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XomicsToModel: omics data integration and generation of thermodynamically consistent metabolic models

Nature Protocols (2025)Cite this article

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Abstract

Constraint-based modeling can mechanistically simulate the behavior of a biochemical system, permitting hypothesis generation, experimental design and interpretation of experimental data, with numerous applications, especially the modeling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in each context. However, the existing model extraction algorithms are unable to ensure that a context-specific model is thermodynamically flux consistent. Here we introduce XomicsToModel, a semiautomated pipeline that integrates bibliomic, transcriptomic, proteomic and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. One of the key advantages of the XomicsToModel pipeline is its ability to seamlessly incorporate omics data into metabolic reconstructions, ensuring not only mechanistic accuracy but also physicochemical consistency. This functionality enables more accurate metabolic simulations and predictions across different biological contexts, enhancing its utility in diverse research fields, including systems biology, drug development and personalized medicine. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model; it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. It can be implemented by anyone who has basic MATLAB programming skills and the fundamentals of constraint-based modeling.

Key points

  • XomicsToModel is a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic and metabolomic data with a generic genome-scale metabolic reconstruction or model.

  • It enables the seamless incorporation of multiomics datasets into metabolic reconstructions, ensuring mechanistic accuracy and physicochemical consistency.

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Fig. 1: Network diagram of upper glycolysis with reactions and metabolites with corresponding labels. Left: a network diagram of upper glycolysis (left) with reactions (arrows, uppercase labels) and metabolites (nodes, lowercase labels) with corresponding labels.
Fig. 2: Overview of the XomicsToModel pipeline.
Fig. 3: The gene–protein–reaction association are Boolean operators that describe the interactions of genes, transcripts, proteins and reactions.
Fig. 4: Comparison of specified and actual metabolites and reactions in an extracted model.
Fig. 5: Comparison of weights on active and inactive reactions and metabolites.

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Data availability

Constraint-based reconstruction, modeling and analysis was implemented in MATLAB (MathWorks). Open-source computer code enabling the reproduction of all computational steps is available within the COBRA Toolbox34 version 3.4+. This includes code for the model generation XomicsToModel.m, thermodynamically feasible model extraction thermoKernel.m, debugging the performance of XomicsToModel debugXomicsToModel.m, the comparison of multiple models compareXomicsToModel.m and for the maximization of flux entropy, with the option of quadratic penalization of deviation from measured experimental fluxes entropicFBA.m; it is available via GitHub at https://github.com/opencobra/cobratoolbox/tree/master/src/dataIntegration/XomicsToModel (ref. 51). An executable narrative tutorial demonstrating the use of the XomicsToModel pipleine is also available, tutorial_XomicsToModel.mlx, with the example data for generating a dopaminergic neuronal metabolic model; it is available via GitHub at https://github.com/opencobra/COBRA.tutorials/blob/master/dataIntegration/XomicsToModel/tutorial_XomicsToModel.mlx (ref. 52). An HTML version of this tutorial is accessible via GitHub at https://github.com/opencobra/COBRA.tutorials/blob/master/dataIntegration/XomicsToModel/tutorial_XomicsToModel.html. Linear and quadratic optimization problems may be solved with open source solvers, for example, GLPK, or using an industrial solver, such as Gurobi 9.1 (Gurobi). Nonlinear convex optimization problems, for example, entropic optimization, may be solved with the open source solver primal-dual interior method for convex objectives PDCO implemented in MATLAB (MathWorks) or with an industrial solver, using the exponential cone solver within Mosek 10.0.30 (Mosek ApS). Note that PDCO may require specialist numerical optimization parameters to be tailored to each model, whereas Mosek is more robust with respect to the numerical characteristics of input models. Each of the aforementioned solvers are interfaced with the COBRA Toolbox and are suited for models that are not numerically ill-scaled.

References

  1. Palsson, B. Ø. Systems Biology: Constraint-Based Reconstruction and Analysis. (Cambridge Univ. Press, 2015).

  2. Thiele, I. & Palsson, B. Ø A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93–121 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Machado, D. et al. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Norsigian, C. J. et al. A workflow for generating multi-strain genome-scale metabolic models of prokaryotes. Nat. Protoc. 15, 1–14 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Wang, H. et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2102344118(2021).

  6. Opdam, S. et al. A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Syst. 4, 318–329.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. https://doi.org/10.1038/msb.2010.56 (2010).

  8. Vinnakota, K. C. et al. Network modeling of liver metabolism to predict plasma metabolite changes during short-term fasting in the laboratory rat. Front. Physiol. 10, 161 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Preciat, G. et al. Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism. Preprint at bioRxiv https://doi.org/10.1101/2021.06.30.450562 (2021).

