curated databases (Table 2) all protein–protein interaction data with at least one publication support to build an integrated protein–protein interaction network. For the mouse network, because the seven databases only contained a limited number of interactions, we first used an ortholog-based method (18) to infer mouse interactions from curated human interactions and then combined them with curated mouse interactions. We implemented—with some modifications—the method previously published by Sales-Pardo et al. (19) to identify hierarchical modules from the integrated networks. Although a standard hierarchical clustering is able to reveal hierarchical structure of a network, it does not specify relevant hierarchical levels and modules at different scales. Moreover, it does not assess the statistical significance of the modular organization of a network. Our implementation directly addresses these limitations. Here we briefly describe our network clustering method. A detailed description of our implementation will be included in a separate manuscript. First, using the random walk-based walktrap algorithm (20), we identify the best partition of the network by maximizing the modularity score (21); Secondly, we use the edge switching algorithm (22) to generate 1000 random networks with the same attributes as the protein–protein interaction network and then identify the best partition and corresponding modularity
