{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Tutorial: Create iML1515 GECKO with manually adjusted turnover numbers\n", "\n", "In the previous tutorial we created a GECKO model of iML1515 with TurNuP predicted turnover numbers for metabolic reactions in a few simple steps. The model predictions of protein concentrations are still not satisfactory, e.g. the model predicts high protein concentrations where the measured values are very low and vice versa. \n", "\n", "To achieve high prediction quality, a GECKO must be constructed using apparent turnover numbers that account for *in vivo* conditions such as enzyme regulation, enzyme saturation, and substrate channeling (Araiza-Olivera et al., 2013). TurNuP predicted turnover numbers can only give approximate values. The deviation between predicted and measured data is estimated to be a factor of 4.8. Furthermore, the quality of the training data is poor, consisting mainly of *in vitro* measurements using enzymatic assays. Measurement points are scarce, highly noisy, and acquired in a non-standardized manner using enzyme-specific assay protocols.\n", "\n", "Compared to enzymatic assays, quantitative proteomics is a fairly standardized procedure with almost complete protein coverage. This justifies the use of quantitative proteomics to adjust predicted turnover numbers to apparent turnover numbers, as required for the GECKO model. \n", "\n", "In this tutorial, we attempt to improve protein predictions by selectively adjusting turnover numbers. This requires a more detailed analysis of the optimization results combined with a certain amount of organism specific knowledge. We will analyze predicted flux distributions and try to redirect fluxes to physiologically relevant pathways. We will selectively compare predicted to measured protein levels and adjust GECKO coupling factors by modifying turnover numbers. Our focus will be on the major carbon metabolism. The methods and code snippets provided can be used to adjust other parts of the model as well.\n", "\n", "Peter Schubert, Heinrich-Heine University Duesseldorf, Institute for Computational Cell Biology (Prof. Dr. M. Lercher), January, 2025" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Initial setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6 minimal media conditions created for simulation\n", "2347 records of proteomics loaded from data/Ecoli_Schmidt_proteomics.xlsx\n", "2232 records with confidence level above 43.0\n" ] } ], "source": [ "# Required imports and model names\n", "import os\n", "import re\n", "import pandas as pd\n", "import cobra\n", "\n", "from f2xba import XbaModel, EcModel\n", "from f2xba import EcmOptimization, EcmResults\n", "from f2xba.utils.mapping_utils import load_parameter_file, write_parameter_file\n", "\n", "fba_model = 'iML1515'\n", "baseline_model = 'iML1515_predicted_GECKO'\n", "target_model = 'iML1515_manual_adjust_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: Analyze baseline GECKO model (cobrapy)\n", "\n", "First, we need to identify deficiencies in our baseline GECKO model. Using Escher maps, we can visualize predicted flux distributions. By applying organism specific knowledge and comparing predicted to measured protein levels, we can visually identify pathways and reactions where predictions appear to be incorrect. Our focus will be on the major carbon metabolism for the glucose medium, the chosen reference condition." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Set parameter Username\n", "Set parameter LicenseID to value 2731209\n", "Academic license - for non-commercial use only - expires 2026-10-31\n", "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_GECKO.xml (Thu Nov 20 18:56:05 2025)\n", "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.68\n", "1494 genes: (492) transporter, (1002) metabolic\n" ] } ], "source": [ "# Load model using cobrapy\n", "fname = os.path.join('SBML_models', f'{baseline_model}.xml')\n", "ecm = cobra.io.read_sbml_model(fname)\n", "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n", "\n", "eo = EcmOptimization(fname, ecm)\n", "sigma = eo.avg_enz_saturation\n", "all_genes = set(eo.m_dict['fbcGeneProducts']['label'].values)\n", "tx_genes, metab_genes = eo.get_tx_metab_genes()\n", "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')\n", "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.453 