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

While genomics gets all the publicity, the next most important step is figuring out how proteins are produced and used into what is called metabolic networks. It is actually a key toward making personalized medicine a reality. A few readers might remember these entry entitled: Instances of Null Spaces: Can Compressive Sensing Help Study Non Steady State Metabolic Networks ?. followed by And so it begins ... Compressive Genomics, Well today's paper that has a co-author from the latter paper look at the first problem by figuring out specific metabolic networks using optimization tools.

An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models by Leonid Chindelevitch, Jason Trigg, Aviv Regev & Bonnie Berger

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.

   

An implementation of MONGOOSE is available from here.

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