Trade-offs between efficiency and robustness in bacterial metabolic networks are associated with niche breadth.
The relation between structure and function in biologic networks is a central point of systems biology research. Key functional features--notably, efficiency and robustness--are linked to the topologic structure of a network, and there appears to be a degree of trade-off between these features, i.e., simulation studies indicate that more efficient networks tend to be less robust. Here, we investigate this issue in metabolic networks from 105 lineages of bacteria having a wide range of ecologies. We take quantitative measurements on each network and integrate this network data with ecologic data using a phylogenetic comparative model. In this setting, we find that biologic conclusions obtained with classical phylogenetic comparative methods are sensitive to correlations between model covariates and phylogenetic branch length. To avoid this problem, we propose a revised statistical framework--hierarchical mixed-effect regression--to accommodate phylogenetic nonindependence. Using this approach, we show that the cartography of metabolic networks does indeed reflect a trade-off between efficiency and robustness. Furthermore, ecologic characteristics related to niche breadth are strong predictors of network shape. Given the broad variation in niche breadth seen among species, we predict that there is no universally optimal balance between efficiency and robustness in bacterial metabolic networks and, thus, no universally optimal network structure. These results highlight the biologic relevance of variation in network structure and the potential role of niche breadth in shaping metabolic strategies of efficiency and robustness.
Department of Biology, Dalhousie University, Halifax, NS, B3H-4J1, Canada. melissa.morine@ucd.ie
Melissa J
MJ
Morine
Hong
H
Gu
Ransom A
RA
Myers
Joseph P
JP
Bielawski
D001419:Bacteria / Q000378:metabolism*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D010802:Phylogeny; D012044:Regression Analysis