Strong associations between microbe phenotypes and their network architecture

Abstract

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 towards better modeling of phenotypes. The methods we use can also be applied to networks from other disciplines.

Replaced by the version scheduled to appear in Phys. Rev. E (Rapid Comm.)

References in corpus (6)

• The large-scale organization of metabolic networks
• Hierarchical structure and the prediction of missing links in networks
• The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
• Community structure in directed networks
• Extracting the hierarchical organization of complex systems
• Modeling and verifying a broad array of network properties

Cited by in corpus (5)

• Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in complex networks
• Slow poisoning and destruction of networks: Edge proximity and its implications for biological and infrastructure networks
• Using complex networks towards information retrieval and diagnostics in multidimensional imaging
• Key to Network Controllability
• Topology dependent payoffs can lead to escape from prisoner's dilemma