h-1 vs. exp 0.290, diff: 0.163\n", "Glycerol : pred gr: 0.609 h-1 vs. exp 0.470, diff: 0.139\n", "Fructose : pred gr: 0.625 h-1 vs. exp 0.540, diff: 0.085\n", "L-Malate : pred gr: 0.695 h-1 vs. exp 0.550, diff: 0.145\n", "Glucose : pred gr: 0.653 h-1 vs. exp 0.660, diff: -0.007\n", "Glucose 6-Phosphate : pred gr: 0.691 h-1 vs. exp 0.780, diff: -0.089\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "Acetate : r² = 0.0286, p = 7.71e-08 ( 999 proteins lin scale)\n", "Glycerol : r² = 0.0433, p = 3.17e-11 ( 999 proteins lin scale)\n", "Fructose : r² = 0.1063, p = 3.62e-26 ( 999 proteins lin scale)\n", "Glucose : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n", "Acetate : r² = 0.2768, p = 5.65e-23 ( 303 proteins log scale)\n", "Glycerol : r² = 0.2684, p = 1.77e-22 ( 307 proteins log scale)\n", "Fructose : r² = 0.2718, p = 3.60e-22 ( 298 proteins log scale)\n", "Glucose : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n", "\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 537.9 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 442.5 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 289.0 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 153.5 mg/gP predicted\n", " 495 proteins not measured vs. 95.4 mg/gP predicted\n", " 495 actual proteins 95.4 mg/gP predicted\n", "total : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n", " metabolic : r² = 0.0340, p = 2.92e-07 ( 762 proteins lin scale)\n", " transport : r² = 0.4967, p = 6.70e-37 ( 237 proteins lin scale)\n", "total : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n", " metabolic : r² = 0.3028, p = 7.92e-22 ( 258 proteins log scale)\n", " transport : r² = 0.2613, p = 1.49e-04 ( 50 proteins log scale)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1 file(s) exported for \"Load reaction data\" into Escher maps\n" ] } ], "source": [ "# Optimize model using cobrapy and extract results\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " with ecm as model: \n", " model.medium = medium \n", " solution = model.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=reference_cond)\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(reference_cond)\n", "er.plot_proteins(reference_cond) \n", "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', target_model))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### check outliers in protein correlation\n", "\n", "The presence of issues in the model parametrization can be identified through a visual examination of protein correlation plots in both linear and log scale. Data points suspected of having issues should be queried and subsequently analyzed in detail." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b3389 pred 30.1695 vs. 0.0809; aroB: 3-dehydroquinate synthase\n", "b1276 pred 11.4470 vs. 0.3967; acnA: Aconitate hydratase 1\n", "b1851 pred 37.5290 vs. 0.0359; edd: Phosphogluconate dehydratase\n", "b3737 pred 10.4876 vs. 0.0161; atpE: ATP synthase subunit c\n", "b1136 pred 0.4634 vs. 10.5086; icd: Isocitrate dehydrogenase [NADP]\n", "b3236 pred 0.0974 vs. 11.7189; mdh: Malate dehydrogenase\n", "b2414 pred 0.1408 vs. 17.2441; cysK: Cysteine synthase A\n", "b3829 pred 0.0000 vs. 29.7986; metE: 5-methyltetrahydropteroyltriglutamate--homocysteine methyltransferase\n" ] } ], "source": [ "# proteins from linear protein correlation plot with poor predictions\n", "for gene, pred in df_proteins[reference_cond].to_dict().items():\n", " if gene in df_mpmf.index:\n", " exp = df_mpmf.at[gene, reference_cond]\n", " if (exp < 1 and pred > 10) or (exp > 10 and pred < 1):\n", " description = df_proteins.at[gene, 'description']\n", " gname = df_proteins.at[gene, 'gene_name']\n", " print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f}; {gname}: {description}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### query results records of interest\n", "\n", "The supplementary information columns present in the optimization results tables are useful for filtering records of interest. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
reaction_strgprgroupsrankmean mmol_per_gDWhabs_mean mmol_per_gDWhstdevAcetateGlycerolFructoseL-MalateGlucoseGlucose 6-Phosphate
rid
EDD6pgc_c => 2ddg6p_c + h2o_cb1851374.4519824.4519825.0543090.00.06.4809020.010.15101210.079977
\n", "