  10. Haraldsdóttir, H. S. et al. CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. Bioinformatics 33, 1741–1743 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fleming, R. M. T. & Thiele, I. Mass conserved elementary kinetics is sufficient for the existence of a non-equilibrium steady state concentration. J. Theor. Biol. 314, 173–181 (2012).

    Article  CAS  PubMed  Google Scholar 

  12. Vlassis, N., Pacheco, M. P. & Sauter, T. Fast reconstruction of compact context-specific metabolic network models. PLoS Comput. Biol. 10, e1003424 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fleming, R. M. T. et al. A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks. J. Theor. Biol. 292, 71–77 (2011).

    Article  PubMed  Google Scholar 

  14. Orth, J. D., Thiele, I. & Palsson, B. Ø What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Thiele, I., Vlassis, N. & Fleming, R. M. T. fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30, 2529–2531 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Beard, D. A., Liang, S.-D. & Qian, H. Energy balance for analysis of complex metabolic networks. Biophys. J. 83, 79–86 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Dill, K. & Bromberg, S. Molecular Driving Forces Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience (Garland Science, 2011).

  19. Boyd, S. P. & Vandenberghe, L. Convex Optimization (Cambridge Univ. Press, 2004).

  20. Desouki, A. A. et al. CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions. Bioinformatics 31, 2159–2165 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Becker, S. A. & Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4, e1000082 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zur, H., Ruppin, E. & Shlomi, T. iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140–3142 (2010).

    Article  CAS  PubMed  Google Scholar 

  23. Wang, Y., Eddy, J. A. & Price, N. D. Reconstruction of Genome-Scale Metabolic Models for 126 Human Tissues Using mCADRE. BMC Syst. Biol. 6, 153 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Luo, X. et al. Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of human dopaminergic neurons. Front. Comput. Neurosci. 19, 1594330 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Luo, X. et al. Identification of metabolites reproducibly associated with Parkinson’s disease via meta-analysis and computational modelling. NPJ Parkinsons Dis. 10, 126 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zagare, A. et al. Omics data integration suggests a potential idiopathic Parkinson’s disease signature. Commun. Biol. 6, 1179 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Fleming, R. M. T. et al. Conserved moiety fluxomics. Preprint at bioRxiv https://doi.org/10.1101/2024.11.21.624666 (2024).

  29. Huang, L. et al. fluxTrAM: integration of tracer-based metabolomics data into atomically resolved genome-scale metabolic networks for metabolic flux analysis. Preprint at bioRxiv https://doi.org/10.1101/2024.11.26.625485 (2024).

  30. Kümmel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 2006.0034 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Thiele, I. et al. Functional characterization of alternate optimal solutions of Escherichia coli’s transcriptional and translational machinery. Biophys. J. 98, 2072–2081 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Medlock, G. L., Moutinho, T. J. & Papin, J. A. Medusa: software to build and analyze ensembles of genome-scale metabolic network reconstructions. PLoS Comput. Biol. 16, e1007847 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Balestrino, R. & Schapira, A. H. V. Parkinson disease. Eur. J. Neurol. 27, 27–42 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA toolbox v.3.0. Nat. Protoc. 14, 639 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Thiele, I. et al. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol. Syst. Biol. 16, e8982 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pacheco, M. P. et al. scFASTCORMICS: a contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data. Metabolites 12, 1211 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Vieira, V., Ferreira, J. & Rocha, M. A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale. PLoS Comput. Biol. 18, e1009294 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Estévez, S. R. & Nikoloski, Z. Generalized framework for context-specific metabolic model extraction methods. Front. Plant Sci. 5, 491 (2014).

    Google Scholar 

  39. Gopalakrishnan, S. et al. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab. Eng. 75, 181–191 (2023).

    Article  CAS  PubMed  Google Scholar 

  40. Tefagh, M. & Boyd, S. P. SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks. BMC Bioinform. 21, 140 (2020).

    Article  Google Scholar 

  41. Akbari, A. et al. The quantitative metabolome is shaped by abiotic constraints. Nat. Commun. 12, 3178 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mayr, E. What Makes Biology Unique?: Considerations on the Autonomy of a Scientific Discipline (Cambridge Univ. Press, 2007).

  43. Haraldsdóttir, H. S., Thiele, I. & Fleming, R. M. T. Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction. Biophys. J. 102, 1703–1711 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Sahin, A., Weilandt, D. R. & Hatzimanikatis, A. Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations. Nat. Commun. 14, 2618 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nath, S. S. Evaluation of thermodynamic consistency of kinetic parameters in cyclic enzyme-catalyzed reaction networks. Chem. Phys. Lett. 804, 139890 (2022).

