Tracing history reveals increasing granularity in the community structure of evolving metabolic networks

Metabolism is considered a central driving force of matter and energy that controls the most fundamental chemical processes of life. It is driven by enzymes, proteinaceous catalysts that increase the rate of chemical reactions, which under ordinary conditions may not take place in the molecular environment of the cell. In a recent study, Mughal and Caetano-Anollés travel back in time by tracing metabolic evolution using methods of phylogenomic retrodiction. In order to achieve this objective, they updated the metabolic Molecular Ancestry Network (MANET) database, which traces historical information of the structural domains of enzymes when these are defined at the SCOP fold level of structural classification. Since the fold family level of SCOP carries a more informative phylogenetic signal than the fold level, the updated MANET database provides a more accurate depiction of the origins and evolution of metabolism. The update uncovers unanticipated patterns of enzyme recruitment operating at global levels and reveals the gradual rise of hierarchy and community structure in evolving networks. Remarkably, these increasing evolutionary constraints on structure were stronger at lower levels of metabolic organization. Thus, evolving metabolic network structure uncovers a ‘principle of granularity’, an evolutionary increase of the cohesiveness of lower-level parts of a hierarchical system. This principle that has been uncovered in metabolism supports the prediction of Herbert A. Simon, the father of complex systems theory, that "Each of the parts of a nearly-decomposable system has strong internal links among its sub-parts, but the several top-level parts are bound together with each other only by comparatively weak linkages". The weak linkage at higher levels of organization provides the flexibility needed for biological innovation.

Mughal F, Caetano-Anolles G (2019) MANET 3.0: Hierarchy and modularity in evolving metabolic networks. PLoS ONE 14(10):e0224201

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