Abstract
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.
Highlights
Biological networks are under continuous evolution and their evolution is one of the major areas of research today [1,2,3,4,5,6]
Most organisms have a common set of reactions as a part of their metabolic networks that relate to essential processes such as generation of energy and the synthesis of important biological molecules, which are required for their survival
We use a stochastic approach to study the evolution of metabolic networks and show that evolutionary inferences can be made using the structure of these networks
Summary
Biological networks are under continuous evolution and their evolution is one of the major areas of research today [1,2,3,4,5,6]. The evolution of biological networks can be studied using various approaches such as maximum likelihood and parsimony [7,8]. The maximum likelihood approach calculates the likelihood of evolution of one network into another by summing over all possible networks that can occur during the course of evolution under the given model. The evolution of biological networks has been studied using stochastic approaches where efficient sampling techniques makes the problem computationally tractable. Wiuf et al [5] used importance sampling to approximate the likelihood and estimate parameters for the growth of protein networks under a duplicate attachment model. The authors approximated the posterior distribution of the model parameters for network growth using a Markov Chain Monte Carlo algorithm
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