Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0

Nature Protocols volume 19, pages 629–667 (2024)Cite this article

Subjects

Abstract

Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.

Key points

  • Genome-scale metabolic models have the potential to predict changes in phenotype resulting from different environmental conditions. Their predictive power can be improved by including more information about the enzyme kinetics.

  • GECKO is a program that gives users an automated and manual mechanism to incorporate relevant data. The protocol also describes how to tune the program to compensate for incorrect or missing values.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: GECKO framework.
Fig. 2: Workflow for ecModel reconstruction using GECKO 3.0.
Fig. 3: Simulations of Crabtree effect in various models.
Fig. 4: Flux variability of various models.
Fig. 5: Comparison of fluxes in full and light ecModels.

Similar content being viewed by others

Data availability

All (limited) data used in the tutorials are distributed as part of the code (see below).

Code availability

The source code of the GECKO toolbox is publicly available under the MIT license at: https://github.com/SysBioChalmers/GECKO/, also available at https://doi.org/10.5281/zenodo.7699818.

References

  1. Kim, W. J., Kim, H. U. & Lee, S. Y. Current state and applications of microbial genome-scale metabolic models. Curr. Opin. Syst. Biol. 2, 10–18 (2017).

    Google Scholar 

  2. Chen, Y., Li, G. & Nielsen, J. Genome-scale metabolic modeling from yeast to human cell models of complex diseases: latest advances and challenges. Methods Mol. Biol. 2049, 329–345 (2019).

    CAS  PubMed  Google Scholar 

  3. Clark, T. J., Guo, L., Morgan, J. & Schwender, J. Modeling plant metabolism: from network reconstruction to mechanistic models. Annu. Rev. Plant Biol. 71, 303–326 (2020).

    CAS  PubMed  Google Scholar 

  4. Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-00440-4 (2020).

  5. Heinken, A., Basile, A., Hertel, J., Thinnes, C. & Thiele, I. Genome-scale metabolic modeling of the human microbiome in the era of personalized medicine. Annu Rev. Microbiol 75, 199–222 (2021).

    CAS  PubMed  Google Scholar 

  6. Lewis, N. E., Nagarajan, H. & Palsson, B. O. Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 10, 291–305 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Lloyd, C. J. et al. COBRAme: a computational framework for genome-scale models of metabolism and gene expression. PLoS Comput. Biol. 14, e1006302 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. Goelzer, A. et al. Quantitative prediction of genome-wide resource allocation in bacteria. Metab. Eng. 32, 232–243 (2015).

    CAS  PubMed  Google Scholar 

  9. Salvy, P. & Hatzimanikatis, V. The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. Nat. Commun. 11, 30 (2020).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  10. Chen, Y. et al. Proteome constraints reveal targets for improving microbial fitness in nutrient‐rich environments. Mol. Syst. Biol. 17, e10093 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Beg, Q. K. et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl Acad. Sci. USA 104, 12663–12668 (2007).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  12. Adadi, R., Volkmer, B., Milo, R., Heinemann, M. & Shlomi, T. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput. Biol. 8, e1002575 (2012).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  13. Sánchez, B. J. et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol. 13, 935 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Bekiaris, P. S. & Klamt, S. Automatic construction of metabolic models with enzyme constraints. BMC Bioinforma. 21, 19 (2020).

    CAS  Google Scholar 

  15. Mao, Z. et al. ECMpy, a simplified workflow for constructing enzymatic constrained metabolic network model. Biomolecules 12, 65 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Domenzain, I. et al. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat. Commun. 13, 1–13 (2022).

    Google Scholar 

  17. Chen, Y. & Nielsen, J. Mathematical modelling of proteome constraints within metabolism. Curr. Opin. Syst. Biol. 25, 50–56 (2021).

    CAS  Google Scholar 

  18. Kerkhoven, E. J. Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints. Curr. Opin. Microbiol. 68, 102168 (2022).

    PubMed  Google Scholar 

  19. Chen, Y. & Nielsen, J. Energy metabolism controls phenotypes by protein efficiency and allocation. Proc. Natl Acad. Sci. USA 116, 17592–17597 (2019).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  20. Gustafsson, J. et al. Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth. Preprint at bioRxiv https://doi.org/10.1101/2022.02.08.479584 (2022).

