{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Tutorial: Create iML1515 GECKO with modified default values\n",
    "\n",
    "In the preceding tutorial, an initial GECKO model of iML1515 was developed, incorporating minimal specific parametrization, default turnover numbers for metabolic and transporter reactions, and enzyme compositions derived from the BioCyc online database. While the correlation between predicted and measured growth rates was deemed acceptable, the correlation between predicted and measured protein levels was found to be unsatisfactory. In this tutorial, we will methodically modify parameters to enhance the prediction of protein levels, with a particular focus on transporters. To this end, a more thorough understanding of the organism is essential. The BioCyc/EcoCyc web portal offers valuable insights into *E. coli*, and reviewing relevant implementation details is also advisable. Coupling constraints, which couple reaction flux to protein requirements, are critical to a GECKO model. As previously outlined in the tutorial, these coupling constraints ($\\text{CC}_{ij}$) are functions of protein copy number in the catalyzing enzyme ($\\text{stoic}_{ij}$), protein molecular weight ($\\text{MW}_i$), turnover number ($\\text{kcat}_{ij}$), number of active sites ($\\text{n_AS}$), and average enzyme saturation ($\\text{avg_enz_sat}$).\n",
    "\n",
    "$\\text{CC}_{ij} = \\frac{\\text{stoic}_{ij} \\cdot \\text{MW}_i } {(\\text{kcat}_{ij} \\cdot \\text{n_AS}_{ij} \\cdot \\text{avg_enz_sat})}$\n",
    "\n",
    "The f2xba method involves the extraction of protein requirements from the gene-product associations configured in the foundational GEM. The average enzyme saturation, applicable to all enzymes, is configured in the ECM configuration file. The protein molecular weight is extracted from UniProt or NCBI online data. In the initial model, protein copy numbers were extracted from BioCyc online data, and the number of active sites were configured based on heuristics. The turnover numbers were set to default values, as specified in the XBA configuration file.\n",
    "\n",
    "It is important to note that lowering the values of coupling constraint will reduce the amount of protein required to catalyze a specific reaction; alternatively, reaction flux will increase for a given protein investment. An increase in the turnover number will reduce the coupling. Within the scope of this study, transporters are delineated as reactions comprising reactants and products residing in multiple cellular compartments. Turnover numbers for several transporters have been measured and can be extracted from Brenda (https://www.brenda-enzymes.org) (Chang et al., 2021) or BioCyc with references to relevant publications. Given the inherent challenges in measuring turnover numbers for membrane-based transporters, we have established a range of default turnover number values for transporters, contingent on their classification as porins, ABC transporters, and so forth. This approach serves to enhance the accuracy of protein predictions.\n",
    "\n",
    "While the adjustment of the turnover number appears to be a viable proposition, the modification of the copy number of proteins in multimeric enzymes does not initially appear feasible. However, there are instances where this adjustment could be proposed, particularly in the context of transporters. For instance, in the initial GECKO model, an ABC transporter consists of a transporter component with a fixed stoichiometry of the participating proteins, and one periplasmic binding protein, which is not tightly coupled. Proteomics suggests that the ratio between periplasmic binding proteins and related transporter components is much greater than 1.0. By adjusting the copy numbers of periplasmic binding proteins, we can improve protein predictions. ACP, flavodoxin, or glutaredoxin are some of the coenzymes implemented in the iML1515 GEM model. In instances where these coenzymes are present in a reaction as either a reactant or a product, they are also incorporated into the gene-product association for that specific reaction. For the improved GECKO model, the coenzymes will be removed from the gene-product association when they do not function as a catalyst. This approach results in a reduction of the necessary genes required in the GECKO model compared to the FBA model.\n",
    "\n",
    "The subsequent parameter modifications were derived iteratively. This process entailed modifying parameters, creating versions of GECKO models, analyzing optimization results, identifying areas for improvement, and reviewing relevant literature.\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\n",
    "\n",
    "In the initial step of the present tutorial and the subsequent tutorials, the environment is established for model creation and optimization. The necessary imports are executed, the model names are defined, media conditions are created, and proteomics data is loaded. Subsequently, the configuration data of a previous model, hereafter referred to as the `baseline_model`, is extended to create an improved or more realistic `target_model`."
