Joint clustering of protein interaction networks by block modeling

Identification of functional modules in protein protein interaction (PPI) networks may help better understand cell functions. Many existing computational methods focus on identifying modules based on either individual PPI networks or protein sequence similarities within the species. As both interaction data and sequence similarities may not be either complete or accurate with respect to revealing protein functionalities, we propose a joint clustering framework based on block modeling to integrate the available information across different species to utilize both protein interaction data and sequence similarities. The motivation is to borrow strengths from multiple data sources for more accurate module identification as evolutionally different species may share similar cellular organization. Our blockmodel joint clustering enables the identification of not only densely connected modules but also those modules containing proteins with similar interaction patterns to the rest of the networks. We develop a simulated annealing (SA) algorithm based on Potts-Models for the blockmodel problem to solve the non-convex combinatorial optimization. Our method is validated using synthetic networks as well as yeast and fruit fly PPI networks. The experimental results conclude that joint clustering outperforms clustering of individual networks separately.