Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution

Eduardo Corel; Philippe Lopez; Raphaël Méheust; Eric Bapteste

doi:10.1016/j.tim.2015.12.003

Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution

Trends Microbiol. 2016 Mar;24(3):224-237. doi: 10.1016/j.tim.2015.12.003. Epub 2016 Jan 13.

Authors

Eduardo Corel¹, Philippe Lopez², Raphaël Méheust², Eric Bapteste²

Affiliations

¹ Equipe AIRE, UMR 7138, Laboratoire Evolution Paris-Seine, Université Pierre et Marie Curie, 7 quai St Bernard 75005 Paris, France. Electronic address: eduardo.corel@upmc.fr.
² Equipe AIRE, UMR 7138, Laboratoire Evolution Paris-Seine, Université Pierre et Marie Curie, 7 quai St Bernard 75005 Paris, France.

Abstract

The tree model and tree-based methods have played a major, fruitful role in evolutionary studies. However, with the increasing realization of the quantitative and qualitative importance of reticulate evolutionary processes, affecting all levels of biological organization, complementary network-based models and methods are now flourishing, inviting evolutionary biology to experience a network-thinking era. We show how relatively recent comers in this field of study, that is, sequence-similarity networks, genome networks, and gene families-genomes bipartite graphs, already allow for a significantly enhanced usage of molecular datasets in comparative studies. Analyses of these networks provide tools for tackling a multitude of complex phenomena, including the evolution of gene transfer, composite genes and genomes, evolutionary transitions, and holobionts.

Keywords: bipartite graph; evolution; gene transfer; graph theory; introgression; symbiosis.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Bacteria / genetics*
Evolution, Molecular*
Gene Regulatory Networks*
Gene Transfer, Horizontal
Genome
Models, Genetic*
Symbiosis