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

Leonid Chindelevitch, Jason Trigg, Aviv Regev and Bonnie Berger ()
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Leonid Chindelevitch: Computer Science and Artificial Intelligence Laboratory, MIT
Jason Trigg: Computer Science and Artificial Intelligence Laboratory, MIT
Aviv Regev: Broad Institute, 7 Cambridge Center
Bonnie Berger: Computer Science and Artificial Intelligence Laboratory, MIT

Nature Communications, 2014, vol. 5, issue 1, 1-9

Abstract: 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.

Date: 2014
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DOI: 10.1038/ncomms5893

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