Clustering Methods in a Protein–Protein Interaction Network

With completion of a draft sequence of the human genome, the field of gen etics stands on the threshold of significant theoretical and practical advances. Crucial to furthering these investigations is a comprehensive understanding of the expression, function, and regulation of the proteins encoded by an organism. It has been observed that proteins seldom act as single isolated species in the performance of their functions; rather, proteins involved in the sam e cellular processes often interact with each other. Therefore, the functions of uncharacterized proteins can be predicted through comparison with the interactions of similar known proteins. A detailed examination of the protein-protein interaction (PPI) network can thus yield significant new un derstanding of protein function. Clustering is the process of grouping data objects into sets (cluste rs) which demonstrate greater similarity among objects in the same cluster than in different clusters . Clustering in the PPI network context groups together proteins which share a larger number of interactions. The results of this process can illuminate the structure of the PPI network and suggest possible functions for members of the cluster which were previously uncharacterized. This chapter will begin with a brief introduction of the properties of protein-p rotein interaction networks, including a review of the data which has been generated by both experimental and computational approaches. A variety of methods which have been employed to cluster these networks will then be presented. These approaches are broadly characterized as either distance-based or