Times cited: 4170

Guimera, R, Amaral, LAN.
Nature 433 , 895 -900 (2005).

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High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks(1-3). Specifically, we demonstrate that we can find functional modules(4,5) in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartographic representation' of complex networks. Metabolic networks(6-8) are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability(9). We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.

  • Network cartography - Netcarto: Given a network, the program identifies modules (densely connected groups of nodes in the network) and classifies nodes according to their roles.
  • Network C libraries - RGraph: Source code of the C libraries for a variety of network calculations, including community finding using simulated annealing, network cartography, and link and network reliability.