" ], "text/plain": [ " reaction_str gpr groups rank mean mmol_per_gDWh \\\n", "rid \n", "EDD 6pgc_c => 2ddg6p_c + h2o_c b1851 37 4.451982 \n", "\n", " abs_mean mmol_per_gDWh stdev Acetate Glycerol Fructose L-Malate \\\n", "rid \n", "EDD 4.451982 5.054309 0.0 0.0 6.480902 0.0 \n", "\n", " Glucose Glucose 6-Phosphate \n", "rid \n", "EDD 10.151012 10.079977 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check reactions catalyzed by a given gene product\n", "df_net_fluxes[df_net_fluxes['gpr'].str.contains('b1851')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### visualize flux distribution on Escher maps\n", "\n", "The predicted flux values were exported in the ‘.json’ format using the command `er.save_to_escher()`. Given the central carbon metabolism focal point, the subsequent step involves accessing the Escher web portal (https://escher.github.io), loading the map designated 'Central Metabolism (iJO1366)', and selecting 'Data > Load reaction data' to upload the file named 'iML1515_predicted_GECKO_Glucose_reaction_data.json' under '.\\data' (Data > Load reaction data).\n", "\n", "A visual inspection of the flux distribution indicates that the upper glycolytic flux appears to be inadequate, as the majority of imported glucose is directed towards the pentose phosphate pathway (PPP). To address this issue, we will adjust the turnover numbers to shift flux from the PPP to glycolysis. Additionally, we will attempt to enhance the flux distribution within the tricarboxylic acid cycle (TCA).\n", "\n", "### query critical reaction fluxes and related proteins. \n", "\n", "Having gained insights into the predicted flux distributions between glycolysis and the pentose phosphate pathway (PPP), we return to the optimization results and query critical reaction fluxes in those pathways and the related proteins. We can observe that the protein encoded by the gene b1852\\zwf has a predicted concentration that is higher than the measured concentration by a factor of 7.5. In contrast, the protein encoded by b4025/pgi has a 8-fold lower predicted concentration than measured. The gene products b1723/pfgB and b3916/pgkA are isoenzymes of the PFK reaction, which has been sub-divided into two sub-reactions: PFK_iso1, catalyzed by b1723 with a molecular weight of 32.4 kDa, and PFK_iso2, catalyzed by b3916 (34.8 kDa). As their predicted turnover numbers are similar (13.37 and 13.34 $s^{-1}$, respectively), GECKO selects the sub-reaction PFK_iso1, due to its lower molecular weight. However, proteomics data and literature (see BioCyc entries for these genes) suggest that b3916/pgkA is the preferred protein. The FBA reaction is catalyzed by the isoenzymes b2097/fbaA and b2925/fbaB, and the predicted flux should be carried by the sub-reaction catalyzed by b2925/fbaA." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 10.911 mmol/gDWh [b1852]\n", "EDA : 10.151 mmol/gDWh [b1850]\n", "PGI : 0.154 mmol/gDWh [b4025]\n", "PFK : 0.092 mmol/gDWh [b1723 or b3916]\n", "FBA : 0.092 mmol/gDWh [b2097 or b2925]\n", "TPI : 0.000 mmol/gDWh [b3919]\n", "GAPD : 10.154 mmol/gDWh [b1779]\n", "PGK : -10.154 mmol/gDWh [b2926]\n", "PGM : -9.030 mmol/gDWh [b0755 or b3612]\n", "ENO : 9.030 mmol/gDWh [b2779]\n", "PYK : 0.000 mmol/gDWh [b1676 or b1854]\n", "G6PDH2r b1852 pred: 2.960 exp: 0.469 mg/gP zwf , 55.7 kDa, Glucose-6-phosphate 1-dehydrogenase\n", "EDA b1850 pred: 8.981 exp: 0.552 mg/gP eda , 22.3 kDa, KHG/KDPG aldolase\n", "PGI b4025 pred: 0.086 exp: 1.931 mg/gP pgi , 61.5 kDa, Glucose-6-phosphate isomerase\n", "PFK b1723 pred: 0.160 exp: 0.208 mg/gP pfkB, 32.4 kDa, 6-phosphofructokinase isozyme 2\n", "PFK b3916 pred: 0.000 exp: 0.344 mg/gP pfkA, 34.8 kDa, 6-phosphofructokinase isozyme 1\n", "FBA b2097 pred: 0.294 exp: 0.561 mg/gP fbaB, 38.1 kDa, Fructose-bisphosphate aldolase class 1\n", "FBA b2925 pred: 0.000 exp: 5.353 mg/gP fbaA, 39.1 kDa, Fructose-bisphosphate aldolase class 2\n", "TPI b3919 pred: 0.000 exp: 1.368 mg/gP tpiA, 26.9 kDa, Triosephosphate isomerase\n", "GAPD b1779 pred: 10.363 exp: 11.410 mg/gP gapA, 35.5 kDa, Glyceraldehyde-3-phosphate dehydrogenase A\n", "PGK b2926 pred: 4.157 exp: 11.229 mg/gP pgk , 41.1 kDa, Phosphoglycerate kinase\n", "PGM b0755 pred: 3.473 exp: 2.348 mg/gP gpmA, 28.5 kDa, 2,3-bisphosphoglycerate-dependent phosphoglyc\n", "PGM b3612 pred: 0.000 exp: 1.018 mg/gP gpmI, 56.1 kDa, 