    Article  CAS  Google Scholar 

  46. Kenney, I. M. & Beckstein, O. Thermodynamically consistent determination of free energies and rates in kinetic cycle models. Biophys. Rep. 3, 100120 (2023).

    CAS  Google Scholar 

  47. Gevorgyan, A., Poolman, M. G. & Fell, D. A. Detection of stoichiometric inconsistencies in biomolecular models. Bioinformatics 24, 2245–2251 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Shah, V. P. et al. Bioanalytical method validation—a revisit with a decade of progress. Pharm. Res. 17, 1551–1557 (2000).

    Article  CAS  PubMed  Google Scholar 

  50. Agren, R. et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 8, e1002518 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. cobratoolbox. GitHub https://github.com/opencobra/cobratoolbox/tree/master/src/dataIntegration/XomicsToModel (2025).

  52. COBRA.tutorials. GitHub https://opencobra.github.io/cobratoolbox/stable/tutorials/tutorial_XomicsToModel.html (2025).

  53. Agren, R. et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 721 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Gudmundsson, S. & Thiele, I. Computationally efficient flux variability analysis. BMC Bioinform. 11, 489 (2010).

    Article  Google Scholar 

  55. Noronha, A. et al. The virtual metabolic human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 47, D614–D624 (2019).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank H. Leegwater for her helpful feedback while revising the manuscript. We acknowledge funding from the European Union's Horizon 2020 Programme (668738), Horizon Europe Framework Programme (101080997), European Research Council (101125633) and Dutch Research Council NWO project 184.034.019.

Author information

Authors and Affiliations

  1. Metabolomics and Analytics Center, Leiden Academic Center for Drug Research, Leiden University, Einsteinweg, Leiden, The Netherlands

    German Preciat, Agnieszka B. Wegrzyn, Thomas Hankemeier & Ronan M. T. Fleming

  2. Department of Translational Bioengineering, University of Guadalajara, Guadalajara, Mexico

    German Preciat

  3. School of Medicine, University of Galway, Galway, Ireland

    Xi Luo, Ines Thiele & Ronan M. T. Fleming

  4. Ryan Institute, University of Galway, Galway, Ireland

    Ines Thiele

  5. Division of Microbiology, University of Galway, Galway, Ireland

    Ines Thiele

  6. APC Microbiome Ireland, Galway, Ireland

    Ines Thiele

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  1. German Preciat
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  4. Ines Thiele
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  6. Ronan M. T. Fleming
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Contributions

G.P.: conceptualization, methodology, software, tutorial and writing—original draft; A.W.: methodology, software, data curation and writing—review and editing; X.L.: methodology and software; I.T.: conceptualization, methodology, software and funding acquisition; T.H.: resources, supervision and funding acquisition; and R.M.T.F.: conceptualization, methodology, software, writing—original draft, writing—review and editing, supervision and funding acquisition.

Corresponding authors

Correspondence to Thomas Hankemeier or Ronan M. T. Fleming.

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Nature Protocols thanks Miguel Rocha and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references

Luo, X., El Assal, D. C., Liu, Y., Ranjbar, S. & Fleming, R. M. T. Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of human dopaminergic neurons. Front. Comput. Neurosci. 19, 594330 (2025): https://doi.org/10.3389/fncom.2025.1594330

Luo, X., Liu, Y., Balck, A., Klein, C. & Fleming, R. M. T. Identification of metabolites reproducibly associated with Parkinson’s disease via meta-analysis and computational modelling. NPJ Parkinsons Dis. 10, 126 (2024): https://doi.org/10.1038/s41531-024-00732-z

Zagare, A. et al. Omics data integration suggests a potential idiopathic Parkinson’s disease signature. Commun. Biol. 6, 1179 (2023): https://doi.org/10.1038/s42003-023-05548-w

Supplementary information

Supplementary Information (download PDF )

This includes five additional functions for use with XomicsToModel. preprocessingOmicsModel: prepares context-specific data for integration into XomicsToModel. modelPredictiveCapacity: assesses the model’s ability to predict fluxes and metabolic activities. XomicsToMultipleModels: generates an ensemble of models with variations in conditions, genetics or data. debugXomicsToModel: analyzes debug files to track changes in genes, reactions and metabolites. compareXomicsModels: identifies overlapping metabolites, reactions and genes across models. plotOverlapResults: visualizes overlaps with heat maps.

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Preciat, G., Wegrzyn, A.B., Luo, X. et al. XomicsToModel: omics data integration and generation of thermodynamically consistent metabolic models. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01288-9

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