Scalable metabolic pathway analysis

Abstract

The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them efficiently using iterative minimization and a simple graph representation of MPs. These methods outperform the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia colis central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.