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An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models

Author

Listed:
  • Leonid Chindelevitch

    (Computer Science and Artificial Intelligence Laboratory, MIT
    Broad Institute, 7 Cambridge Center)

  • Jason Trigg

    (Computer Science and Artificial Intelligence Laboratory, MIT)

  • Aviv Regev

    (Broad Institute, 7 Cambridge Center
    Howard Hughes Medical Institute
    MIT)

  • Bonnie Berger

    (Computer Science and Artificial Intelligence Laboratory, MIT
    Broad Institute, 7 Cambridge Center)

Abstract

Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations.

Suggested Citation

  • Leonid Chindelevitch & Jason Trigg & Aviv Regev & Bonnie Berger, 2014. "An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5893
    DOI: 10.1038/ncomms5893
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    Cited by:

    1. Jared T. Guilbeau & Md. Istiaq Hossain & Sam D. Karhbet & Ralph Baker Kearfott & Temitope S. Sanusi & Lihong Zhao, 2017. "A review of computation of mathematically rigorous bounds on optima of linear programs," Journal of Global Optimization, Springer, vol. 68(3), pages 677-683, July.
    2. Ambros M. Gleixner & Daniel E. Steffy & Kati Wolter, 2016. "Iterative Refinement for Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 449-464, August.

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