diff:  0.124\n",
      "Glycerol                 : pred gr: 0.402 h-1 vs. exp 0.470, diff: -0.068\n",
      "Fructose                 : pred gr: 0.553 h-1 vs. exp 0.540, diff:  0.013\n",
      "L-Malate                 : pred gr: 0.441 h-1 vs. exp 0.550, diff: -0.109\n",
      "Glucose                  : pred gr: 0.646 h-1 vs. exp 0.660, diff: -0.014\n",
      "Glucose 6-Phosphate      : pred gr: 0.597 h-1 vs. exp 0.780, diff: -0.183\n",
      "Acetate                  : r² = 0.3685, p = 6.15e-113 (1112 proteins lin scale)\n",
      "Glycerol                 : r² = 0.5317, p = 4.47e-185 (1112 proteins lin scale)\n",
      "Fructose                 : r² = 0.6779, p = 2.40e-275 (1112 proteins lin scale)\n",
      "Glucose                  : r² = 0.8505, p = 0.00e+00 (1112 proteins lin scale)\n",
      "Acetate                  : r² = 0.4140, p = 8.21e-51 ( 423 proteins log scale)\n",
      "Glycerol                 : r² = 0.5012, p = 2.53e-66 ( 428 proteins log scale)\n",
      "Fructose                 : r² = 0.4760, p = 2.26e-59 ( 411 proteins log scale)\n",
      "Glucose                  : r² = 0.5040, p = 5.21e-67 ( 429 proteins log scale)\n",
      "condition: Glucose\n",
      "1616 proteins in model with total predicted mass fraction of 1000.0 mg/gP\n",
      "     1112 have been measured with mpmf of  807.2 mg/gP vs. 640.4 mg/gP predicted\n",
      "           785 metabolic proteins measured 436.9 mg/gP vs. 330.4 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  46.0 mg/gP predicted\n",
      "            90 processes proteins measured 270.0 mg/gP vs. 264.0 mg/gP predicted\n",
      "      504 proteins not measured vs. 359.6 mg/gP predicted\n",
      "             5 dummy  proteins     315.4 mg/gP predicted\n",
      "           499 actual  proteins      44.2 mg/gP predicted\n",
      "total           : r² = 0.8505, p = 0.00e+00 (1112 proteins lin scale)\n",
      " metabolic      : r² = 0.5907, p = 4.93e-154 ( 785 proteins lin scale)\n",
      " transport      : r² = 0.4913, p = 2.39e-36 ( 237 proteins lin scale)\n",
      " processes      : r² = 0.9819, p = 1.68e-78 (  90 proteins lin scale)\n",
      "total           : r² = 0.5040, p = 5.21e-67 ( 429 proteins log scale)\n",
      " metabolic      : r² = 0.4379, p = 2.76e-38 ( 293 proteins log scale)\n",
      " transport      : r² = 0.5831, p = 6.73e-10 (  46 proteins log scale)\n",
      " processes      : r² = 0.8245, p = 5.18e-35 (  90 proteins log scale)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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: 1340.7 s\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    rel_mmol_per_l =  (sigma / (1.0-min(sigma, .99))) * importer_km\n",
    "    ex_sidx2mmol_per_l = {re.sub('EX_', '', ex_ridx): rel_mmol_per_l for ex_ridx in medium}\n",
    "    ex_sidx2mmol_per_l['h_e'] = 100.0 * sigma   # H-symport reactions to be not constraint by main metabolite\n",
    "    ro.medium = ex_sidx2mmol_per_l\n",
    "\n",
    "    solution = ro.solve(gr_min=0.0, gr_max=1.2, bisection_tol=1e-3)\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:25s}: INFEASIBLE')\n",
    "            \n",
    "rr = RbaResults(ro, pred_results, df_mpmf)\n",
    "df_fluxes = rr.collect_fluxes()\n",
    "df_all_net_fluxes = rr.collect_fluxes(net=True)    \n",
    "metabolic_rids = [rid for rid in list(df_all_net_fluxes.index) if re.match('(PROD)|(DEGR)', rid) is None]\n",
    "synthesis_rids = [rid for rid in list(df_all_net_fluxes.index) if re.match('(PROD)|(DEGR)', rid)]\n",
    "df_net_fluxes = df_all_net_fluxes.loc[metabolic_rids]\n",
    "\n",
    "df_synt_fluxes = df_all_net_fluxes.loc[synthesis_rids]\n",
    "df_protein_conc = rr.collect_protein_results('µmol_per_gDW')\n",
    "df_proteins = rr.collect_protein_results('mg_per_gP')\n",
    "df_enzyme_conc = rr.collect_enzyme_results('µmol_per_gDW')\n",
    "df_rna_conc = rr.collect_rna_results('µmol_per_gDW')\n",
    "df_occupancy = rr.collect_density_results()\n",
    "df_capacity = rr.collect_density_results(capacity=True)\n",
    "df_species_conc = rr.collect_species_conc()\n",
    "\n",
    "rr.report_proteomics_correlation(scale='lin')\n",
    "rr.report_proteomics_correlation(scale='log')\n",
    "rr.report_protein_levels(reference_cond)\n",
    "rr.plot_grs(exp_grs, highlight=reference_cond)\n",
    "rr.plot_proteins(reference_cond)\n",
    "rr.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', f'{target_model}_TRBA'))\n",
