Evolutionary significance of metabolic network properties

Abstract

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein–protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.

Control of fluxes in metabolic networks

“…To determine whether the flux coupling profiles reflect functionally important features of the analyzed metabolic networks, we assess their statistical significance using network randomization under mass-balance constraints (Basler et al 2011). We find that the flux coupling profiles for most networks and coupling types are significantly different from their randomized variants, suggesting that they reflect evolutionary features of their metabolism (Methods; Basler et al 2012).…”

Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved towards Low-Entropy Dynamics

“…More recent work attempts to link topological properties with biological function [41,42], particularly with enzyme essentiality and flux organization [43][44][45]. However, the design principles of metabolic networks and, particularly, dynamic processes as essential elements in shaping the topology of metabolic networks, are far from being understood (see, e.g., [46]). Metabolic networks are at the same time scale-free, modular, layered and bipartite.…”

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

“…The main idea being that physico-chemical constraints are part of the null model so that any remaining deviations must result from evolutionary pressure and are thus worth studying. Basler et al [42] discuss a number of structural properties that show changed results when compared with this new null model. Some dynamical systems are highly sensitive to motifs as small functional devices.…”

“…In recent publications, Basler et al [42,47] have presented and motivated a novel method for generating chemically sensible, mass-balanced randomized metabolisms. The main idea being that physico-chemical constraints are part of the null model so that any remaining deviations must result from evolutionary pressure and are thus worth studying.…”

“…Ignoring any hierarchy of functional modules, the uptake reactions, reactions contributing to biomass generation and all the reactions in-between result in a layered organization of metabolism. These complications present a formidable challenge in any attempt to explore the interrelations between network topology and dynamical function for metabolic systems [41,42].…”