Modular co-evolution of metabolic networks

Abstract

The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear. In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do. The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

26 pages, 7 figures

References in corpus (10)

• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• Hierarchical organization of modularity in metabolic networks
• Functional cartography of complex metabolic networks
• Specificity and stability in topology of protein networks
• Subnetwork hierarchies of biochemical pathways
• Pattern Detection in Complex Networks: Correlation Profile of the Internet
• Core-periphery organization of complex networks
• Currency and commodity metabolites: Their identification and relation to the modularity of metabolic networks
• Probabilistic Clustering of Sequences: Inferring new bacterial regulons by comparative genomics