Multiple Alignment of Biological Networks: A Flexible Approach
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
Fast and accurate alignment of multiple protein networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Dividing protein interaction networks for modular network comparative analysis
Pattern Recognition Letters
Parsimonious reconstruction of network evolution
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Reconstruction of network evolutionary history from extant network topology and duplication history
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: The increasing availability of large-scale protein–protein interaction (PPI) data has fuelled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison. Results: We develop a new framework for protein network alignment, based on reconstruction of an ancestral PPI network. The reconstruction algorithm is built upon a proposed model of protein network evolution, which takes into account phylogenetic history of the proteins and the evolution of their interactions. The application of our methodology to the PPI networks of yeast, worm and fly reveals that the most probable conserved ancestral interactions are often related to known protein complexes. By projecting the conserved ancestral interactions back onto the input networks we are able to identify the corresponding conserved protein modules in the considered species. In contrast to most of the previous methods, our algorithm is able to compare many networks simultaneously. The performed experiments demonstrate the ability of our method to uncover many functional modules with high specificity. Availability: Information for obtaining software and supplementary results are available at http://bioputer.mimuw.edu.pl/papers/cappi. Contact: januszd@mimuw.edu.pl