Learning the structure of protein-protein interaction networks

Modeling and analyzing protein-protein interaction (PPI) networks is an important problem in systems biology. Many random graph models were proposed to capture specific network properties or mimic the way real PPI networks might have evolved. In this paper we introduce a new generative model for PPI networks which is based on geometric random graphs and uses the whole connectivity information of the real PPI networks to learn their structure. Using only the high confidence part of yeast S. cerevisiae PPI network for training our new model, we successfully reproduce structural properties of other lower-confidence yeast, as well as of human PPI networks coming from different data sources. Thus, our new approach allows us to utilize high quality parts of currently available PPI data to create accurate models for PPI networks of different species.