Directed Hungary Greedy Algorithm for Biomolecular Networks Alignment

Abstract

There are many algorithms for conducting alignments between undirected biomolecular networks (UBNs), including protein-protein interaction networks (PINs). However, none of them are specialized for directed biomolecular networks (DBNs), such as gene regulatory networks (GRNs) and metabolic networks (MNs). It is challenging and meaningful to achieve optimal mapping in DBN alignment. In this article, we propose a new algorithm, referred to as Directed Hungary Greedy Algorithm (DHGA), for the alignment of DBNs. DHGA focuses on a directed graph and catches information on the direction of edges. Furthermore, both the homology of biomolecules and the similarities of the network topologies are taken into consideration. In DHGA, expert knowledge can be brought in to pre-match biomolecules. We verified the effectiveness and robustness of DHGA using simulation datasets. Our experiments demonstrate that the performance of DHGA is clearly improved when expert knowledge is introduced. Moreover, we conducted DHGA on two metabolic pathway maps from KEGG and identified 21 pairs of similar cell cycle regulatory relationships between human and yeast, 12 of which were supported by references indicating that the paired relationships have the same function.