   ]
  },
  {
   "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 time\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_default_GECKO'\n",
    "target_model = 'iML1515_modified_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: Export kcats values and enzyme composition\n",
    "\n",
    "The XBA configuration data employed in the creation of the initial GECKO model is updated, and gene products are removed via the table designated as `remove gps`. This includes structural proteins (G_b0957), proteins that are not expressed under normal conditions (G_b1319), and coenzymes that do not act as catalysts. Subsequent to the aforementioned modifications, an in-memory instance of XbaModel is generated, and it is utilized to export two tables, which are subsequently stored in the XLSX format.\n",
    "\n",
    "The first table encompasses the turnover numbers, while the second table comprises the enzyme composition. The table with turnover numbers is indexed by the reaction identifier (`key`). The `rid` column provides the identifier of the net reaction, respectively the reaction identifier used in the foundational GEM. The direction of the reaction with respect to the net reaction is specified by the `dirxn` column, which can be forward (1) or reverse (-1). The catalyzing enzyme identifier (`enzyme`), the turnover number `kcat_per_s` in units of $s^{-1}$, and other informative columns are also included. The table that enumerates enzyme compositions is indexed by the enzyme identifier (`eid`), and the composition itself specifies the required genes and protein copy numbers. Enzymes can have one or more active sites, and enzymes can be named (`name`). The table also contains optional information columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 table(s) with parameters loaded from data/iML1515_default_GECKO_xba_parameters.xlsx (Thu Nov 20 18:48:27 2025)\n",
      "2 table(s) with parameters written to  data/export_xba_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Create updated XBA parameter file\n",
    "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "\n",
    "data = [['G_b0957', 'ompA (30.4 mg/gP) used in R_H2Otex has mainly a structural'], \n",
    "        ['G_b1319', 'ompG used in R_H2Otex, not expressed under lab conditions'],\n",
    "        # remove proteins used stoichiometrically in iML1515 (add to RBA model as target)\n",
    "        ['G_b1094', 'acp, Acyl carrier protein, is non-catalytic'],\n",
    "        ['G_b2582', 'trxC, Thioredoxin-2, is non-catalytic'],\n",
    "        ['G_b3781', 'trxA, Thioredoxin-1, is non-catalytic'],\n",
    "        ['G_b2529', 'iscU, scaffold protein for [Fe-S] cluster assembly, is non-catalytic'],\n",
    "        ['G_b1679', 'sufE, sulfur carrier protein SufE, is non-catalytic'],\n",
    "        ['G_b1681', 'sufD, FeS cluster assembly protein sufD, is non-catalytic'],\n",
    "        ['G_b1682', 'sufC, Probable ATP-dependent transporter SufC, is non-catalytic'],\n",
    "        ['G_b1683', 'sufB, FeS cluster assembly protein sufB, is non-catalytic'],\n",
    "        ['G_b3860', 'dsbA, Thiol:disulfide interchange protein DsbA, used stoichiometrically'],\n",
    "        ['G_b2893', 'dsbC, Thiol:disulfide interchange protein DsbC, used stoichiometrically'],\n",
    "        ['G_b4136', 'dsbD, Thiol:disulfide interchange protein DsbD, used stoichiometrically'],\n",
    "        ['G_b0604', 'dsbG, Thiol:disulfide interchange protein DsbB, used stoichiometrically'],\n",
    "        ['G_b1677', 'lpp, Major outer membrane lipoprotein Lpp, used stoichiometrically'],\n",
    "        ['G_b0684', 'fldA, Flavodoxin-1, used stoichiometrically'],\n",
    "        ['G_b2895', 'fldb, Flavodoxin-2, used stoichiometrically'],\n",
    "        ['G_b0849', 'grxA, Glutaredoxin-1, used stoichiometrically'],\n",
    "        ['G_b1064', 'grxB, Glutaredoxin-2, used stoichiometrically'],\n",
    "        ['G_b3610', 'grxC, Glutaredoxin-3, used stoichiometrically'],\n",
    "        ['G_b1654', 'grxD, Glutaredoxin-4, used stoichiometrically'],\n",
    "       ]\n",
    "\n",
    "df = pd.DataFrame(data, columns=['gp_id', 'notes']).set_index('gp_id')\n",
    "xba_params['remove_gps'] = pd.concat((xba_params['remove_gps'], df))\n",
    "\n",
    "write_parameter_file(os.path.join('data', f'export_xba_parameters.xlsx'), xba_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading: SBML_models/iML1515.xml (last modified: Thu Dec  5 10:03:46 2024)\n",
      "2 table(s) with parameters loaded from data/export_xba_parameters.xlsx (Thu Nov 20 18:52:31 2025)\n",
      "  22 gene product(s) removed from reactions (1494 gene products remaining)\n",
      "extracting UniProt protein data from data/uniprot_organism_83333.tsv\n",
      "1494 proteins created\n",
      "1203 enzymes added with default stoichiometry\n",
      "3855 enzymes extracted from Biocyc\n",
      "1197 enzyme compositions updated from Biocyc Enzyme data\n",
