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

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.

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

“…The total time (12,599 seconds ≈ 3.5 hours) is modest compared to an expected time of months for the exact solver approach of ref. 16.…”

“…The solution time was about two weeks, compared to a few minutes for a standard double-precision solver, but the latter’s final objective value had only one correct digit. QSopt_ex has since been applied to a collection of 98 metabolic models by Chindelvitch et al 16. via their MONGOOSE toolbox.…”

“…Most of the 98 models have less than 1000 metabolites and reactions. QSopt_ex required about a day to solve all models16, compared to a few seconds in total for a standard solver.…”

Computational biology in the 21st century

“…3 While standard network science tools have been employed in these analyses—for example, several approaches make use of diffusion or random walks to explore the topology of networks 9,11 —they are often paired with more specific biological data, as seen in IsoRank 32 and IsoRankN’s 21 use of conserved biological function in addition to random walks for global multiple network alignment. Other tools solve other biological problems, such as MONGOOSE, 10 which analyzes metabolic networks. However, given its breadth, biological network science is beyond the scope of this article.…”

A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation

“…Recently, more than 1000 prokaryotic genomes have been fully sequenced, thus allowing FBA models to incorporate also information on enzymes and genome, including the relationships among genes, proteins and reactions (GPR mapping). To date, more than 90 genome-wide metabolic reconstructions have been published (12 ).…”