Reconstruction and evolution of metabolic networks across a phylogenetic tree

Abstract

Due to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a first global probabilistic approach to annotate genome‐scale metabolic networks that integrates sequence homology and context‐based correlations under a single principled framework. The developed method for Global Biochemical reconstruction Using Sampling (GLOBUS) not only provides annotation probabilities for each functional assignment, but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote sequence identity to known enzymes. Taking advantage of the developed methods for metabolic reconstruction, we performed a comparative study of the genotype to phenotype relationship in many hundreds of different species throughout the bacterial phylogenetic tree. Our results, which are in excellent agreement with available experimentally measured phenotypes, reveal significant differences in metabolic properties of closely related strains, and a marked contribution of bacterial lifestyle to phenotypic diversity. The analysis allows an understanding of phenotype‐to‐genotype relationships, and their evolution, on a scale not currently accessible to experimental approaches.