Fitting a geometric graph to a protein-protein interaction network

Motivation: Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm...

• Type: Journal
• Year: 2008
• Where: BIOINFORMATICS
• Authors: Desmond J. Higham, Marija Rasajski, Natasa Przulj
• Keywords: BIOINFORMATICS 2008, Geometric Random Graphs, Metric Space, PPI Networks