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.

High-throughput generation, optimization and analysis of genome-scale metabolic models

Abstract

Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Model SEED genome-scale metabolic reconstruction pipeline.
Figure 2: Properties of SEED models organized by taxonomy.
Figure 3: Properties of SEED models predicted using FBA.
Figure 4: Accuracy of models generated by the Model SEED pipeline.

References

  1. Yus, E. et al. Impact of genome reduction on bacterial metabolism and its regulation. Science 326, 1263–1268 (2009).

    CAS  Article  Google Scholar 

  2. Kumar, V.S. & Maranas, C.D. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput. Biol. 5, e1000308 (2009).

    Article  Google Scholar 

  3. Feist, A.M. & Palsson, B.O. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat. Biotechnol. 26, 659–667 (2008).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  5. Overbeek, R., Disz, T. & Stevens, R. The SEED: A peer-to-peer environment for genome annotation. Commun. ACM 47, 46–51 (2004).

    Article  Google Scholar 

  6. Aziz, R.K. et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9, 75 (2008).

    Article  Google Scholar 

  7. DeJongh, M. et al. Toward the automated generation of genome-scale metabolic networks in the SEED. BMC Bioinformatics 8, 139 (2007).

    Article  Google Scholar 

  8. Jankowski, M.D., Henry, C.S., Broadbelt, L.J. & Hatzimanikatis, V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys. J. 95, 1487–1499 (2008).

    CAS  Article  Google Scholar 

  9. Henry, C.S., Zinner, J., Cohoon, M. & Stevens, R. iBsu1103: a new genome scale metabolic model of B. subtilis based on SEED annotations. Genome Biol. 10, R69 (2009).

    Article  Google Scholar 

  10. Suthers, P.F. et al. A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189. PLOS Comput. Biol. 5, e1000285 (2009).

    Article  Google Scholar 

  11. Notebaart, R.A., van Enckevort, F.H., Francke, C., Siezen, R.J. & Teusink, B. Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7, 296 (2006).

    Article  Google Scholar 

  12. Tsoka, S., Simon, D. & Ouzounis, C.A. Automated metabolic reconstruction for Methanococcus jannaschii. Archaea 1, 223–229 (2004).

    CAS  Article  Google Scholar 

  13. Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A.C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).

    Article  Google Scholar 

  14. Pramanik, J. & Keasling, J.D. Effect of Escherichia coli biomass composition on central metabolic fluxes predicted by a stoichiometric model. Biotechnol. Bioeng. 60, 230–238 (1998).

    CAS  Article  Google Scholar 

  15. Satish Kumar, V., Dasika, M.S. & Maranas, C.D. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 212 (2007).

    Article  Google Scholar 

  16. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  Article  Google Scholar 

  17. Kanehisa, M., Goto, S., Kawashima, S. & Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42–46 (2002).

    CAS  Article  Google Scholar 

  18. Feist, A.M. et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, 121 (2007).

    Article  Google Scholar 

  19. Reed, J.L., Vo, T.D., Schilling, C.H. & Palsson, B.O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54 (2003).

    Article  Google Scholar 

  20. Durot, M. et al. Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data. BMC Syst. Biol. 2, 85 (2008).

    Article  Google Scholar 

  21. Oh, Y.K., Palsson, B.O., Park, S.M., Schilling, C.H. & Mahadevan, R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem. 282, 28791–28799 (2007).

    CAS  Article  Google Scholar 

  22. Goelzer, A. et al. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis. BMC Syst. Biol. 2, 20 (2008).

    Article  Google Scholar 

  23. Schilling, C.H. et al. Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol. 184, 4582–4593 (2002).

    CAS  Article  Google Scholar 

  24. Oliveira, A.P., Nielsen, J. & Forster, J. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol. 5, 39 (2005).

    Article  Google Scholar 

  25. Feist, A.M., Scholten, J.C., Palsson, B.O., Brockman, F.J. & Ideker, T. Modeling methanogenesis with a genome-scale metabolic reconstruction of. Methanosarcina barkeri. Mol. Syst. Biol. 2, 2006 0004 (2006).

