{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Tutorial: Create iML1515 GECKO model with predicted turnover numbers\n",
    "\n",
    "An initial GECKO model of iML1515 was created, with default values primarily being used to expedite the model creation process. In the preceding tutorial, enzyme-related parameters, particularly those associated with transporters, were modified, leading to enhanced protein predictions. However, this necessitated a more profound comprehension of the organism and its modeling. In this tutorial, we will employ a semi-automated procedure to substitute the default turnover numbers for metabolic reactions with those predicted via the TurNuP web portal (https://turnup.cs.hhu.de/Kcat). It should be noted that the interface to the TurNuP web portal may undergo changes in the future, which would require an update to the procedure given below.\n",
    "\n",
    "As was previously established, coupling constraints are critical to GECKO insofar as they couple reaction fluxes with protein requirements. The turnover numbers are important parameters of these coupling constraints. Up to this point, a default value for the turnover numbers has been used for metabolic reactions. It is important to note that turnover numbers can span a range of several orders of magnitude, and in databases such as Brenda, measured values for turnover numbers of metabolic reactions can be found. However, this covers only a small percentage of reactions, even for a model organism like *E. coli*. It is noteworthy that the values obtained for turnover numbers can vary significantly between different laboratories. This variability underscores the challenges associated with accurately measuring turnover numbers (Bisswanger, 2014). Furthermore, in vitro reactions may not accurately reflect the complex in vivo conditions, highlighting the need for more precise and comprehensive experimental methods to determine turnover numbers. \n",
    "\n",
    "TurNuP (Kroll et al., 2023) is a general and organism-independent model that predicts turnover numbers for natural reactions of wild-type enzymes. This prediction is based on provided protein sequence and metabolite references. TurNuP has been trained on turnover numbers extracted from online databases like Brenda. The average deviation of predictions to measured values has been determined to be 4.8-fold. While the predicted turnover numbers might not be entirely accurate, it is hypothesized that they can provide a relative difference within the reaction network, which is more realistic than using a single default value. GECKO and RBA models do not model detailed enzyme saturation levels per reaction. However, it is possible to modulate the turnover numbers to implicitly model a per reaction saturation level, averaged across conditions.\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": {},
   "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 pandas as pd\n",
    "import cobra\n",
    "\n",
    "from f2xba import XbaModel, 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_modified_GECKO'\n",
    "target_model = 'iML1515_predicted_GECKO'\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}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Predict turnover values using TurNuP\n",
    "\n",
    "The TurNuP web portal, developed by the Institute of Computational Cell Biology at Heinrich-Heine-University Düsseldorf, Germany, facilitates the prediction of turnover numbers (kcat values) for metabolic enzyme-catalyzed reactions. The file-based interface supports the upload and download of up to 500 entries in '.xlsx' files. The replacement of manual turnover numbers with predicted values is outlined below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1: create input files for TurNuP\n",
    "\n",
    "The TurNuP web portal requires three pieces of information to be entered into its input records: the amino acid sequence, and, when available, the KEGG compound identifiers for substrates and products. It should be noted that only input records for forward directions are necessary, as TurNuP predicts the same turnover number for forward and reverse directions. Files that are uploaded and downloaded will be placed in the `.\\tmp` directory. An XbaModel instance is then created using the XBA configuration of the baseline model. Subsequently, the method `xba_model.generate_turnup_input()` is invoked to generate the input files, each comprising up to 500 records.\n",
    "\n",
    "Implementation details: ‘xba_model.generate_turnup_input()’ extracts the turnover numbers for metabolic reactions that have been utilized for the baseline model and generates TurNuP input records. For homomeric enzymes, the amino acid sequence of the protein is used; for heteromeric enzymes, the protein sequences are concatenated. KEGG compound identifiers for substrates and products are extracted from the species annotation data of the foundational GEM. In the event that no KEGG identifier has been defined, the respective input field is left empty and the metabolite identifier is returned. This approach enables the manual mapping of such metabolite identifiers to KEGG, InChi, or Smiles references using a Python dictionary. This dictionary can then be provided as an optional parameter, termed \"mids2ref,\" to the ‘xba_model.generate_turnup_input()’ function."
