Modular Algorithms for Biomolecular Network Alignment

Abstract

Comparative analyses of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. The rapidly advancing field of systems biology aims to understand the structure, function, dynamics, and evolution of complex biological systems in terms of the underlying networks of interactions among the large number of molecular participants involved including genes, proteins, and metabolites. In particular, the comparative analysis of network models representing biomolecular interactions in different species or tissues offers a powerful means of identifying conserved modules, predicting functions of specific genes or proteins and studying the evolution of biological processes, among other applications. The primary focus of this dissertation is on the biomolecular network alignment problem: Given two or more networks, the problem is to optimally match the nodes and links in one network with the nodes and links of the other. We describe a suite of modular, extensible, and efficient algorithms for aligning biomolecular network models including: (1) undirected graphs in their weighted and unweighted variations (2) undirected graphs in their labeled and unlabeled variants. The resulting algorithms have been implemented as part of the Biomolecular Network Alignment (BiNA) Toolkit, an open source, user-friendly suite of software for comparative analysis of networks. Our experiments show that BiNA is (i) competitive with the state-of-the-art network alignment tools with respect to the quality of alignments (based on a variety of performance measures) and (ii) able to align large networks ranging in size from a few hundreds of nodes and a few thousand edges to tens of thousands of nodes with millions of edges. We describe several applications of BiNA including (1) construction of phylogenetic trees based on protein-protein interaction networks, and (2) identification of biochemical pathways involved in ligand recognition in B cells by aligning gene co-expression networks constructed from mRNA profiles of B cells exposed to different ligands.