2,3-bisphosphoglycerate-independent phosphogl\n", "ENO b2779 pred: 12.648 exp: 11.257 mg/gP eno , 45.6 kDa, Enolase\n", "PYK b1676 pred: 0.000 exp: 3.080 mg/gP pykF, 50.7 kDa, Pyruvate kinase I\n", "PYK b1854 pred: 0.000 exp: 0.666 mg/gP pykA, 51.3 kDa, Pyruvate kinase II\n" ] } ], "source": [ "# analyze flux split upper glycolysis and PPP\n", "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n", "\n", "rid2genes = {}\n", "for rid in rids:\n", " gpr = df_net_fluxes.at[rid, 'gpr']\n", " print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{gpr}]')\n", " rid2genes[rid] = re.findall(r'b\\d{4}', gpr )\n", " \n", "for rid, genes in rid2genes.items():\n", " for gene in genes:\n", " pred = df_proteins[reference_cond].get(gene, 0.0)\n", " if gene in df_mpmf.index:\n", " row = df_mpmf.loc[gene]\n", " exp = row[reference_cond]\n", " print(f'{rid:10s} {gene} pred: {pred:6.3f} exp: {exp:6.3f} mg/gP {row[\"gene_name\"]:4s}, '\n", " f'{row[\"mw_Da\"]/1000.0:5.1f} kDa, {row[\"description\"][:45]}')\n", " else:\n", " print(f'{rid:10s} {gene} pred: {pred:6.3f} exp: not measured')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Manual adjustment of model parameters\n", "\n", "In order to improve protein predictions, we propose a redistribution of traffic flux between glycolysis and PPP. By decreasing the cost of proteins in glycolysis relative to PPP, flux can be redistributed from PPP to glycolysis. This can be achieved by increasing or decreasing the turnover numbers of selected reactions in these pathways. \n", "\n", "First, we assess the impact of different parameter sets in memory. Once we are satisfied, we generate a new GECKO model with the modified parameters. The method of choice for modifying the turnover numbers of selected reactions in memory is EcmOptimize's `eo.scale_kcats()`, which, in fact, updates the coupling factors. The turnover numbers are scaled by a factor provided in a Python dictionary; for example, the factor 2.0 will double the original turnover number. At the termination of the optimization loop, a reversion to the original turnover numbers is facilitated by invoking the function `eo.unscale_kcats()`. Alternatively, when the initial scaling is executed within the context of the cobrapy model (`with ecm as model:`), the reversion to the original values is carried out automatically. Modifications to the turnover numbers affect the predicted growth rates, necessitating adjustments to the average enzyme saturation. This adjustment is achieved indirectly by decreasing the total protein available, utilizing the cobrapy assignment statement `model.reactions.get_by_id('V_PC_total').upper_bound` to establish the upper limit of the variable ‘V_PC_total’ within the model context. The identification of a suitable set of scaling factors that enhance protein predictions is an iterative process, and the resultant set is presented below." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.513 h-1 vs. exp 0.290, diff: 0.223\n", "Glycerol : pred gr: 0.656 h-1 vs. exp 0.470, diff: 0.186\n", "Fructose : pred gr: 0.648 h-1 vs. exp 0.540, diff: 0.108\n", "L-Malate : pred gr: 0.664 h-1 vs. exp 0.550, diff: 0.114\n", "Glucose : pred gr: 0.661 h-1 vs. exp 0.660, diff: 0.001\n", "Glucose 6-Phosphate : pred gr: 0.676 h-1 vs. exp 0.780, diff: -0.104\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 443.0 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 406.3 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 250.6 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 155.7 mg/gP predicted\n", " 495 proteins not measured vs. 36.6 mg/gP predicted\n", " 495 actual proteins 36.6 mg/gP predicted\n", "total : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", " metabolic : r² = 0.2173, p = 2.22e-42 ( 762 proteins lin scale)\n", " transport : r² = 0.4309, p = 1.36e-30 ( 237 proteins lin scale)\n", "total : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", " metabolic : r² = 0.3800, p = 1.33e-28 ( 260 proteins log scale)\n", " transport : r² = 0.2661, p = 1.75e-04 ( 48 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 cobrapy and analyze results with fitted parameters\n", "\n", "scale_kcats = {\n", " # 1. rebalance flux between upper glycolysis and PPP\n", " 'PGI': 2.0, 'PFK_iso2': 20.0, 'FBA_iso2': 10.0, 'TPI': 20.0, 'ENO': 2.0, 'PGM_iso1_REV': 2.0,\n", " 'G6PDH2r': 0.2,\n", " \n", " # 2. flux through usage of TCA-cycle\n", " 'PDH': 10.0, 'ACONTa_iso1': 7.0, 'ACONTb_iso1': 7.0, \n", " 'ICDHyr': 0.2, 'AKGDH': 8.0, 'SUCOAS_REV': 3.0, 'SUCDi': 8.0, 'MDH': 0.1, \n", " \n", " # 3. fix the low predicted flux for DHQS reaction\n", " 'DHQS': 16.0/0.23,\n", " } \n", "\n", "# check that scale_kcats dict contains correct reaction ids\n", "for rid in scale_kcats:\n", " if rid not in eo.rdata:\n", " print(f'{rid:15s} not found')\n", " raise ValueError \n", "\n", "# optimization loop\n", "eo.scale_kcats(scale_kcats)\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " with ecm as model:\n", "\n", " new_sat_level = .56\n", " model.reactions.get_by_id('V_PC_total').upper_bound = total_protein * new_sat_level / sigma\n", "\n", " model.medium = medium \n", " solution = model.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", "eo.unscale_kcats()\n", "\n", "# results analysis\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=reference_cond)\n", "\n", "print(f'Protein mass fractions:')\n", "er.report_protein_levels(reference_cond)\n", "er.plot_proteins(reference_cond) \n", "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', f'{baseline_model}_adjusted'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### confirm flux redistribution\n", "\n", "The updated turnover numbers have resulted in a significant redistribution of flux from the pentose phosphate pathway (PPP) to glycolysis. Predictions of protein concentration appear to be more accurate when compared to measured protein levels. Furthermore, the selection of correct isoenzymes has been confirmed, as evidenced by the observations of b3916/pfkA and b2925/fbaA." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 3.747 mmol/gDWh [b1852]\n", "EDA : 0.000 mmol/gDWh [b1850]\n", "PGI : 3.949 mmol/gDWh [b4025]\n", "PFK : 5.872 mmol/gDWh [b1723 or b3916]\n", "FBA : 5.872 mmol/gDWh [b2097 or b2925]\n", "TPI : 5.779 mmol/gDWh [b3919]\n", "GAPD : 12.553 mmol/gDWh [b1779]\n", "PGK : -12.553 mmol/gDWh [b2926]\n", "PGM : -11.417 mmol/gDWh [b0755 or b3612]\n", "ENO : 11.417 mmol/gDWh [b2779]\n", "PYK : 3.173 mmol/gDWh [b1676 or b1854]\n", "G6PDH2r b1852 pred: 5.082 exp: 0.469 mg/gP zwf , 55.7 kDa, Glucose-6-phosphate 1-dehydrogenase\n", "EDA b1850 pred: 0.000 exp: 0.552 mg/gP eda , 22.3 kDa, KHG/KDPG aldolase\n", "PGI b4025 pred: 1.102 exp: 1.931 mg/gP pgi , 61.5 kDa, Glucose-6-phosphate isomerase\n", "PFK b1723 pred: 0.000 exp: 0.208 mg/gP pfkB, 32.4 kDa, 6-phosphofructokinase isozyme 2\n", "PFK b3916 pred: 0.550 exp: 0.344 mg/gP pfkA, 34.8 kDa, 6-phosphofructokinase isozyme 1\n", "FBA b2097 pred: 0.000 exp: 0.561 mg/gP fbaB, 38.1 kDa, Fructose-bisphosphate aldolase class 1\n", "FBA b2925 pred: 3.404 exp: 5.353 mg/gP fbaA, 39.1 kDa, Fructose-bisphosphate aldolase class 2\n", "TPI b3919 pred: 1.252 exp: 1.368 mg/gP tpiA, 26.9 kDa, Triosephosphate isomerase\n", "GAPD b1779 pred: 12.812 exp: 11.410 mg/gP gapA, 35.5 kDa, Glyceraldehyde-3-phosphate dehydrogenase A\n", "PGK b2926 pred: 5.139 exp: 11.229 mg/gP pgk , 41.1 kDa, Phosphoglycerate kinase\n", "PGM b0755 pred: 2.195 exp: 2.348 mg/gP gpmA, 28.5 kDa, 2,3-bisphosphoglycerate-dependent phosphoglyc\n", "PGM b3612 pred: 0.000 exp: 1.018 mg/gP gpmI, 56.1 kDa, 2,3-bisphosphoglycerate-independent phosphogl\n", "ENO b2779 pred: 7.995 exp: 11.257 mg/gP eno , 45.6 kDa, Enolase\n", "PYK b1676 pred: 6.622 exp: 3.080 mg/gP pykF, 50.7 kDa, Pyruvate kinase I\n", "PYK b1854 pred: 0.000 exp: 0.666 mg/gP pykA, 51.3 kDa, Pyruvate kinase II\n" ] } ], "source": [ "# analyze flux split upper glycolysis and PPP\n", "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n", "\n", "rid2genes = {}\n", "for rid in rids:\n", " gpr = df_net_fluxes.at[rid, 'gpr']\n", " print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{gpr}]')\n", " rid2genes[rid] = re.findall(r'b\\d{4}', gpr )\n", " \n", "for rid, genes in rid2genes.items():\n", " for gene in genes:\n", " pred = df_proteins[reference_cond].get(gene, 0.0)\n", " if gene in df_mpmf.index:\n", " row = df_mpmf.loc[gene]\n", " exp = row[reference_cond]\n", " print(f'{rid:10s} {gene} pred: {pred:6.3f} exp: {exp:6.3f} mg/gP {row[\"gene_name\"]:4s}, '\n", " f'{row[\"mw_Da\"]/1000.0:5.1f} kDa, {row[\"description\"][:45]}')\n", " else:\n", " print(f'{rid:10s} {gene} pred: {pred:6.3f} exp: not measured')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Create GECKO model with adjusted parameters\n", "\n", "Assuming that the model predictions meet our satisfaction level, the adjusted turnover numbers can be updated. Subsequently, the XBA and ECM configuration files can be updated, and the target GECKO model can be created." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 table(s) with parameters loaded from data/iML1515_predicted_GECKO_kcats.xlsx (Thu Nov 20 18:55:42 2025)\n", "1 table(s) with parameters written to data/iML1515_manual_adjust_GECKO_kcats.xlsx\n", "3 table(s) with parameters loaded from data/iML1515_predicted_GECKO_xba_parameters.xlsx (Thu Nov 20 18:55:42 2025)\n", "3 table(s) with parameters written to data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx\n", "1 table(s) with parameters loaded from data/iML1515_predicted_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:55:42 2025)\n", "1 table(s) with parameters written to data/iML1515_manual_adjust_GECKO_ecm_parameters.xlsx\n", "loading: SBML_models/iML1515.xml (last modified: Thu Dec 5 10:03:46 2024)\n", "3 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx (Thu Nov 20 18:58:14 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_manual_adjust_GECKO_kcats.xlsx (Thu Nov 20 18:58:14 2025)\n", "5357 kcat values updated from data/iML1515_manual_adjust_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_manual_adjust_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:58:14 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_manual_adjust_GECKO.xml\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create updated kcats file\n", "df_kcats = load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats']\n", "for rid, scale in scale_kcats.items():\n", " df_kcats.at[f'R_{rid}', 'kcat_per_s'] *= scale\n", " df_kcats.at[f'R_{rid}', 'notes'] = 'manually adjusted'\n", "write_parameter_file(os.path.join('data', f'{target_model}_kcats.xlsx'), {'kcats': df_kcats})\n", "\n", "# Create updated XBA parameter\n", "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n", "xba_params['general'].at['kcats_fname', 'value'] = os.path.join('data', f'{target_model}_kcats.xlsx')\n", "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n", "\n", "# Create updated ECM parameter file\n", "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'))\n", "ecm_params['general'].at['avg_enz_sat', 'value'] = new_sat_level\n", "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)\n", "\n", "# 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", "ec_model = EcModel(xba_model)\n", "ec_model.configure(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'))\n", "ec_model.export(os.path.join('SBML_models', f'{target_model}.xml'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "---\n", "## Step 5: Load and optimize GECKO model (cobrapy)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SBML model loaded by sbmlxdf: SBML_models/iML1515_manual_adjust_GECKO.xml (Thu Nov 20 18:58:37 2025)\n", "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.56\n" ] } ], "source": [ "# Load model using cobrapy\n", "fname = os.path.join('SBML_models', f'{target_model}.xml')\n", "ecm = cobra.io.read_sbml_model(fname)\n", "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n", "\n", "eo = EcmOptimization(fname, ecm)\n", "sigma = eo.avg_enz_saturation\n", "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.513 h-1 vs. exp 0.290, diff: 0.223\n", "Glycerol : pred gr: 0.656 h-1 vs. exp 0.470, diff: 0.186\n", "Fructose : pred gr: 0.648 h-1 vs. exp 0.540, diff: 0.108\n", "L-Malate : pred gr: 0.664 h-1 vs. exp 0.550, diff: 0.114\n", "Glucose : pred gr: 0.661 h-1 vs. exp 0.660, diff: 0.001\n", "Glucose 6-Phosphate : pred gr: 0.676 h-1 vs. exp 0.780, diff: -0.104\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "Acetate : r² = 0.0895, p = 4.31e-22 ( 999 proteins lin scale)\n", "Glycerol : r² = 0.0838, p = 9.63e-21 ( 999 proteins lin scale)\n", "Fructose : r² = 0.2062, p = 5.73e-52 ( 999 proteins lin scale)\n", "Glucose : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", "Acetate : r² = 0.2912, p = 4.52e-24 ( 300 proteins log scale)\n", "Glycerol : r² = 0.3015, p = 5.08e-25 ( 300 proteins log scale)\n", "Fructose : r² = 0.3129, p = 9.11e-26 ( 296 proteins log scale)\n", "Glucose : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", "\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 