Complexity and robustness in hypernetwork models of metabolism. (7th October 2016)

• Record Type: Journal Article
• Authors: Pearcy, Nicole; Chuzhanova, Nadia; Crofts, Jonathan J.
• Is Part Of: Journal of theoretical biology. Volume 406(2016)
• Page Start: 99
• Page End: 104
• Publication Date: 2016-10-07
• Subjects: Complexity -- Hypernetworks -- Metabolism -- Evolution; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2016.06.032
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity. Abstract : Highlights: We employ a hypernetwork formalism to model chemical reaction networks of bacteria. We extend the ideas of network percolation to hypernetworks as a method for quantifying hypernetwork robustness. 115 metabolic networks of bacteria are studied. We find that percolation thresholds are significantly correlated with the amount of variability present within a species habitat, implying that hypernetwork complexity increases with habitat variability.