  21. Chen, Y. & Nielsen, J. Yeast has evolved to minimize protein resource cost for synthesizing amino acids. Proc. Natl Acad. Sci. USA 119, e2114622119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021).

    CAS  PubMed  Google Scholar 

  23. Davidi, D. & Milo, R. Lessons on enzyme kinetics from quantitative proteomics. Curr. Opin. Biotechnol. 46, 81–89 (2017).

    CAS  PubMed  Google Scholar 

  24. Li, F. et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat. Catal. https://doi.org/10.1038/s41929-022-00798-z (2022).

  25. Sulheim, S. et al. Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production. iScience 23, 101525 (2020).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  26. Zhou, J., Zhuang, Y. & Xia, J. Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions. Microb. Cell Fact. 20, 125 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Ye, C. et al. Improving lysine production through construction of an Escherichia coli enzyme‐constrained model. Biotechnol. Bioeng. 117, 3533–3544 (2020).

    CAS  PubMed  Google Scholar 

  28. Chen, Y. et al. Genome‐scale modeling for Bacillus coagulans to understand the metabolic characteristics. Biotechnol. Bioeng. 117, 3545–3558 (2020).

    CAS  PubMed  Google Scholar 

  29. Lu, H. et al. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Mol. Syst. Biol. 17, e10427 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Ishchuk, O. P. et al. Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 119, e2108245119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Li, Z. et al. Systems engineering of Escherichia coli for high-level shikimate production. Metab. Eng. 75, 1–11 (2023).

    PubMed  Google Scholar 

  32. Bujdoš, D. et al. Engineering of Pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model. Metab. Eng. 75, 29–46 (2023).

    PubMed  Google Scholar 

  33. Domenzain, I., Lu, Y., Shi, J., Lu, H. & Nielsen, J. Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast. Preprint at bioRxiv https://doi.org/10.1101/2023.01.31.526512 (2023).

  34. Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. https://doi.org/10.1038/s41596-018-0098-2 (2019).

  35. Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis? Nat. Biotechnol. 28, 245 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  37. Choi, H. S., Lee, S. Y., Kim, T. Y. & Woo, H. M. In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol. 76, 3097–3105 (2010).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  39. Cho, J. S., Gu, C., Han, T. H., Ryu, J. Y. & Lee, S. Y. Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. Curr. Opin. Syst. Biol. 15, 1–11 (2019).

    Google Scholar 

  40. Robinson, J. L. et al. An atlas of human metabolism. Sci. Signal. 13, 1482 (2020).

    Google Scholar 

  41. Yeo, H. C., Hong, J., Lakshmanan, M. & Lee, D. Y. Enzyme capacity-based genome scale modelling of CHO cells. Metab. Eng. 60, 138–147 (2020).

    CAS  PubMed  Google Scholar 

  42. Wendering, P. & Nikoloski, Z. Genome-scale modeling specifies the metabolic capabilities of Rhizophagus irregularis. mSystems 7, e0121621 (2022).

    PubMed  Google Scholar 

  43. Wu, K. et al. ecBSU1: A genome-scale enzyme-constrained model of Bacillus subtilis based on the ECMpy workflow. Microorganisms 11, 178 (2023).

    PubMed  PubMed Central  Google Scholar 

  44. Österberg, L. et al. A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism. PLoS Comput. Biol. 17, e1008891 (2021).

    PubMed  PubMed Central  Google Scholar 

  45. Chen, Y., Li, F. & Nielsen, J. Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 22, 1–9 (2022).

    Google Scholar 

  46. The UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023).

  47. Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res 49, D545–D551 (2021).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Norsigian, C. J., Fang, X., Seif, Y., Monk, J. M. & Palsson, B. O. A workflow for generating multi-strain genome-scale metabolic models of prokaryotes. Nat. Protoc. https://doi.org/10.1038/s41596-019-0254-3 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 46, 7542–7553 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wang, H. et al. RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput. Biol. 14, e1006541 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. Davidi, D. et al. Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements. Proc. Natl Acad. Sci. USA 113, 3401–3406 (2016).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  53. Küken, A., Gennermann, K. & Nikoloski, Z. Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana. Plant J. 103, 2168–2177 (2020).

    PubMed  Google Scholar 

  54. Heckmann, D. et al. Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers. Proc. Natl Acad. Sci. USA 117, 23182–23190 (2020).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  55. Chen, Y. & Nielsen, J. In vitro turnover numbers do not reflect in vivo activities of yeast enzymes. Proc. Natl Acad. Sci. USA 118, 2021 (2021).

    Google Scholar 

  56. Xu, R., Razaghi-Moghadam, Z. & Nikoloski, Z. Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics https://doi.org/10.1093/BIOINFORMATICS/BTAB575 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Heckmann, D. et al. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat. Commun. 9, 5252 (2018).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  58. Schendel, F., Mueller, E., Stubbe, J., Shiau, A. & Smith, J. Formylglycinamide ribonucleotide synthetase from Escherichia coli: cloning, sequencing, overproduction, isolation, and characterization. Biochemistry 28, 2459–2471 (1989).

    CAS  PubMed  Google Scholar 

  59. Sánchez, B. J. et al. Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae. Proteomics 21, e2000093 (2021).

    PubMed  Google Scholar 

  60. Van Hoek, P., Van Dijken, J. P. & Pronk, J. T. Effect of specific growth rate on fermentative capacity of baker’s yeast. Appl. Environ. Microbiol. 64, 4226–4233 (1998).

    ADS  PubMed  PubMed Central  Google Scholar 

  61. Heublein, M. et al. The novel component Kgd4 recruits the E3 subunit to the mitochondrial α-ketoglutarate dehydrogenase. Mol. Biol. Cell 25, 3342–3349 (2014).

    PubMed  PubMed Central  Google Scholar 

  62. Sánchez, B. J., Li, F., Kerkhoven, E. J. & Nielsen, J. SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. BMC Syst. Biol. 13, 4 (2019).

    PubMed  PubMed Central  Google Scholar 

  63. Lewis, N. E. et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 6, 390 (2010).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The Novo Nordisk Foundation (grant no. NNF20CC0035580); the Knut and Alice Wallenberg Foundation; the European Union’s Horizon 2020 research and innovation program (grant agreements 720824 and 814650); the Research Council for Environment, Agricultural Sciences, and Spatial Planning (Formas, grant 2018-00597); and the Swedish Research Council (VR, grant 2019-04624).

Author information

Author notes

  1. These authors contributed equally: Yu Chen, Johan Gustafsson, Albert Tafur Rangel.

Authors and Affiliations

  1. Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden

    Yu Chen, Johan Gustafsson, Albert Tafur Rangel, Iván Domenzain, Cheewin Kittikunapong, Feiran Li, Le Yuan, Jens Nielsen & Eduard J. Kerkhoven

  2. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Chen

  3. Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, Lyngby, Denmark

    Albert Tafur Rangel & Eduard J. Kerkhoven

  4. Department of Life Sciences, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, Sweden

    Mihail Anton

  5. Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Feiran Li

  6. BioInnovation Institute, Copenhagen, Denmark

    Jens Nielsen

  7. SciLifeLab, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden

    Eduard J. Kerkhoven

Authors
  1. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johan Gustafsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Albert Tafur Rangel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mihail Anton
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iván Domenzain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cheewin Kittikunapong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Feiran Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Le Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jens Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Eduard J. Kerkhoven
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.G., A.T.R., M.A. and E.J.K. wrote the code. Y.C., J.G., A.T.R., M.A., I.D., C.K., F.L., L.Y. and E.J.K. contributed to the software design. Y.C., C.K. and F.L. tested the protocol. Y.C., J.G. and E.J.K. drafted the manuscript. A.T.R., M.A., I.D., C.K., F.L., L.Y. and J.N. edited the manuscript. E.J.K. supervise the project.

Corresponding author

Correspondence to Eduard J. Kerkhoven.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Domenzain, I. et al. Nat. Commun. 13, 3766 (2022): https://doi.org/10.1038/s41467-022-31421-1

Li, F. et al. Nat. Catal. 5, 662–672 (2022): https://doi.org/10.1038/s41929-022-00798-z

Sánchez, B. J. et al. Mol. Syst. Biol. 13, 935 (2017): https://doi.org/10.15252/msb.20167411

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Gustafsson, J., Tafur Rangel, A. et al. Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0. Nat Protoc 19, 629–667 (2024). https://doi.org/10.1038/s41596-023-00931-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-023-00931-7

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research