{
 "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 Nov 20 19:03:46 2025)\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",
      "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 Nov 20 18:52:47 2025)\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 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",
      "1920 constraints (+43); 2734 variables (+22); 1517 genes (+1); 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; (374 not supported)\n",
      "1899 reactions supported by TD data; (498 not supported)\n",
      "7221 constraints to add\n",
      "7221 constraint ids added to the model (9141 total constraints)\n",
      "3668 ∆rG'/∆rG'˚ variables to add\n",
      "3668 variable ids added to the model (6402 total variables)\n",
      "2407 forward/reverse use variables to add\n",
      "2407 variable ids added to the model (8809 total variables)\n",
      "1525 log concentration variables to add\n",
      "1525 variable ids added to the model (10334 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 (10909 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 1920 (1212)\n",
      "1834 metabolic/transporter reactions with TD data from total 2397\n",
      "9141 constraints (+7264); 10909 variables (+8197); 1517 genes (+1); 3682 parameters (+3677)\n",
      "8 table(s) with parameters loaded from data/iML1515_RBA_rba_parameters.xlsx (Thu Nov 20 19:03:50 2025)\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",
      "10909 reactions -> 13329 isoreactions, 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",
      "9141 species\n",
      "12998 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 XBA model 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 (10783 total constraints)\n",
      "5391 RBA constraints to add\n",
      "5391 constraint ids added to the model (16174 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 (14972 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 (18764 total variables)\n",
      "   7 process machine concentration variables to add\n",
      "   7 variable ids added to the model (18771 total variables)\n",
      "  27 macromolecule concentration target variables to add\n",
      "  27 variable ids added to the model (18798 total variables)\n",
      "   1 metabolites concentration target variable to add\n",
      "   1 variable ids added to the model (18799 total variables)\n",
      "   5 density target and slack variables to add\n",
      "   5 variable ids added to the model (18804 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",
      "XBA model 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 (Fri Nov 21 11:43:36 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",
      "3792 enzymes, 7 process machines, 1834 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: 59.9 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.log(lb), np.log(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 = 0.3646, p = 1.88e-111 (1112 proteins lin scale)\n",
      "Glycerol                 : r² = 0.5282, p = 2.73e-183 (1112 proteins lin scale)\n",
      "Fructose                 : r² = 0.6779, p = 2.37e-275 (1112 proteins lin scale)\n",
      "Glucose                  : r² = 0.8524, p = 0.00e+00 (1112 proteins lin scale)\n",
      "Acetate                  : r² = 0.4070, p = 9.65e-51 ( 432 proteins log scale)\n",
      "Glycerol                 : r² = 0.4938, p = 5.82e-65 ( 428 proteins log scale)\n",
      "Fructose                 : r² = 0.4595, p = 9.71e-57 ( 412 proteins log scale)\n",
      "Glucose                  : r² = 0.4785, p = 4.59e-62 ( 427 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. 641.9 mg/gP predicted\n",
      "           785 metabolic proteins measured 436.9 mg/gP vs. 332.1 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  45.8 mg/gP predicted\n",
      "            90 processes proteins measured 270.0 mg/gP vs. 264.0 mg/gP predicted\n",
      "      504 proteins not measured vs. 358.1 mg/gP predicted\n",
      "             5 dummy  proteins     314.8 mg/gP predicted\n",
      "           499 actual  proteins      43.4 mg/gP predicted\n",
      "total           : r² = 0.8524, p = 0.00e+00 (1112 proteins lin scale)\n",
      " metabolic      : r² = 0.5963, p = 2.13e-156 ( 785 proteins lin scale)\n",
      " transport      : r² = 0.4918, p = 2.13e-36 ( 237 proteins lin scale)\n",
      " processes      : r² = 0.9819, p = 1.65e-78 (  90 proteins lin scale)\n",
      "total           : r² = 0.4785, p = 4.59e-62 ( 427 proteins log scale)\n",
      " metabolic      : r² = 0.4106, p = 4.87e-35 ( 291 proteins log scale)\n",
      " transport      : r² = 0.5833, p = 6.67e-10 (  46 proteins log scale)\n",
