Enumeration of Condition-Specific Dense Modules in Protein Interaction Networks

Modern systems biology aims at understanding how the different molecular components of a biological cell interact. Often, cellular functions are performed by complexes consisting of many different proteins. The composition of these complexes may change according to the cellular environment, and one protein may be involved in several different processes. The automatic discovery of functional modules in protein interaction data is challenging. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically mine for dense modules with interesting profiles. Given a weighted protein interaction network, our method discovers all modules that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way. Our experiments show that the novel approach is feasible and produces biologically meaningful results. In validation studies using yeast data, the new method achieved a higher coverage than clique-based approaches, while maintaining a high reliability level. Moreover, we enhanced the yeast network by phenotypic and phylogenetic profiles and the human network by tissue-specific expression data to investigate profile-consistent modules. The resulting sets of modules revealed condition-specific reorganization of complexes as well as inter-complex associations