Evolution of complex modular biological networks

TL;DR

This paper explores how artificial metabolic networks evolve modularity and robustness in varying environments, revealing insights into gene interactions and network structure that mirror biological systems.

Contribution

It introduces new tools to analyze network modularity without prior assumptions and applies them to both artificial and biological networks, linking topology and genetic interactions.

Findings

Redundant genes tend to be within modules, forming synthetic lethal pairs.

Suppressor pairs often span modules, indicating alternative pathways.

Network modularity correlates with robustness and evolvability.

Abstract

Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.