A Bayesian approach to the evolution of metabolic networks on a phylogeny.

Journal article

The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions) or complex (incorporating dependencies among reactions) stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.

Authors: Mithani, A; Preston, G; Hein, J

• Publication status: Published
• Journal: PLoS computational biology
• Volume: 6
• Issue: 8
• Pages: 861-864
• Publication date: 2010-01-01
• DOI: 10.1371/journal.pcbi.1000868
• EISSN: 1553-7358
• ISSN: 1553-734X
• Language: English
• Keywords: Computer Simulation; Models, Biological; Metabolic Networks and Pathways; Models, Statistical; Phylogeny; Genome, Bacterial; Pseudomonas; Biological Evolution; Bayes Theorem