Network reduction methods for genome-scale metabolic models.

Abstract

Genome-scale metabolic models (GSMs) provide a comprehensive representation of cellular metabolism. GSMs provide a mechanistic link between cellular genotypes and metabolic phenotypes, and are thus widely used to analyze metabolism at the systems level. GSMs consist of hundreds or thousands of reactions. They have thus largely been analyzed with computationally efficient constraint-based methods such as flux-balance analysis, limiting their scope and phenotype prediction accuracy. Computationally more demanding but potentially more informative methods, such as kinetic and dynamic modeling, are currently limited to small or medium-sized models. Thus, it is desirable to achieve unbiased stoichiometric reductions of large-scale metabolic models to small, coarse-grained model representations that capture significant metabolic modules. Here, we review published automated and semiautomated methods used for large-scale metabolic model reduction. The top-down methods discussed provide minimal networks that retain a set of user-protected phenotypes, but may reduce the model's metabolic and phenotypic versatility. In contrast, the two bottom-up approaches reviewed retain a more unbiased set of phenotypes; at the same time, these methods require the partitioning of the GSM into metabolic subsystems by the user, and make strong assumptions on the subsystems' connections and their states, respectively.