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Abstract: Biological networks have evolved to be highly functional within uncertainenvironments while remaining extremely adaptable. One of the main contributorsto the robustness and evolvability of biological networks is believed to betheir modularity of function, with modules defined as sets of genes that arestrongly interconnected but whose function is separable from those of othermodules. Here, we investigate the in silico evolution of modularity androbustness in complex artificial metabolic networks that encode an increasingamount of information about their environment while acquiring ubiquitousfeatures of biological, social, and engineering networks, such as scale-freeedge distribution, small-world property, and fault-tolerance. These networksevolve in environments that differ in their predictability, and allow us tostudy modularity from topological, information-theoretic, and gene-epistaticpoints of view using new tools that do not depend on any preconceived notion ofmodularity. We find that for our evolved complex networks as well as for theyeast protein-protein interaction network, synthetic lethal pairs consistmostly of redundant genes that lie close to each other and therefore withinmodules, while knockdown suppressor pairs are farther apart and often straddlemodules, suggesting that knockdown rescue is mediated by alternative pathwaysor modules. The combination of network modularity tools together with geneticinteraction data constitutes a powerful approach to study and dissect the roleof modularity in the evolution and function of biological networks.

Author: Arend Hintze, Christoph Adami (KGI)

Source: https://arxiv.org/

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