Biological networks show the complex interactions between bio-chemical entities that are often vital for the survival of organisms. The entities communicate with each other to collaborate and perform complex functions that they can not do individually. Numerous applications follow an interaction pattern that resembles biological networks. Wireless networks, sensor networks and homeland security are just a few examples to these applications. Employing biological networks to model the communication patterns in these applications is very promising as the biological networks are robust and flexible. The biological networks efficiently adapt to the alterations in genes or proteins to minimize the damage done to the network by finding alternative ways to keep the network stable whenever it is possible.
One of the critical problems in analysis of biological networks as well as many other applications with complex communication networks is finding similarities between them. To solve this problem, it is necessary to find an alignment of the interacting entities of the input pathways. An alignment of two networks is a one-to-one mapping between a subset of their nodes (i.e., entities). This research develops a generic framework that enables efficient alignment of two networks for gene interaction and metabolic networks. These two networks cover a broad spectrum of communication models ranging from Boolean to stoichiometric models. Unlike existing network alignment methods, this proposal considers the functional similarities of the interacting entities in addition to their structural and topological similarities. This research also develops new methods for indexing network databases. These index structures allow answering range and top-k queries efficiently over a network database.