      "2221 reactions catalyzed by 1203 enzymes\n",
      "default kcat values configured for ['metabolic', 'transporter'] reactions\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",
      "5357 reaction kcat values exported to data/iML1515_default_GECKO_kcats.xlsx\n",
      "1203 enzyme compositions exported to data/iML1515_default_GECKO_enzyme_composition.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Extract kcat and enzyme composition\n",
    "xba_model = XbaModel(os.path.join('SBML_models', f'{fba_model}.xml'))\n",
    "xba_model.configure(os.path.join('data', f'export_xba_parameters.xlsx'))\n",
    "\n",
    "xba_model.export_kcats(os.path.join('data', f'{baseline_model}_kcats.xlsx'))\n",
    "xba_model.export_enz_composition(os.path.join('data', f'{baseline_model}_enzyme_composition.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_default_GECKO_kcats.xlsx (Thu Nov 20 18:52:42 2025)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rid</th>\n",
       "      <th>dirxn</th>\n",
       "      <th>enzyme</th>\n",
       "      <th>kcat_per_s</th>\n",
       "      <th>notes</th>\n",
       "      <th>info_active_sites</th>\n",
       "      <th>info_ecns</th>\n",
       "      <th>info_type</th>\n",
       "      <th>info_genes</th>\n",
       "      <th>info_name</th>\n",
       "      <th>info_reaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R_CYTDK2</th>\n",
       "      <td>R_CYTDK2</td>\n",
       "      <td>1</td>\n",
       "      <td>enz_b2066</td>\n",
       "      <td>12.5</td>\n",
       "      <td>XBA model export</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7.1.213, 2.7.1.48</td>\n",
       "      <td>metabolic</td>\n",
       "      <td>b2066</td>\n",
       "      <td>Cytidine kinase (GTP)</td>\n",
       "      <td>M_cytd_c + M_gtp_c =&gt; M_cmp_c + M_gdp_c + M_h_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R_XPPT</th>\n",
       "      <td>R_XPPT</td>\n",
       "      <td>1</td>\n",
       "      <td>enz_b0238</td>\n",
       "      <td>12.5</td>\n",
       "      <td>XBA model export</td>\n",
       "      <td>4</td>\n",
       "      <td>2.4.2.22, 2.4.2.8</td>\n",
       "      <td>metabolic</td>\n",
       "      <td>b0238</td>\n",
       "      <td>Xanthine phosphoribosyltransferase</td>\n",
       "      <td>M_prpp_c + M_xan_c =&gt; M_ppi_c + M_xmp_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R_HXPRT_iso1</th>\n",
       "      <td>R_HXPRT</td>\n",
       "      <td>1</td>\n",
       "      <td>enz_b0125</td>\n",
       "      <td>12.5</td>\n",
       "      <td>XBA model export</td>\n",
       "      <td>4</td>\n",
       "      <td>2.4.2.8</td>\n",
       "      <td>metabolic</td>\n",
       "      <td>b0125</td>\n",
       "      <td>Hypoxanthine phosphoribosyltransferase (Hypoxa...</td>\n",
       "      <td>M_hxan_c + M_prpp_c =&gt; M_imp_c + M_ppi_c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   rid  dirxn     enzyme  kcat_per_s             notes  \\\n",
       "key                                                                      \n",
       "R_CYTDK2      R_CYTDK2      1  enz_b2066        12.5  XBA model export   \n",
       "R_XPPT          R_XPPT      1  enz_b0238        12.5  XBA model export   \n",
       "R_HXPRT_iso1   R_HXPRT      1  enz_b0125        12.5  XBA model export   \n",
       "\n",
       "              info_active_sites            info_ecns  info_type info_genes  \\\n",
       "key                                                                          \n",
       "R_CYTDK2                      1  2.7.1.213, 2.7.1.48  metabolic      b2066   \n",
       "R_XPPT                        4    2.4.2.22, 2.4.2.8  metabolic      b0238   \n",
       "R_HXPRT_iso1                  4              2.4.2.8  metabolic      b0125   \n",
       "\n",
       "                                                      info_name  \\\n",
       "key                                                               \n",
       "R_CYTDK2                                  Cytidine kinase (GTP)   \n",
       "R_XPPT                       Xanthine phosphoribosyltransferase   \n",
       "R_HXPRT_iso1  Hypoxanthine phosphoribosyltransferase (Hypoxa...   \n",
       "\n",
       "                                                info_reaction  \n",
       "key                                                            \n",
       "R_CYTDK2      M_cytd_c + M_gtp_c => M_cmp_c + M_gdp_c + M_h_c  \n",
       "R_XPPT                M_prpp_c + M_xan_c => M_ppi_c + M_xmp_c  \n",
       "R_HXPRT_iso1         M_hxan_c + M_prpp_c => M_imp_c + M_ppi_c  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kcats file\n",
    "load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_default_GECKO_enzyme_composition.xlsx (Thu Nov 20 18:52:42 2025)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>composition</th>\n",
       "      <th>active_sites</th>\n",
       "      <th>info_mw_kDa</th>\n",
       "      <th>info_n_reactions</th>\n",
       "      <th>info_sample_rid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eid</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>enz_b1395</th>\n",
       "      <td>NaN</td>\n",
       "      <td>gene=b1395, stoic=1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.733</td>\n",
       "      <td>1</td>\n",
       "      <td>R_HADPCOADH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enz_b1007_b1012</th>\n",
       "      <td>enz_b1007_b1012</td>\n",
       "      <td>gene=b1007, stoic=1.0; gene=b1012, stoic=1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.968</td>\n",
       "      <td>1</td>\n",
       "      <td>R_PYROX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enz_b0784_b0785</th>\n",
       "      <td>molybdopterin synthase</td>\n",
       "      <td>gene=b0784, stoic=2.0; gene=b0785, stoic=2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>51.478</td>\n",
       "      <td>1</td>\n",
       "      <td>R_MPTS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name  \\\n",
       "eid                                       \n",
       "enz_b1395                           NaN   \n",
       "enz_b1007_b1012         enz_b1007_b1012   \n",
       "enz_b0784_b0785  molybdopterin synthase   \n",
       "\n",
       "                                                  composition  active_sites  \\\n",
       "eid                                                                           \n",
       "enz_b1395                               gene=b1395, stoic=1.0             1   \n",
       "enz_b1007_b1012  gene=b1007, stoic=1.0; gene=b1012, stoic=1.0             1   \n",
       "enz_b0784_b0785  gene=b0784, stoic=2.0; gene=b0785, stoic=2.0             2   \n",
       "\n",
       "                 info_mw_kDa  info_n_reactions info_sample_rid  \n",
       "eid                                                             \n",
       "enz_b1395             51.733                 1    R_HADPCOADH3  \n",
       "enz_b1007_b1012       59.968                 1         R_PYROX  \n",
       "enz_b0784_b0785       51.478                 1          R_MPTS  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# enzyme composition file\n",
    "load_parameter_file(os.path.join('data', f'{baseline_model}_enzyme_composition.xlsx'))['enzymes'].tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Modify turnover numbers and enzyme composition\n",
    "\n",
    "The data tables that were exported in the preceding step can be updated by employing a spreadsheet editor. However, this will not be the method employed in this tutorial. Instead, the tables will be updated using Python code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1: modify turnover numbers\n",
    "\n",
    "#### measured values\n",
    "\n",
    "In instances where turnover numbers have been measured, they will selectively be used to replace the default values employed in the initial GECKO model.\n",
    "\n",
    "#### outer membrane pores\n",
    "\n",
    "The FBA model of iML1515 contains 283 transport reactions across the outer membrane that utilize any of four isoenzymes, namely the gene products of b0241/phoE, b0929/ompF, b1377/ompN, and b2215/ompC. Of these, OmpN, with one copy of ompN, has a molecular weight of 41 kD and is the ‘cheapest’ enzyme. The three other outer membrane pores are trimeric with molecular weights of approximately 117 kD (PhoE), 118 kD (OmpF), and 121 kD (OmpC). Two proteins, b0241/phoE and b1377/ompN, were not included in the proteomics data due to their low expression levels under minimal and rich media conditions, as reported by BioCyc. In contrast, b0929/ompF and b2215/ompC were identified as the top 20 most abundant protein masses in the proteomics data.\n",
    "\n",
    "The GECKO formalism will invariably select the ‘cheapest’ iso-enzyme for a given reaction, i.e., b1377/ompN. By reducing the turnover numbers of all (sub-)reactions utilizing b1377 or b0241, we can redirect traffic to the other two pores, b0929 and b2215. The assignment of distinct turnover numbers to these (sub-)reactions, contingent on the charge state of the transported metabolite, could facilitate a balanced utilization of the two proteins.\n",
    "\n",
    "#### transporter types\n",
    "\n",
    "The model encompasses diverse transporter types, including ABC transporters and aquaporins, for which distinct default turnover values have been established.\n",
    "default kcat values.\n",
    "\n",
    "#### Python code\n",
    "\n",
    "It is not necessary to comprehend the Python code presented below in its entirety."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_default_GECKO_kcats.xlsx (Thu Nov 20 18:52:42 2025)\n",
      "5357 kcat records in total\n",
      "3009 kcat values updated\n",
      "       11 kcat records set to specific values\n",
      "      543 kcat records set to   25.0 s-1 for general-tx transporter\n",
