A statistical mechanics description of environmental variability in metabolic networks

TL;DR

This paper applies a statistical mechanics framework to analyze how environmental variability influences the structural organization and disequilibrium of bacterial metabolic networks, introducing new measures for network analysis.

Contribution

It introduces a novel approach using statistical mechanics to quantify disequilibrium and a new directed centrality measure for metabolic network analysis.

Findings

Environmental variability correlates with decreased equilibrium constants in bacterial metabolic networks.

Metabolic networks exhibit increased disequilibrium in more variable habitats.

New centrality measure characterizes metabolite participation in pathways.

Abstract

Many of the chemical reactions that take place within a living cell are irreversible. Due to evolutionary pressures, the number of allowable reactions within these systems are highly constrained and thus the resulting metabolic networks display considerable asymmetry. In this paper, we explore possible evolutionary factors pertaining to the reduced symmetry observed in these networks, and demonstrate the important role environmental variability plays in shaping their structural organization. Interpreting the returnability index as an equilibrium constant for a reaction network in equilibrium with a hypothetical reference system, enables us to quantify the extent to which a metabolic network is in disequilibrium. Further, by introducing a new directed centrality measure via an extension of the subgraph centrality metric to directed networks, we are able to characterise individual metabolites by their participation within metabolic pathways. To demonstrate these ideas, we study 116 metabolic networks of bacteria. In particular, we find that the equilibrium constant for the metabolic networks decreases significantly in-line with variability in bacterial habitats, supporting the view that environmental variability promotes disequilibrium within these biochemical reaction systems.