Functional cartography of complex metabolic networks.
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. Specifically, we demonstrate that we can find functional modules 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 are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. 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.
Guimerà
Roger
R
NICO and Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Nunes Amaral
Luís A
LA
D000255:Adenosine Triphosphate / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D001105:Archaea / Q000378:metabolism*; D001419:Bacteria / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D004926:Escherichia coli / Q000378:metabolism; D005057:Eukaryotic Cells / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological
D000255:Adenosine Triphosphate
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