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

Nat Commun

1] Mathematics Department, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, USA [2] Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.

Published: October 2014

AI Article Synopsis

  • Constraint-based models allow for genome-wide studies of metabolism, using flux balance analysis to interpret data.
  • The analysis results differ between software applications, which can be fixed by employing exact arithmetic instead of floating-point arithmetic.
  • MONGOOSE, a new analysis toolbox, reveals that nearly half of 98 metabolic models have blocked biomass reactions and provides methods to address this, enhancing the understanding of metabolic networks' structure and functionality.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4205847PMC
http://dx.doi.org/10.1038/ncomms5893DOI Listing

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