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

Nature Biotechnology volume 28, pages 977–982 (2010)Cite this article

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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.

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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.

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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.

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Authors and Affiliations

  1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA

    Christopher S Henry

  2. Department of Computer Science, Hope College, Holland, Michigan, USA

    Matthew DeJongh & Paul M Frybarger

  3. Department of Biology, Hope College, Holland, Michigan, USA

    Aaron A Best & Paul M Frybarger

  4. Computer Science Department and Computation Institute, University of Chicago, Chicago, Illinois, USA

    Ben Linsay & Rick L Stevens

  5. Computing, Environment, and Life Sciences Directorate, Argonne National Laboratory, Argonne, Illinois, USA

    Rick L Stevens

Authors
  1. Christopher S Henry
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  2. Matthew DeJongh
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  4. Paul M Frybarger
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  6. Rick L Stevens
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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.

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Correspondence to Christopher S Henry.

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

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Supplementary Tables S1–S9 (XLS 4084 kb)

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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

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