{ "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 ($CC_{ij}$) are functions of protein copy number in the catalyzing enzyme ($stoic_{ij}$), protein molecular weight ($MW_i$), turnover number ($kcat_{ij}$), number of active sites ($n\\_AS$), and average enzyme saturation ($avg\\_enz\\_sat$).\n", "\n", "\\begin{equation}\n", "CC_{ij} = \\frac{stoic_{ij} \\cdot MW_i} {(kcat_{ij} \\cdot n\\_AS_{ij} \\cdot avg\\_enz\\_sat)}\n", "\\end{equation}\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 Feb 12 15:14:36 2026)\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 Feb 12 15:16:50 2026)\n", " 22 gene product(s) removed from reactions (1494 gene products remaining)\n", "3 reactions with atom imbalances, e.g. [R_PUACGAMS, R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.atom_imbalances.\n", "2 reactions with charge imbalances, e.g. [R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.charge_imbalances.\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 Feb 12 15:17:00 2026)\n" ] }, { "data": { "text/html": [ "
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riddirxnenzymekcat_per_snotesinfo_active_sitesinfo_ecnsinfo_typeinfo_genesinfo_nameinfo_reaction
key
R_CYTDK2R_CYTDK21enz_b206612.5XBA model export12.7.1.213, 2.7.1.48metabolicb2066Cytidine kinase (GTP)M_cytd_c + M_gtp_c => M_cmp_c + M_gdp_c + M_h_c
R_XPPTR_XPPT1enz_b023812.5XBA model export42.4.2.22, 2.4.2.8metabolicb0238Xanthine phosphoribosyltransferaseM_prpp_c + M_xan_c => M_ppi_c + M_xmp_c
R_HXPRT_iso1R_HXPRT1enz_b012512.5XBA model export42.4.2.8metabolicb0125Hypoxanthine phosphoribosyltransferase (Hypoxa...M_hxan_c + M_prpp_c => M_imp_c + M_ppi_c
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" ], "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 Feb 12 15:17:01 2026)\n" ] }, { "data": { "text/html": [ "
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namecompositionactive_sitesinfo_mw_kDainfo_n_reactionsinfo_sample_rid
eid
enz_b1395NaNgene=b1395, stoic=1.0151.7331R_HADPCOADH3
enz_b1007_b1012enz_b1007_b1012gene=b1007, stoic=1.0; gene=b1012, stoic=1.0159.9681R_PYROX
enz_b0784_b0785molybdopterin synthasegene=b0784, stoic=2.0; gene=b0785, stoic=2.0251.4781R_MPTS
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" ], "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 Feb 12 15:17:00 2026)\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 Feb 12 15:17:01 2026)\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 Feb 12 15:16:50 2026)\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 Feb 12 15:14:40 2026)\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": 10, "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 Feb 12 15:17:15 2026)\n", " 22 gene product(s) removed from reactions (1494 gene products remaining)\n", " 2 attributes on reaction instances updated\n", "3 reactions with atom imbalances, e.g. [R_PUACGAMS, R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.atom_imbalances.\n", "2 reactions with charge imbalances, e.g. [R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.charge_imbalances.\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 Feb 12 15:17:11 2026)\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 Feb 12 15:17:08 2026)\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 Feb 12 15:17:15 2026)\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", "modelled 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 - modelled 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": 10, "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": 11, "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 Feb 12 15:18:13 2026)\n", "total modeled protein: 306.59 mg/gDW, average saturation level: 0.93\n", "Duration: 19.4 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": 12, "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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", 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" ] }, "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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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1 file(s) exported for \"Load reaction data\" into Escher maps\n", "Duration: 4.9 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": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Feb 12 15:17:23 2026)\n", "1 table(s) with parameters written to protein_predictions.xlsx\n" ] }, { "data": { "text/html": [ "
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2iML1515_modified_GECKO0.1359810.201613999322
\n", "
" ], "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": 13, "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": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SBML model loaded by sbmlxdf: SBML_models/iML1515_modified_GECKO.xml (Thu Feb 12 15:18:13 2026)\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": 15, "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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", 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" ] }, "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": [ "
" ] }, "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 " ] } ], "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 }