A novel framework for large scale metabolic network alignments by compression

• Authors: Michael Dang;Ferhat Ay;Tamer Kahveci
• Venue: Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
• Year: 2011

Although the problem of aligning metabolic networks has been considered in the past, the running time and space complexity of these solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can be used with any existing method to significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing method as our base algorithm. This alignment results in supernode mappings in the compressed domain, each of which are smaller instances of network alignment. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. Our experiments demonstrate that this method can reduce the sizes of metabolic networks by almost half at each compression level. For the overall framework, we demonstrate how well it increases the performance of an existing alignment method. We observe that we can align twice or more as large networks using the same amount of resources with our framework compared to a recent method for network alignment, namely SubMAP. Our results also suggest that the alignment obtained by only one level of compression captures the original alignment results with very high accuracy.