      "       12 kcat records set to 5000.0 s-1 for aquaporin transporter\n",
      "       27 kcat records set to  100.0 s-1 for pts transporter\n",
      "      151 kcat records set to   50.0 s-1 for abc transporter\n",
      "        7 kcat records set to    2.5 s-1 for low_flux transporter\n",
      "      562 kcat records set to   70.0 s-1 for phoE transporter\n",
      "      564 kcat records set to   30.0 s-1 for ompN transporter\n",
      "      327 kcat records set to  275.0 s-1 for ompC (ionic) transporter\n",
      "      239 kcat records set to  385.0 s-1 for ompC (neutral) transporter\n",
      "      327 kcat records set to  275.0 s-1 for ompF (ionic) transporter\n",
      "      239 kcat records set to  370.0 s-1 for ompF (neutral) transporter\n",
      "1 table(s) with parameters written to  data/iML1515_modified_GECKO_kcats.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Load original kcats file and modify selected transporter kcat values\n",
    "specific_tx_kcats = {'R_NADH16pp': [600.0, 'manual - E. coli, pubmed 672041'], \n",
    "                     'R_NADH17pp': [600.0, 'manual - E. coli, pubmed 672041'],\n",
    "                     'R_NADH18pp': [600.0, 'manual - E. coli, pubmed 672041'],\n",
    "                     'R_CYTBO3_4pp': [320.0, 'manual - E. coli, pubmed 8382081'], \n",
    "                     'R_ATPS4rpp_iso1': [285.0, 'manual - E. coli, pubmed 19411254'],\n",
    "                     'R_ATPS4rpp_iso2': [285.0, 'manual - E. coli, pubmed 19411254'], \n",
    "                     'R_FDH4pp_iso2': [563.0, 'manual - E. coli, pubmed 1099093'], \n",
    "                     'R_FDH5pp_iso2': [563.0, 'manual - E. coli, pubmed 1099093'],\n",
    "                     'R_SUCDi': [85.0, 'manual - E. coli, pubmed 11803023'],\n",
    "                     'R_SULabcpp_iso2': [100.0, 'manual - adjusted to proteomics'],\n",
    "                     'R_SO4t2pp': [0.1, 'manual - adjusted to proteomics'],}\n",
    "rids_low_tx_flux = {'R_MG2tpp', 'R_MG2uabcpp','R_NI2tpp', 'R_NI2uabcpp', 'R_COBALT2tpp'}\n",
    "enz2omp = {'enz_b0241': 'phoE', 'enz_b0929': 'ompF', 'enz_b1377': 'ompN', 'enz_b2215': 'ompC'}\n",
    "default_kcats = {'general-tx': 25.0, 'aquaporin': 5000.0, 'pts': 100.0, 'abc': 50.0, 'low_flux': 2.5, \n",
    "                 'phoE': 70.0, 'ompN': 30.0, \n",
    "                 'ompC (ionic)': 275.0, 'ompC (neutral)': 385.0, \n",
    "                 'ompF (ionic)': 275.0, 'ompF (neutral)': 370.0 }\n",
    "\n",
    "# Create updated kcats file\n",
    "df_kcats = load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats']\n",
    "\n",
    "# Update selected kcats for transporters\n",
    "updates = {tx_type: 0 for tx_type in default_kcats.keys()} \n",
    "n_specific_kcats = 0\n",
    "for key, row in df_kcats.iterrows():\n",
    "    if key in specific_tx_kcats:\n",
    "        df_kcats.at[key, 'kcat_per_s'] = specific_tx_kcats[key][0]\n",
    "        df_kcats.at[key, 'notes'] = specific_tx_kcats[key][1]\n",
    "        n_specific_kcats += 1   \n",
    "    elif row['rid'] in rids_low_tx_flux:\n",
    "        df_kcats.at[key, 'kcat_per_s'] = default_kcats['low_flux']\n",
    "        df_kcats.at[key, 'notes'] = 'manual low flux reactions'\n",
    "        updates['low_flux'] += 1   \n",
    "    elif row['info_type'] == 'transporter':\n",
    "        r = xba_model.reactions[row['rid']]\n",
    "        reactants = list(r.reactants)\n",
    "        if len(reactants) == 1 and re.match('M_h2o_', reactants[0]):\n",
    "            tx_type = 'aquaporin'\n",
    "        elif 'pts' in row['rid']:\n",
    "            tx_type = 'pts'\n",
    "        elif 'abc' in row['rid']:\n",
    "            tx_type = 'abc'\n",
    "        elif r.compartment == 'e-p':\n",
    "            tx_type = enz2omp.get(row['enzyme'], 'general-tx')\n",
    "            species_charge = xba_model.species[reactants[0]].charge\n",
    "            if tx_type == 'ompF':\n",
    "                tx_type = 'ompF (neutral)' if species_charge == 0 else 'ompF (ionic)'\n",
    "            elif tx_type == 'ompC':\n",
    "                    tx_type = 'ompC (neutral)' if species_charge == 0 else 'ompC (ionic)'    \n",
    "        else: \n",
    "            tx_type = 'general-tx'    \n",
    "        df_kcats.at[key, 'kcat_per_s'] = default_kcats[tx_type]\n",
    "        df_kcats.at[key, 'notes'] = f'default for {tx_type} transporter'\n",
    "        updates[tx_type] += 1\n",
    "\n",
    "print(f'{len(df_kcats):4d} kcat records in total')\n",
    "print(f'{sum(updates.values()) + n_specific_kcats:4d} kcat values updated')\n",
    "print(f'{n_specific_kcats:9d} kcat records set to specific values')\n",
    "for tx_type, count in updates.items():\n",
    "    print(f'{count:9d} kcat records set to {default_kcats[tx_type]:6.1f} s-1 for {tx_type} transporter')\n",
    "\n",
    "# Write updated kcats to file\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_kcats.xlsx'), {'kcats': df_kcats})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2: modify enzyme compositions\n",
    "\n",
    "According to proteomics data, the copy number of loosely coupled proteins required in ATP binding cassette (ABC) and phosphotransferase system (PTS) transport processes should be modified. Periplasmic binding proteins are identified by name or gene locus. The loosely attached components of PTS transporters are identified by gene locus. The number of active sites is modified for large multienzyme complexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_default_GECKO_enzyme_composition.xlsx (Thu Nov 20 18:52:42 2025)\n",
      "1203 enzymes in total\n",
      "  62 enzyme compositions manually updated\n",
      "1 table(s) with parameters written to  data/iML1515_enzyme_composition_updated.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Load enzyme composition template file\n",
    "df_enz_comp = load_parameter_file(os.path.join('data', f'{baseline_model}_enzyme_composition.xlsx'))['enzymes']\n",
    "    \n",
    "# Collect genes related to periplasmic binding proteins. Accessing protein data in xba_model.\n",
    "binding_proteins = {'b1857', 'b0698', 'b2177'}\n",
    "for uid, p in xba_model.proteins.items():\n",
    "    if re.match('.*binding.*protein', p.name) and 'ATP-binding' not in p.name:\n",
    "        binding_proteins.add(p.locus)\n",
    "\n",
    "# Update composition of selected enzymes\n",
    "pts_update = {'b1101':1.0, 'b1817': 7.0, 'b2415': 9.0, 'b2416': 4.0, 'b2417': 12.0}\n",
    "enz_modified = set()\n",
    "for eid, row in df_enz_comp.iterrows():\n",
    "        composition = []\n",
    "        for kv in row['composition'].split(';'):\n",
    "            gene = re.match(r'.*gene=(b\\d{4})', kv).group(1)\n",
    "            stoic = float(re.match(r'.*stoic=([0-9\\.])', kv).group(1))\n",
    "            if 'abc' in row['info_sample_rid'] and gene in binding_proteins:\n",
    "                stoic = 50.0\n",
    "                enz_modified.add(eid)\n",
    "            elif gene in pts_update:\n",
    "                stoic = pts_update[gene]\n",
    "                enz_modified.add(eid)\n",
    "            composition.append(f'gene={gene}, stoic={stoic}')\n",
    "        if eid in enz_modified:\n",
    "            df_enz_comp.at[eid, 'composition'] = '; '.join(composition)\n",
    "            df_enz_comp.at[eid, 'notes'] = 'enzyme composition manually updated'\n",
    "print(f'{len(df_enz_comp):4d} enzymes in total')\n",
    "print(f'{len(enz_modified):4d} enzyme compositions manually updated')\n",
    "\n",
    "# update number of active sites in PDH and AGKDH\n",
    "df_enz_comp.at['enz_b0114_b0115_b0116', 'active_sites'] = 24  # PDH\n",
    "df_enz_comp.at['enz_b0116_b0726_b0727', 'active_sites'] = 24  # AKGDH\n",
    "\n",
    "write_parameter_file(os.path.join('data', f'{fba_model}_enzyme_composition_updated.xlsx'), \n",
    "                     {'enzymes': df_enz_comp})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Create XBA and ECM configuration files\n",
    "\n",
    "The creation of XBA and ECM configuration files for the target model is achieved through the extension of the configuration files from the baseline model. Rather than utilizing the ECM configuration parameter `pm2totpm_val_or_paxdb` to define the mass fraction of modeled protein to total protein, an alternative approach is explored, employing a protein abundance dataset for *E. coli* that is downloaded from PaxDB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 download PaxDb file\n",
    "\n",
    "In order to proceed, it is necessary to navigate to the 'PaxDb: Protein Abundance Database web portal' (https://pax-db.org) and select the species '*Escherichia coli* str. K-12 substr. MG1655'. Subsequently, the dataset for '*E. coli* - Whole organism (Integrated)' (511145-WHOLE_ORGANISM-integrated.txt) should be downloaded. The downloaded file should then be stored in the `.\\data` folder.\n",
    "\n",
    "#### PaxDb files for other organisms:\n",
    "\n",
    "f2xba anticipates that the gene product references contained within the protein abundance dataset correspond to the gene labels employed in the model. In instances where PaxDb lacks a protein abundance dataset for a particular organism, it is imperative to ascertain the proportion of modeled protein mass to total protein mass and allocate this value to the ECM configuration parameter designated as `pm2totpm_val_or_paxdb`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 update XBA and ECM configuration files\n",
    "\n",
    "#### XBA configuration\n",
    "\n",