    PubMed  Google Scholar 

  26. Jamshidi, N. & Palsson, B.O. Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst. Biol. 1, 26 (2007).

    Article  Google Scholar 

  27. Nogales, J., Palsson, B.O. & Thiele, I. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst. Biol. 2, 79 (2008).

    Article  Google Scholar 

  28. Duarte, N.C., Herrgard, M.J. & Palsson, B.O. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14, 1298–1309 (2004).

    CAS  Article  Google Scholar 

  29. Becker, S.A. & Palsson, B.O. Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol. 5, 8 (2005).

    Article  Google Scholar 

  30. Douglas, A.E. Nutritional interactions in insect-microbial symbioses: aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43, 17–37 (1998).

    CAS  Article  Google Scholar 

  31. Mahadevan, R. & Schilling, C.H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).

    CAS  Article  Google Scholar 

  32. Konstantinidis, K.T. & Tiedje, J.M. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc. Natl. Acad. Sci. USA 101, 3160–3165 (2004).

    CAS  Article  Google Scholar 

  33. von Eiff, C. et al. Phenotype microarray profiling of Staphylococcus aureus menD and hemB mutants with the small-colony-variant phenotype. J. Bacteriol. 188, 687–693 (2006).

    CAS  Article  Google Scholar 

  34. Bochner, B.R. Global phenotypic characterization of bacteria. FEMS Microbiol. Rev. 33, 191–205 (2009).

    CAS  Article  Google Scholar 

  35. Keymer, D.P., Miller, M.C., Schoolnik, G.K. & Boehm, A.B. Genomic and phenotypic diversity of coastal Vibrio cholerae strains is linked to environmental factors. Appl. Environ. Microbiol. 73, 3705–3714 (2007).

    CAS  Article  Google Scholar 

  36. Gerdes, S. et al. Essential genes on metabolic maps. Curr. Opin. Biotechnol. 17, 448–456 (2006).

    CAS  Article  Google Scholar 

  37. Zhang, R., Ou, H.Y. & Zhang, C.T. DEG: a database of essential genes. Nucleic Acids Res. 32, D271–D272 (2004).

    CAS  Article  Google Scholar 

  38. Nakahigashi, K. et al. Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism. Mol. Syst. Biol. 5, 306 (2009).

    Article  Google Scholar 

  39. Karp, P.D., Riley, M., Paley, S.M. & Pellegrini-Toole, A. The MetaCyc Database. Nucleic Acids Res. 30, 59–61 (2002).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the US Department of Energy under contract DE-ACO2-06CH11357, by the National Institute of Allergy and Infectious Diseases under contract HHSN266200400042C and by the National Science Foundation under grants MCB-0745100, CCF-0829929 and DBI-0850546. M.D. was also supported by the Argonne National Laboratory Guest Faculty Program and the United States Fulbright Scholarship Program. We acknowledge the SEED annotators and development team for producing the annotations and computational infrastructure that make this work possible. We thank R. Overbeek, V. Vonstein, R. Olson, T. Disz, S. Devoid, F. Xia and T. Paczian for assistance with the use of the SEED genome annotation and analysis tools.

Author information

Authors and Affiliations

Authors

Contributions

C.S.H. developed and operated the Model SEED resource and the related algorithms with assistance from all other authors. All authors participated in curation and testing of the SEED models. M.D. contributed significantly to reaction network annotation, model testing and model curation. A.A.B. contributed significantly to template biomass reaction design, model curation and selection of organisms for reconstruction. P.M.F. contributed to reaction network annotation. R.L.S. contributed to model curation and the design of pipeline and algorithms. B.L. contributed to Model SEED web interface development. All authors contributed to the writing and revision of the manuscript.

Corresponding author

Correspondence to Christopher S Henry.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Methods and Supplementary Fig. 1 (PDF 676 kb)

Supplementary Tables

Supplementary Tables S1–S9 (XLS 4084 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Henry, C., DeJongh, M., Best, A. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28, 977–982 (2010). https://doi.org/10.1038/nbt.1672

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1672

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing