Biological Network Alignment Through Multiobjective Metaheuristic Optimization

As biological inquiry produces ever more network data, such as protein-protein interaction networks, gene regulatory networks, and metabolic networks, many algorithms have been proposed for the purpose of pairwise network alignment– finding a mapping from the nodes of one network to the nodes of another in such a way that the mapped nodes can be considered to correspond with respect to both their place in the network topology and their biological attributes. This technique promises to identify previously undiscovered homologies between proteins of different species and reveal functional similarities in biological networks. In the past few years, a wealth of different aligners have been published, but they vary greatly in performance, and few aligners manage to produce alignments that maximize both the topological and biological similarity of the nodes being aligned. We identify several technical and conceptual flaws in existing approaches to network alignment, and propose both a new way of framing the problem, as well as propose a novel approach to creating alignments, by directly optimizing alignment objectives through a multiobjective genetic algorithm. In contrast to existing tools, we can unify the previously distinct notions of local and global network alignment, and instead of producing a single alignment for a given problem, produce many distinct, informative alignments for a given pair of networks. To the best of our knowledge, the work proposed here will be completely novel in the biological network alignment