537.9 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 493.4 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 304.3 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 189.0 mg/gP predicted\n", " 495 proteins not measured vs. 44.5 mg/gP predicted\n", " 495 actual proteins 44.5 mg/gP predicted\n", "total : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", " metabolic : r² = 0.2173, p = 2.22e-42 ( 762 proteins lin scale)\n", " transport : r² = 0.4309, p = 1.36e-30 ( 237 proteins lin scale)\n", "total : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", " metabolic : r² = 0.3800, p = 1.33e-28 ( 260 proteins log scale)\n", " transport : r² = 0.2661, p = 1.75e-04 ( 48 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 cobrapy and analyze results\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " with ecm as model: \n", " model.medium = medium \n", " solution = model.optimize()\n", " if solution.status == 'optimal':\n", " gr = solution.objective_value\n", " pred_results[cond] = solution\n", " print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, '\n", " f'diff: {gr - exp_grs[cond]:6.3f}')\n", " else: \n", " print(f'{cond} ended with status {solution.status}')\n", "\n", "# Analyze results\n", "er = EcmResults(eo, pred_results, df_mpmf)\n", "df_fluxes = er.collect_fluxes()\n", "df_net_fluxes = er.collect_fluxes(net=True)\n", "df_proteins = er.collect_protein_results()\n", "er.plot_grs(exp_grs, highlight='Glucose')\n", "\n", "print(f'Protein mass fractions:')\n", "er.report_proteomics_correlation(scale='lin')\n", "er.report_proteomics_correlation(scale='log')\n", "print()\n", "er.report_protein_levels('Glucose')\n", "er.plot_proteins('Glucose', plot_fname=os.path.join('plots', f'{target_model}_proteins_Glucose.pdf')) \n", "er.save_to_escher(df_net_fluxes['Glucose'], os.path.join('escher', target_model))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 3.747 mmol/gDWh [b1852]\n", "EDA : 0.000 mmol/gDWh [b1850]\n", "PGI : 3.949 mmol/gDWh [b4025]\n", "PFK : 5.872 mmol/gDWh [b1723 or b3916]\n", "FBA : 5.872 mmol/gDWh [b2097 or b2925]\n", "TPI : 5.779 mmol/gDWh [b3919]\n", "GAPD : 12.553 mmol/gDWh [b1779]\n", "PGK : -12.553 mmol/gDWh [b2926]\n", "PGM : -11.417 mmol/gDWh [b0755 or b3612]\n", "ENO : 11.417 mmol/gDWh [b2779]\n", "PYK : 3.173 mmol/gDWh [b1676 or b1854]\n" ] } ], "source": [ "# analyze reactions selected fluxes\n", "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n", "for rid in rids:\n", " print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{df_net_fluxes.at[rid, \"gpr\"]}]')" ] }, { "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 Nov 20 18:56:40 2025)\n", "1 table(s) with parameters written to protein_predictions.xlsx\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
modellin r2log r2lin proteinslog proteins
No
1iML1515_default_GECKO0.0332440.1902251018299
2iML1515_modified_GECKO0.1359810.201613999322
3iML1515_predicted_GECKO0.0788030.268769999308
4iML1515_manual_adjust_GECKO0.2054130.333470999308
\n", "
" ], "text/plain": [ " model lin r2 log r2 lin proteins \\\n", "No \n", "1 iML1515_default_GECKO 0.033244 0.190225 1018 \n", "2 iML1515_modified_GECKO 0.135981 0.201613 999 \n", "3 iML1515_predicted_GECKO 0.078803 0.268769 999 \n", "4 iML1515_manual_adjust_GECKO 0.205413 0.333470 999 \n", "\n", " log proteins \n", "No \n", "1 299 \n", "2 322 \n", "3 308 \n", "4 308 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import scipy\n", "import numpy as np\n", "\n", "number = 4\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", "By manually adjusting the turnover numbers of seven reactions in glycolysis and the PPP, a substantial flux shift from the PPP to glycolysis can be achieved, thereby enhancing the predictions of protein concentrations in these pathways. Consequently, the overall correlation between predicted and measured protein concentrations has been shown to improve. The adaptation of enzyme-related parameters to known flux distributions and proteomics data emerges as a promising approach to refine our model.