      " processes      : r² = 0.8246, p = 5.14e-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: 895.8 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>1235</td>\n",
       "      <td>3.393408</td>\n",
       "      <td>2.396451e+00</td>\n",
       "      <td>5.738807</td>\n",
       "      <td>2.161641</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>7.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adp_c</th>\n",
       "      <td>ADP C10H12N5O10P2</td>\n",
       "      <td>1289</td>\n",
       "      <td>0.101925</td>\n",
       "      <td>7.215396e-02</td>\n",
       "      <td>0.062019</td>\n",
       "      <td>0.058000</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.143528</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.2320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datp_c</th>\n",
       "      <td>DATP C10H12N5O12P3</td>\n",
       "      <td>1291</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>1.520235e-17</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.090500</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>1271</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>2.517439e-01</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.162500</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>1290</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>1.520235e-17</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.092000</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  1235         3.393408  2.396451e+00  5.738807   \n",
       "adp_c    ADP C10H12N5O10P2  1289         0.101925  7.215396e-02  0.062019   \n",
       "datp_c  DATP C10H12N5O12P3  1291         0.090500  1.520235e-17  0.090500   \n",
       "ctp_c     CTP C9H12N3O14P3  1271         0.325000  2.517439e-01  0.650000   \n",
       "dctp_c   DCTP C9H12N3O13P3  1290         0.092000  1.520235e-17  0.092000   \n",
       "\n",
       "        Glycerol  Fructose  L-Malate  Glucose  Glucose 6-Phosphate  \n",
       "sid                                                                 \n",
       "atp_c   2.161641    1.7800  1.780000   1.7800               7.1200  \n",
       "adp_c   0.058000    0.0580  0.143528   0.0580               0.2320  \n",
       "datp_c  0.090500    0.0905  0.090500   0.0905               0.0905  \n",
       "ctp_c   0.162500    0.6500  0.162500   0.1625               0.1625  \n",
       "dctp_c  0.092000    0.0920  0.092000   0.0920               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 (Fri Nov 21 11:42:03 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",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>iML1515_TRBA</td>\n",
       "      <td>0.852415</td>\n",
       "      <td>0.478507</td>\n",
       "      <td>1112</td>\n",
       "      <td>427</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",
       "9                  iML1515_TRBA  0.852415  0.478507          1112   \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  \n",
       "9            427  "
      ]
     },
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_TRBA.xml (Fri Nov 21 11:43:36 2025)\n",
      "MILP Model of iML1515_TFA_RBA\n",
      "18804 variables, 16174 constraints, 149589 non-zero matrix coefficients\n",
      "3792 enzymes, 7 process machines, 1834 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: 32.7 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": 10,
   "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 = 0.3627, p = 1.03e-110 (1112 proteins lin scale)\n",
      "Glycerol                 : r² = 0.5293, p = 7.89e-184 (1112 proteins lin scale)\n",
      "Fructose                 : r² = 0.6778, p = 2.80e-275 (1112 proteins lin scale)\n",
      "Glucose                  : r² = 0.8507, p = 0.00e+00 (1112 proteins lin scale)\n",
      "Acetate                  : r² = 0.4001, p = 9.07e-49 ( 424 proteins log scale)\n",
      "Glycerol                 : r² = 0.4831, p = 7.00e-63 ( 427 proteins log scale)\n",
      "Fructose                 : r² = 0.4624, p = 1.72e-57 ( 414 proteins log scale)\n",
      "Glucose                  : r² = 0.6411, p = 1.75e-95 ( 422 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 = 0.8507, p = 0.00e+00 (1112 proteins lin scale)\n",
      " metabolic      : r² = 0.5907, p = 4.94e-154 ( 785 proteins lin scale)\n",
      " transport      : r² = 0.4965, p = 7.11e-37 ( 237 proteins lin scale)\n",
      " processes      : r² = 0.9819, p = 1.68e-78 (  90 proteins lin scale)\n",
      "total           : r² = 0.6411, p = 1.75e-95 ( 422 proteins log scale)\n",
      " metabolic      : r² = 0.5959, p = 7.98e-58 ( 286 proteins log scale)\n",
      " transport      : r² = 0.5833, p = 6.67e-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: 190.0 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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}