Open AccessMetabolic modeling of endosymbiont genome reduction on a temporal scale
Author(s) -
Yizhak Keren,
Tuller Tamir,
Papp Balázs,
Ruppin Eytan
Publication year - 2011
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1038/msb.2011.11
Subject(s) - biology , buchnera , in silico , metabolic network , genome , gene , gene regulatory network , systems biology , computational biology , phylogenetic tree , process (computing) , flux balance analysis , selection (genetic algorithm) , scale (ratio) , phylogenetics , evolutionary biology , genetics , machine learning , computer science , gene expression , physics , quantum mechanics , operating system
A fundamental challenge in Systems Biology is whether a cell‐scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola . We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy ( R =0.54). The model's network‐based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.