\n", "\n", "In the subsequent tutorial, we will proceed to enhance our GECKO model through the implementation of an automatic fitting process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "---\n", "## (Alternative) gurobipy - manual parameter fitting\n", "\n", "The manual fitting of model parameters using the gurobipy interface is nearly equivalent to the modifications applied using cobrapy. However, it should be noted that model contexts are not supported using the gurobipy interface. Consequently, it is necessary to explicitly revert model modifications at the termination of the optimization loop. The function `eo.set_variable_bounds()` updates variable bounds and is employed to modify the total available protein. Consequently, at the termination of the loop, it is necessary to reset the total protein using the aforementioned function. The update of turnover numbers and the creation of the target GECKO model have been demonstrated at step 4 above." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_GECKO.xml (Thu Nov 20 18:56:05 2025)\n", "LP Model of iML1515_GECKO\n", "7343 variables, 3372 constraints, 28438 non-zero matrix coefficients\n", "total modeled protein: 306.59 mg/gDW, average saturation level: 0.68\n" ] } ], "source": [ "# Load model using gurobipy\n", "fname = os.path.join('SBML_models', f'{baseline_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: 0.368 h-1 vs. exp 0.290, diff: 0.078\n", "Glycerol : pred gr: 0.570 h-1 vs. exp 0.470, diff: 0.100\n", "Fructose : pred gr: 0.559 h-1 vs. exp 0.540, diff: 0.019\n", "L-Malate : pred gr: 0.568 h-1 vs. exp 0.550, diff: 0.018\n", "Glucose : pred gr: 0.574 h-1 vs. exp 0.660, diff: -0.086\n", "Glucose 6-Phosphate : pred gr: 0.596 h-1 vs. exp 0.780, diff: -0.184\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 443.0 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 374.7 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 239.8 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 134.9 mg/gP predicted\n", " 495 proteins not measured vs. 68.3 mg/gP predicted\n", " 495 actual proteins 68.3 mg/gP predicted\n", "total : r² = 0.1419, p = 4.97e-35 ( 999 proteins lin scale)\n", " metabolic : r² = 0.0980, p = 8.58e-19 ( 762 proteins lin scale)\n", " transport : r² = 0.4748, p = 1.02e-34 ( 237 proteins lin scale)\n", "total : r² = 0.2961, p = 6.63e-25 ( 305 proteins log scale)\n", " metabolic : r² = 0.3397, p = 1.35e-24 ( 255 proteins log scale)\n", " transport : r² = 0.2675, p = 1.20e-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 with modified parameters\n", "\n", "scale_kcats = {'PGI': 2.0, 'PFK_iso2': 20.0, 'FBA_iso2': 10.0, 'TPI': 20.0, 'ENO': 2.0, 'PGM_iso1_REV': 3.0} \n", "for rid in scale_kcats:\n", " if rid not in eo.rdata:\n", " print(f'{rid:15s} not found')\n", " raise ValueError \n", "\n", "new_sat_level = .56\n", "orig_bounds = eo.set_variable_bounds({'V_PC_total': (None, total_protein * new_sat_level / sigma)})\n", "eo.scale_kcats(scale_kcats)\n", "\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " eo.medium = medium\n", " solution = eo.optimize()\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", "eo.unscale_kcats()\n", "if orig_bounds:\n", " eo.set_variable_bounds(orig_bounds)\n", "\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=reference_cond)\n", "\n", "print(f'Protein mass fractions:')\n", "er.report_protein_levels(reference_cond)\n", "er.plot_proteins(reference_cond) \n", "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', f'{baseline_model}_adjusted'))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 4.229 mmol/gDWh [b1852]\n", "EDA : 0.000 mmol/gDWh [b1850]\n", "PGI : 4.951 mmol/gDWh [b4025]\n", "PFK : 7.271 mmol/gDWh [b1723 or b3916]\n", "FBA : 7.271 mmol/gDWh [b2097 or b2925]\n", "TPI : 7.190 mmol/gDWh [b3919]\n", "GAPD : 15.569 mmol/gDWh [b1779]\n", "PGK : -15.569 mmol/gDWh [b2926]\n", "PGM : -14.582 mmol/gDWh [b0755 or b3612]\n", "ENO : 14.582 mmol/gDWh [b2779]\n", "PYK : 3.227 mmol/gDWh [b1676 or b1854]\n" ] } ], "source": [ "# analyze reactions selected fluxes\n", "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n", "for rid in rids:\n", " print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{df_net_fluxes.at[rid, \"gpr\"]}]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "---\n", "## References\n", "- Araiza-Olivera, D., Chiquete-Felix, N., Rosas-Lemus, M., Sampedro, J. G., Peña, A., Mujica, A., & Uribe-Carvajal, S. (2013). A glycolytic metabolon in Saccharomyces cerevisiae is stabilized by F-actin. " ] }, { "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 }