    "The XBA configuration file has been updated, with the following changes: Within the `generals` table, the `biocyc_org_prefix` parameter has been substituted with the `enzyme_comp_fname` parameter, thereby referencing the updated enzyme composition file. Additionally, the `kcats_fname` parameter has been incorporated, referencing the updated turnover numbers file. The new table, `modify_attributes`, is used to modify specific model parameters. The spontaneous reaction 'R_MG2t3_2pp' is blocked to enforce active transport through alternative reactions, and the reaction 'R_GLCDpp' is blocked since it requires the cofactor pyrroloquinoline quinone (PQQ), which is not part of the specified medium conditions.\n",
    "\n",
    "#### ECM configuration\n",
    "\n",
    "The ECM configuration involves the update of the parameters `pm2totpm_val_or_paxdb`, now referencing the PacDb file, and the adjustment of the `avg_enz_sat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 table(s) with parameters loaded from data/export_xba_parameters.xlsx (Thu Nov 20 18:52:31 2025)\n",
      "3 table(s) with parameters written to  data/iML1515_modified_GECKO_xba_parameters.xlsx\n",
      "1 table(s) with parameters loaded from data/iML1515_default_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:48:29 2025)\n",
      "1 table(s) with parameters written to  data/iML1515_modified_GECKO_ecm_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Update XBA parameter file\n",
    "xba_params = load_parameter_file(os.path.join('data', f'export_xba_parameters.xlsx'))\n",
    "\n",
    "xba_params['general'].drop(index='biocyc_org_prefix', inplace=True)\n",
    "data = [['kcats_fname', os.path.join('data', f'{target_model}_kcats.xlsx')], \n",
    "        ['enzyme_comp_fname', os.path.join('data', f'{fba_model}_enzyme_composition_updated.xlsx')]]\n",
    "df = pd.DataFrame(data, columns=['parameter', 'value']).set_index('parameter')\n",
    "xba_params['general'] = pd.concat((xba_params['general'], df))\n",
    "\n",
    "data = [['R_GLCDpp', 'reaction', 'fbc_upper_bound', 'cobra_0_bound',\n",
    "         'block, as both isoenzymes gcd/b0124 and ylil/b0837 require PQQ as cofactor'],\n",
    "        ['R_MG2t3_2pp', 'reaction', 'fbc_upper_bound', 'cobra_0_bound',\n",
    "         'block, as this reaction has no enzyme attached'],]\n",
    "cols=['id', 'component', 'attribute', 'value', 'notes']\n",
    "xba_params['modify_attributes'] = pd.DataFrame(data, columns=cols).set_index('id')\n",
    "\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n",
    "\n",
    "# Update ECM parameter file\n",
    "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'),['general'])\n",
    "\n",
    "ecm_params['general'].at['avg_enz_sat', 'value'] = 0.93\n",
    "paxdb_fname = os.path.join('data', '511145-WHOLE_ORGANISM-integrated.txt')\n",
    "ecm_params['general'].at['pm2totpm_val_or_paxdb', 'value'] = paxdb_fname\n",
    "\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Create GECKO model\n",
    "\n",
    "This code section bears a strong resemblance to the preceding tutorial, with two notable exceptions. Firstly, validation of the model is no longer performed. Secondly, the model is not exported as a spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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_ecm_parameters.xlsx (Thu Nov 20 18:52:54 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_modified_GECKO.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "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 4: Load and optimize GECKO model (cobrapy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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_modified_GECKO.xml (Thu Nov 20 18:53:18 2025)\n",
      "total modeled protein: 306.59 mg/gDW, average saturation level: 0.93\n",
      "Duration: 18.6 s\n"
     ]
    }
   ],
   "source": [
    "# Load model using cobrapy\n",
    "start = time.time()\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "ecm = cobra.io.read_sbml_model(fname)\n",
    "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n",
    "\n",
    "# Load SBML model with EcmOptimization\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}')\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.404 h-1 vs. exp 0.290, diff:  0.114\n",
      "Glycerol                 : pred gr: 0.702 h-1 vs. exp 0.470, diff:  0.232\n",
      "Fructose                 : pred gr: 0.664 h-1 vs. exp 0.540, diff:  0.124\n",
      "L-Malate                 : pred gr: 0.735 h-1 vs. exp 0.550, diff:  0.185\n",
      "Glucose                  : pred gr: 0.661 h-1 vs. exp 0.660, diff:  0.001\n",
      "Glucose 6-Phosphate      : pred gr: 0.695 h-1 vs. exp 0.780, diff: -0.085\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 = 0.1066, p = 3.05e-26 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.0315, p = 1.61e-08 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.1877, p = 5.65e-47 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.1360, p = 1.55e-33 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.2412, p = 3.63e-20 ( 309 proteins log scale)\n",
      "Glycerol                 : r² = 0.1787, p = 5.86e-15 ( 312 proteins log scale)\n",