    "rr.save_to_escher(df_species_conc[reference_cond], os.path.join('escher', f'{target_model}_TRBA'))\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>1246</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>2.757564</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>7.1200</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>7.120</td>\n",
       "      <td>1.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adp_c</th>\n",
       "      <td>ADP C10H12N5O10P2</td>\n",
       "      <td>1301</td>\n",
       "      <td>0.174000</td>\n",
       "      <td>0.089853</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datp_c</th>\n",
       "      <td>DATP C10H12N5O12P3</td>\n",
       "      <td>1302</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.110839</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.090500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctp_c</th>\n",
       "      <td>CTP C9H12N3O14P3</td>\n",
       "      <td>1288</td>\n",
       "      <td>0.379685</td>\n",
       "      <td>0.209675</td>\n",
       "      <td>0.251642</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.223186</td>\n",
       "      <td>0.251642</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.251642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dctp_c</th>\n",
       "      <td>DCTP C9H12N3O13P3</td>\n",
       "      <td>1306</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.092000</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  1246         3.560000  2.757564  1.780000   \n",
       "adp_c    ADP C10H12N5O10P2  1301         0.174000  0.089853  0.232000   \n",
       "datp_c  DATP C10H12N5O12P3  1302         0.135750  0.110839  0.090500   \n",
       "ctp_c     CTP C9H12N3O14P3  1288         0.379685  0.209675  0.251642   \n",
       "dctp_c   DCTP C9H12N3O13P3  1306         0.092000  0.000000  0.092000   \n",
       "\n",
       "        Glycerol  Fructose  L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                                                 \n",
       "atp_c     7.1200  1.780000  1.780000    7.120             1.780000  \n",
       "adp_c     0.0580  0.232000  0.232000    0.058             0.232000  \n",
       "datp_c    0.0905  0.090500  0.090500    0.362             0.090500  \n",
       "ctp_c     0.6500  0.223186  0.251642    0.650             0.251642  \n",
       "dctp_c    0.0920  0.092000  0.092000    0.092             0.092000  "
      ]
     },
     "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 16:45:53 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",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>iML1515_TRBA</td>\n",
       "      <td>0.850468</td>\n",
       "      <td>0.504015</td>\n",
       "      <td>1112</td>\n",
       "      <td>429</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",
       "9                  iML1515_TRBA  0.850468  0.504015          1112   \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  \n",
       "9            429  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "number = 9\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 = rr.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] = 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_TRBA.xml (Thu Feb 12 16:48:48 2026)\n",
      "MILP Model of iML1515_TFA_RBA\n",
      "18996 variables, 16363 constraints, 150325 non-zero matrix coefficients\n",
      "3792 enzymes, 7 process machines, 1897 TD reaction constraints\n",
      "RBA enzyme efficiency constraints configured (C_EF_xxx, C_ER_xxx) ≤ 0\n",
      "average saturation level: 0.5, importer Km: 1.0 mmol/l\n",
      "1611 genes: (492) transporter, (1025) metabolic, (94) process machines\n",
      "13 metabolite concentrations constrained\n",
      "Duration: 34.2 s\n"
     ]
    }
   ],
   "source": [
    "# Load TRBA model using gurobipy\n",
    "start = time.time()\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "ro = RbaOptimization(fname)\n",
    "sigma = ro.avg_enz_saturation\n",
    "importer_km = ro.importer_km\n",
    "print(f'average saturation level: {sigma}, importer Km: {importer_km} mmol/l')\n",
    "all_genes = set(ro.m_dict['fbcGeneProducts']['label'].values)\n",
    "tx_genes, metab_genes = ro.get_tx_metab_genes()\n",
    "pm_genes = all_genes.difference(tx_genes.union(metab_genes))\n",
    "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic, '\n",
    "      f'({len(pm_genes)}) process machines')\n",
    "\n",
    "# Set nucleotide concentrations (optional)\n",
    "orig_concs = ro.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: 0.414 h-1 vs. exp 0.290, diff:  0.124\n",
      "Glycerol                 : pred gr: 0.402 h-1 vs. exp 0.470, diff: -0.068\n",
