Structural pattern discovery in protein-protein interaction networks

Abstract

Most proteins in a cell do not act in isolation, but carry out their function through interactions with other proteins. Elucidating these interactions is therefore central for our understanding of cellular function and organization. Recently, experimental techniques have been developed, which have allowed us to measure protein interactions on a genomic scale for several model organisms. These datasets have a natural representation as weighted graphs, also known as protein-protein interaction (PPI) networks. This chapter will present some recent advances in computational methods for the analysis of these networks, which are aimed at revealing their structural patterns. In particular, we shall focus on methods for uncovering modules that correspond to protein complexes, and on random graph models, which can be used to de-noise large scale PPI networks. In Sect. 23.1, the state-of-the-art techniques and algorithms are described followed by the definition of measures to assess the quality of the predicted complexes and the presentation of a benchmark of the detection algorithms on four PPI networks. Section 23.2 moves beyond protein complexes and explores other structural patterns of protein-protein interaction networks using random graph models.