   ]
  },
  {
   "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",
      "3 table(s) with parameters loaded from data/iML1515_modified_GECKO_xba_parameters.xlsx (Thu Nov 20 18:52:54 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_modified_GECKO_kcats.xlsx (Thu Nov 20 18:52:45 2025)\n",
      "5357 kcat values updated from data/iML1515_modified_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",
      "1 table(s) with parameters loaded from data/iML1515_modified_GECKO_kcats.xlsx (Thu Nov 20 18:52:45 2025)\n",
      "1993 kcat records (356 reversible), 1162 records with sequence and reaction\n",
      "353 mids without reference ids, e.g.: ['M_mqn8', 'M_mql8', 'M_q8h2', 'M_2dmmq8', 'M_2dmmql8', 'M_udcpdp', 'M_4ampm', 'M_kdo', 'M_ddcaACP', 'M_myrsACP']\n",
      "4 TurNuP input files (max 500 records) stored under tmp/tmp_turnup_input_1_seq.xlsx ... tmp/tmp_turnup_input_4_seq.xlsx\n",
      " - upload files individually to TurNuP Web Server: https://turnup.cs.hhu.de/Kcat_multiple_input\n",
      " - retrieve TurNuP kcat predictions and continue with process_turnup_output()\n"
     ]
    }
   ],
   "source": [
    "# generate TurNuP input files\n",
    "\n",
    "# delete previously generated input and output files\n",
    "for file in os.listdir('tmp'):\n",
    "    if re.match('.*_outputxx.xlsx', file) or re.match('tmp_turnup_input.*.xlsx', file):\n",
    "#    if re.match('.*_output.xlsx', file) or re.match('tmp_turnup_input.*.xlsx', file):\n",
    "        os.remove(os.path.join('tmp', file))\n",
    "\n",
    "# Create an instance of XbaModel\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",
    "kcats_fname = os.path.join('data', f'{baseline_model}_kcats.xlsx')\n",
    "mids_no_ref = xba_model.generate_turnup_input(kcats_fname, input_basename=os.path.join('tmp', 'tmp_turnup_input'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2: upload input files to TurNuP web portal\n",
    "\n",
    "All input files must be uploaded to the TurNuP web portal using a web browser. To access the TurNuP web portal, navigate to https://turnup.cs.hhu.de/Kcat_multiple_input. Within the designated page titled `kcat Prediction - Multiple Input`, select the `Choose File` option and navigate to the ‘.\\tmp’ directory to select an input file, such as ‘tmp_turnup_input_1_seq.xlsx’. Submit the file by pressing the `Submit` button. This action will redirect to a new page titled ‘Job Status’, accompanied by a `Download Link`. It is imperative to note that initially, this link will not result in any changes to the page; it is essential that the page remains open until the input file has been successfully processed. That is to say, the download link will redirect to a page where the output file can be downloaded. These steps must be repeated for each input file that has been created, resulting in multiple open sessions to the portal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 2.3: download output files from TurNuP\n",
    "\n",
    "The processing of all input files can require up to one hour. It is therefore advisable to periodically access the download links. Upon completion of a job, a new page will be displayed, bearing the title `Your job calculation is finished`. Select `Download` in order to obtain the output file. Subsequently, the page can be closed. The process should be repeated until all output files have been downloaded. Finally, the output files must be copied to the `.\\tmp` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4: update turnover numbers\n",
    "\n",
    "During the generation of input files, information columns were incorporated, which are subsequently transferred to the output files and can be utilized as keys. The predicted turnover numbers contained within the downloaded output files will replace the turnover numbers of the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_modified_GECKO_kcats.xlsx (Thu Nov 20 18:52:45 2025)\n",
      "2349 predicted kcat values added\n",
      "1 table(s) with parameters written to  data/iML1515_predicted_GECKO_kcats.xlsx\n"
     ]
    }
   ],
   "source": [
    "# update turnover numbers\n",
    "\n",
    "# process TurNuP output files\n",
    "pred_kcats = {}\n",
    "for file in os.listdir('tmp'):\n",
    "    if re.match('.*_output.xlsx', file):\n",