      "Fructose                 : pred gr: 0.553 h-1 vs. exp 0.540, diff:  0.013\n",
      "L-Malate                 : pred gr: 0.441 h-1 vs. exp 0.550, diff: -0.109\n",
      "Glucose                  : pred gr: 0.646 h-1 vs. exp 0.660, diff: -0.014\n",
      "Glucose 6-Phosphate      : pred gr: 0.597 h-1 vs. exp 0.780, diff: -0.183\n",
      "Acetate                  : r² = 0.3647, p = 1.85e-111 (1112 proteins lin scale)\n",
      "Glycerol                 : r² = 0.5304, p = 2.10e-184 (1112 proteins lin scale)\n",
      "Fructose                 : r² = 0.6775, p = 4.84e-275 (1112 proteins lin scale)\n",
      "Glucose                  : r² = 0.8505, p = 0.00e+00 (1112 proteins lin scale)\n",
      "Acetate                  : r² = 0.4254, p = 1.40e-53 ( 431 proteins log scale)\n",
      "Glycerol                 : r² = 0.5002, p = 3.04e-65 ( 422 proteins log scale)\n",
      "Fructose                 : r² = 0.4623, p = 1.79e-57 ( 414 proteins log scale)\n",
      "Glucose                  : r² = 0.4980, p = 4.93e-66 ( 430 proteins log scale)\n",
      "condition: Glucose\n",
      "1616 proteins in model with total predicted mass fraction of 1000.0 mg/gP\n",
      "     1112 have been measured with mpmf of  807.2 mg/gP vs. 640.4 mg/gP predicted\n",
      "           785 metabolic proteins measured 436.9 mg/gP vs. 330.4 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  46.0 mg/gP predicted\n",
      "            90 processes proteins measured 270.0 mg/gP vs. 264.0 mg/gP predicted\n",
      "      504 proteins not measured vs. 359.6 mg/gP predicted\n",
      "             5 dummy  proteins     315.4 mg/gP predicted\n",
      "           499 actual  proteins      44.2 mg/gP predicted\n",
      "total           : r² = 0.8505, p = 0.00e+00 (1112 proteins lin scale)\n",
      " metabolic      : r² = 0.5907, p = 4.92e-154 ( 785 proteins lin scale)\n",
      " transport      : r² = 0.4913, p = 2.39e-36 ( 237 proteins lin scale)\n",
      " processes      : r² = 0.9819, p = 1.68e-78 (  90 proteins lin scale)\n",
      "total           : r² = 0.4980, p = 4.93e-66 ( 430 proteins log scale)\n",
      " metabolic      : r² = 0.4320, p = 9.59e-38 ( 294 proteins log scale)\n",
      " transport      : r² = 0.5831, p = 6.73e-10 (  46 proteins log scale)\n",
      " processes      : r² = 0.8245, p = 5.18e-35 (  90 proteins log scale)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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: 173.9 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",
    "    rel_mmol_per_l =  (sigma / (1.0-min(sigma, .99))) * importer_km\n",
    "    ex_sidx2mmol_per_l = {re.sub('EX_', '', ex_ridx): rel_mmol_per_l for ex_ridx in medium}\n",
    "    ex_sidx2mmol_per_l['h_e'] = 100.0 * sigma   # H-symport reactions to be not constraint by main metabolite\n",
    "    ro.medium = ex_sidx2mmol_per_l\n",
    "\n",
    "    solution = ro.solve(gr_min=0.0, gr_max=1.2, bisection_tol=1e-3)\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:25s}: INFEASIBLE')\n",
    "            \n",
    "rr = RbaResults(ro, pred_results, df_mpmf)\n",
    "df_fluxes = rr.collect_fluxes()\n",
    "df_all_net_fluxes = rr.collect_fluxes(net=True)    \n",
    "metabolic_rids = [rid for rid in list(df_all_net_fluxes.index) if re.match('(PROD)|(DEGR)', rid) is None]\n",
    "synthesis_rids = [rid for rid in list(df_all_net_fluxes.index) if re.match('(PROD)|(DEGR)', rid)]\n",
    "df_net_fluxes = df_all_net_fluxes.loc[metabolic_rids]\n",
    "\n",
    "df_synt_fluxes = df_all_net_fluxes.loc[synthesis_rids]\n",
    "df_protein_conc = rr.collect_protein_results('µmol_per_gDW')\n",
    "df_proteins = rr.collect_protein_results('mg_per_gP')\n",
    "df_enzyme_conc = rr.collect_enzyme_results('µmol_per_gDW')\n",
    "df_rna_conc = rr.collect_rna_results('µmol_per_gDW')\n",
    "df_occupancy = rr.collect_density_results()\n",
    "df_capacity = rr.collect_density_results(capacity=True)\n",
    "df_species_conc = rr.collect_species_conc()\n",
    "\n",
    "rr.report_proteomics_correlation(scale='lin')\n",
    "rr.report_proteomics_correlation(scale='log')\n",
    "rr.report_protein_levels(reference_cond)\n",
    "rr.plot_grs(exp_grs, highlight=reference_cond)\n",
    "rr.plot_proteins(reference_cond)\n",
    "rr.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', target_model))\n",
    "rr.save_to_escher(df_species_conc[reference_cond], os.path.join('escher', target_model))\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.12",
   "language": "python",
   "name": "python310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}