Incorporating Knowledge of Topology Improves Reconstruction of Interaction Networks from Microarray Data

Abstract

Reconstruction of biological interaction networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These networks have specific topologies, whose characteristics are defined by evolutionary relationships between proteins and the physical limitations imposed on proteins interacting in three-dimensional space. In this study, a method is proposed applying the topology of known biological networks to the analysis of microarray data for protein-protein binding interactions. In this method, genomic biological networks are derived from the body of published scientific literature. The numbers of interacting neighbors for proteins of specific molecular functions are observed. That information is used in the analysis of microarray expression data to regenerate biological networks using a rank-based algorithm, Gene Ontology Restricted Value Neighborhood (GRV-N). The results of this analysis demonstrate that incorporating knowledge of network topology improves the ability of expression analysis to reconstruct interaction networks with a high degree of biological relevance.