Understanding the dependence and interplay between architecture and function in biological networks has great relevance to disease progression, biological fabrication, and biological systems in general. We propose methods to assess the association of various microbe characteristics and phenotypes with the topology of their networks. We adopt an automated approach to characterize metabolic networks of 32 microbial species using 11 topological metrics from complex networks. Clustering allows us to extract the indispensable, independent, and informative metrics. Using hierarchical linear modeling, we identify relevant subgroups of these metrics and establish that they associate with microbial phenotypes surprisingly well. This work can serve as a stepping stone to cataloging biologically relevant topological properties of networks and toward better modeling of phenotypes. The methods we use can also be applied to networks from other disciplines.

• Received 31 July 2009

DOI:https://doi.org/10.1103/PhysRevE.80.040902