    "        with pd.ExcelFile(os.path.join('tmp', file)) as xlsx:\n",
    "            df_pred_kcats = pd.read_excel(xlsx)\n",
    "        for _, row in df_pred_kcats.iterrows():\n",
    "            if type(row['fwd_key']) is str:\n",
    "                pred_kcats[row['fwd_key']] = row['kcat [s^(-1)]']\n",
    "            if type(row['rev_key']) is str:\n",
    "                pred_kcats[row['rev_key']] = row['kcat [s^(-1)]']\n",
    "                \n",
    "# create updated kcats file\n",
    "df_kcats = load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats']\n",
    "count = 0\n",
    "for key in df_kcats.index:\n",
    "    if key in pred_kcats:\n",
    "        df_kcats.at[key, 'kcat_per_s'] = pred_kcats[key]\n",
    "        df_kcats.at[key, 'notes'] = f'TurNuP predicted'\n",
    "        count += 1\n",
    "print(f'{count} predicted kcat values added')\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_kcats.xlsx'), {'kcats': df_kcats})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Create XBA and ECM configuration files\n",
    "\n",
    "The parameter `kcats_fname` in the XBA configuration file is updated, and the parameter `avg_enz_sat` in the ECM configuration file is adjusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 table(s) with parameters loaded from data/iML1515_modified_GECKO_xba_parameters.xlsx (Thu Nov 20 18:52:54 2025)\n",
      "3 table(s) with parameters written to  data/iML1515_predicted_GECKO_xba_parameters.xlsx\n",
      "1 table(s) with parameters loaded from data/iML1515_modified_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:52:54 2025)\n",
      "1 table(s) with parameters written to  data/iML1515_predicted_GECKO_ecm_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Create updated XBA parameter\n",
    "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "xba_params['general'].at['kcats_fname', 'value'] = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n",
    "\n",
    "# Create updated ECM parameter file\n",
    "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'))\n",
    "ecm_params['general'].at['avg_enz_sat', 'value'] = 0.68\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Create GECKO model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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_GECKO_xba_parameters.xlsx (Thu Nov 20 18:55:42 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_GECKO_kcats.xlsx (Thu Nov 20 18:55:42 2025)\n",
      "5357 kcat values updated from data/iML1515_predicted_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",
      "1 table(s) with parameters loaded from data/iML1515_predicted_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:55:42 2025)\n",
      "PaxDb file E.coli - Whole organism (Integrated), year 2023, covering 3743 proteins (total 1000006.5 ppm) loaded from data/511145-WHOLE_ORGANISM-integrated.txt\n",
      "modeled protein fraction of total protein mass 0.5379 g/g\n",
      "1494 protein constraints to add\n",
      "1494 constraint ids added to the model (3371 total constraints)\n",
      "protein constraint: 306.59 mg/gDW - modeled protein\n",
      "   1 constraint ids added to the model (3372 total constraints)\n",
      "1495 protein variables to add\n",
      "1495 variable ids added to the model (7343 total variables)\n",
      "3372 constraints (+1495); 7343 variables (+4631); 1494 genes (-22); 11 parameters (+6)\n",
      "model exported to SBML format: SBML_models/iML1515_predicted_GECKO.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create GECKO model\n",
    "xba_model = XbaModel(os.path.join('SBML_models', f'{fba_model}.xml'))\n",
    "xba_model.configure(os.path.join('data', f'{target_model}_xba_parameters.xlsx'))\n",
    "\n",
    "ec_model = EcModel(xba_model)\n",
    "ec_model.configure(os.path.join('data', f'{target_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 5: Load and optimize GECKO model (cobrapy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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_predicted_GECKO.xml (Thu Nov 20 18:56:05 2025)\n",
      "total modeled protein: 306.59 mg/gDW, average saturation level: 0.68\n"
     ]
    }
   ],
   "source": [
