  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 one can (i) find functional modules in
complex networks, and (ii) 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
more potential for immediate applicability. We use our method to analyze the
metabolic networks of twelve organisms from three different super-kingdoms. 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
low-degree metabolites that connect different modules are more conserved than
hubs whose links are mostly within a single module.
