Local graph alignment and motif search in biological networks

TL;DR

This paper introduces a novel graph alignment method for detecting topological motifs in biological networks, aiding the understanding of molecular functions from network topology.

Contribution

It presents a statistical model and scoring function for motifs based on similar but not identical patterns, along with a graph alignment algorithm for biological network analysis.

Findings

Applied to E. coli gene regulation network

Identified significant topological motifs

Demonstrated effectiveness of the alignment algorithm

Abstract

Interaction networks are of central importance in post-genomic molecular biology, with increasing amounts of data becoming available by high-throughput methods. Examples are gene regulatory networks or protein interaction maps. The main challenge in the analysis of these data is to read off biological functions from the topology of the network. Topological motifs, i.e., patterns occurring repeatedly at different positions in the network have recently been identified as basic modules of molecular information processing. In this paper, we discuss motifs derived from families of mutually similar but not necessarily identical patterns. We establish a statistical model for the occurrence of such motifs, from which we derive a scoring function for their statistical significance. Based on this scoring function, we develop a search algorithm for topological motifs called graph alignment, a procedure with some analogies to sequence alignment. The algorithm is applied to the gene regulation network of E. coli.