    "# Load model using cobrapy\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, average saturation level: {sigma}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.453 h-1 vs. exp 0.290, diff:  0.163\n",
      "Glycerol                 : pred gr: 0.609 h-1 vs. exp 0.470, diff:  0.139\n",
      "Fructose                 : pred gr: 0.625 h-1 vs. exp 0.540, diff:  0.085\n",
      "L-Malate                 : pred gr: 0.695 h-1 vs. exp 0.550, diff:  0.145\n",
      "Glucose                  : pred gr: 0.653 h-1 vs. exp 0.660, diff: -0.007\n",
      "Glucose 6-Phosphate      : pred gr: 0.691 h-1 vs. exp 0.780, diff: -0.089\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 = 0.0286, p = 7.71e-08 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.0433, p = 3.17e-11 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.1063, p = 3.62e-26 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.2768, p = 5.65e-23 ( 303 proteins log scale)\n",
      "Glycerol                 : r² = 0.2684, p = 1.77e-22 ( 307 proteins log scale)\n",
      "Fructose                 : r² = 0.2718, p = 3.60e-22 ( 298 proteins log scale)\n",
      "Glucose                  : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n",
      "\n",
      "condition: Glucose\n",
      "1494 proteins in model with total predicted mass fraction of 537.9 mg/gP\n",
      "      999 have been measured with mpmf of  514.4 mg/gP vs. 442.5 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 289.0 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs. 153.5 mg/gP predicted\n",
      "      495 proteins not measured vs.  95.4 mg/gP predicted\n",
      "           495 actual  proteins      95.4 mg/gP predicted\n",
      "total           : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.0340, p = 2.92e-07 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.4967, p = 6.70e-37 ( 237 proteins lin scale)\n",
      "total           : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n",
      " metabolic      : r² = 0.3028, p = 7.92e-22 ( 258 proteins log scale)\n",
      " transport      : r² = 0.2613, p = 1.49e-04 (  50 proteins log scale)\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"
     ]
    }
   ],
   "source": [
    "# Optimize model using cobrapy and analyze results\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "\n",
    "    ecm.medium = medium \n",
    "    solution = ecm.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",
    "# Analyze results\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='Glucose')\n",
    "\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.report_protein_levels('Glucose')\n",
    "er.plot_proteins('Glucose', plot_fname=os.path.join('plots', f'{target_model}_proteins_Glucose.pdf'))  \n",
    "er.save_to_escher(df_net_fluxes['Glucose'], os.path.join('escher', target_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Track progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Nov 20 18:53:48 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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model    lin r2    log r2  lin proteins  log proteins\n",
       "No                                                                         \n",
       "1     iML1515_default_GECKO  0.033244  0.190225          1018           299\n",
       "2    iML1515_modified_GECKO  0.135981  0.201613           999           322\n",
       "3   iML1515_predicted_GECKO  0.078803  0.268769           999           308"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "number = 3\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] = 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": [
    "## Closing remarks\n",
    "\n",
    "The process of creating a GECKO model based on turnover numbers predicted by TurNuP is a straightforward one, with the potential to be applied to any organism, provided that KEGG compound identifiers have been configured in the foundational GEM. No organism-specific knowledge is required. However, it should be noted that TurNuP predictions are only applicable to metabolic reactions, as it has not been trained on transporters. The GECKO model has undergone two improvements. Firstly, the required average enzyme saturation level is lower and more realistic, with a decrease from 64% to 80% in the previous model. Secondly, the protein prediction for our reference condition glucose improved when comparing on log scale. However, on the linear protein correlation plot, metabolic proteins are predicted with high level, but zero or now corresponding measurement and vice versa. \n",