      "Fructose                 : r² = 0.2014, p = 8.11e-17 ( 311 proteins log scale)\n",
      "Glucose                  : r² = 0.2016, p = 2.22e-17 ( 322 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. 507.8 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 392.2 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs. 115.5 mg/gP predicted\n",
      "      495 proteins not measured vs.  30.1 mg/gP predicted\n",
      "           495 actual  proteins      30.1 mg/gP predicted\n",
      "total           : r² = 0.1360, p = 1.55e-33 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.1035, p = 8.34e-20 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.4379, p = 3.11e-31 ( 237 proteins lin scale)\n",
      "total           : r² = 0.2016, p = 2.22e-17 ( 322 proteins log scale)\n",
      " metabolic      : r² = 0.2199, p = 2.83e-16 ( 272 proteins log scale)\n",
      " transport      : r² = 0.2573, p = 1.70e-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",
      "Duration: 3.9 s\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using cobrapy and analyze results\n",
    "start = time.time()\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    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))\n",
    "print(f'Duration: {time.time()-start:.1f} s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Track progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Nov 20 18:50:20 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",
       "  </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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "number = 2\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",
    "A few parameters, primarily those associated with transporters, were modified, leading to substantial improvements in the predicted protein levels. The modifications were made based on a comprehensive understanding of the organism and the manner in which the reaction network is modeled. This included adjustments to transporter turnover numbers, the enzyme composition of transporters, and the number of active sites for large multienzyme complexes. Additionally, coenzymes that did not function as catalysts were removed from the modeled gene products. It should be noted that metabolic reactions continue to be configured with the default turnover number. In the subsequent tutorial, we will utilize an AI-based tool to predict the turnover numbers of all metabolic reactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## (Alternative) gurobipy - model optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_modified_GECKO.xml (Thu Nov 20 18:53:18 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: 0.93\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.404 h-1 vs. exp 0.290, diff:  0.114\n",
      "Glycerol                 : pred gr: 0.702 h-1 vs. exp 0.470, diff:  0.232\n",
      "Fructose                 : pred gr: 0.664 h-1 vs. exp 0.540, diff:  0.124\n",
      "L-Malate                 : pred gr: 0.735 h-1 vs. exp 0.550, diff:  0.185\n",
      "Glucose                  : pred gr: 0.661 h-1 vs. exp 0.660, diff:  0.001\n",
      "Glucose 6-Phosphate      : pred gr: 0.695 h-1 vs. exp 0.780, diff: -0.085\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 = 0.1066, p = 3.05e-26 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.0315, p = 1.61e-08 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.1877, p = 5.65e-47 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.1360, p = 1.55e-33 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.2412, p = 3.63e-20 ( 309 proteins log scale)\n",
      "Glycerol                 : r² = 0.1787, p = 5.86e-15 ( 312 proteins log scale)\n",
      "Fructose                 : r² = 0.2014, p = 8.11e-17 ( 311 proteins log scale)\n",
      "Glucose                  : r² = 0.2016, p = 2.22e-17 ( 322 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. 507.8 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 392.2 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs. 115.5 mg/gP predicted\n",
      "      495 proteins not measured vs.  30.1 mg/gP predicted\n",
      "           495 actual  proteins      30.1 mg/gP predicted\n",
      "total           : r² = 0.1360, p = 1.55e-33 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.1035, p = 8.34e-20 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.4379, p = 3.11e-31 ( 237 proteins lin scale)\n",
      "total           : r² = 0.2016, p = 2.22e-17 ( 322 proteins log scale)\n",
      " metabolic      : r² = 0.2199, p = 2.83e-16 ( 272 proteins log scale)\n",
      " transport      : r² = 0.2573, p = 1.70e-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",
    "- Chang, A., Jeske, L., Ulbrich, S., Hofmann, J., Koblitz, J., Schomburg, I., Neumann-Schaal, M., Jahn, D., & Schomburg, D. (2021). BRENDA, the ELIXIR core data resource in 2021: new developments and "
   ]
  },
  {
   "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
}