    "\n",
    "In the next tutorial, we will analyze in more detail predicted flux distributions and protein concentration. Subsequent to this analysis, we will attempt to manually adjust selected turnover numbers to improve protein correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## (alternatively) Load GECKO model, optimize and analyze (gurobipy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_GECKO.xml (Thu Nov 20 18:56:05 2025)\n",
      "LP Model of iML1515_GECKO\n",
      "7343 variables, 3372 constraints, 28438 non-zero matrix coefficients\n",
      "total modeled protein: 306.59 mg/gDW, average saturation level: 0.68\n"
     ]
    }
   ],
   "source": [
    "# Load model using gurobipy\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "eo = EcmOptimization(fname)\n",
    "total_protein = eo.get_variable_bounds('V_PC_total')['V_PC_total'][1]\n",
    "sigma = eo.avg_enz_saturation\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, average saturation level: {sigma}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.453 h-1 vs. exp 0.290, diff:  0.163\n",
      "Glycerol                 : pred gr: 0.609 h-1 vs. exp 0.470, diff:  0.139\n",
      "Fructose                 : pred gr: 0.625 h-1 vs. exp 0.540, diff:  0.085\n",
      "L-Malate                 : pred gr: 0.695 h-1 vs. exp 0.550, diff:  0.145\n",
      "Glucose                  : pred gr: 0.653 h-1 vs. exp 0.660, diff: -0.007\n",
      "Glucose 6-Phosphate      : pred gr: 0.691 h-1 vs. exp 0.780, diff: -0.089\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 = 0.0286, p = 7.71e-08 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.0433, p = 3.17e-11 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.1063, p = 3.62e-26 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.2768, p = 5.65e-23 ( 303 proteins log scale)\n",
      "Glycerol                 : r² = 0.2684, p = 1.77e-22 ( 307 proteins log scale)\n",
      "Fructose                 : r² = 0.2718, p = 3.60e-22 ( 298 proteins log scale)\n",
      "Glucose                  : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n",
      "\n",
      "condition: Glucose\n",
      "1494 proteins in model with total predicted mass fraction of 537.9 mg/gP\n",
      "      999 have been measured with mpmf of  514.4 mg/gP vs. 442.5 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 289.0 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs. 153.5 mg/gP predicted\n",
      "      495 proteins not measured vs.  95.4 mg/gP predicted\n",
      "           495 actual  proteins      95.4 mg/gP predicted\n",
      "total           : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.0340, p = 2.92e-07 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.4967, p = 6.70e-37 ( 237 proteins lin scale)\n",
      "total           : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n",
      " metabolic      : r² = 0.3028, p = 7.92e-22 ( 258 proteins log scale)\n",
      " transport      : r² = 0.2613, p = 1.49e-04 (  50 proteins log scale)\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"
     ]
    }
   ],
   "source": [
    "# Optimize model using gurobipy and analyze results\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",
    "# Analyze results\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='Glucose')\n",
    "\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.report_protein_levels('Glucose')\n",
    "er.plot_proteins('Glucose', plot_fname=os.path.join('plots', f'{target_model}_proteins_Glucose.pdf'))  \n",
    "er.save_to_escher(df_net_fluxes['Glucose'], os.path.join('escher', target_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## References\n",
    "\n",
    "- Bisswanger, H. (2014). Enzyme assays. Perspectives in Science, 1(1), 41-55. https://doi.org/https://doi.org/10.1016/j.pisc.2014.02.005 \n",
    "- Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023). Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nature Communications, 14(1), 4139. https://doi.org/10.1038/s41467-023-39840-4 "
   ]
  },
  {
   "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
}