Parsimonious reconstruction of network evolution

Authors:
Rob Patro;Emre Sefer;Justin Malin;Guillaume Marçais;Saket Navlakha;Carl Kingsford
Affiliations:
Center for Bioinformatics and Computational Biology and Department of Computer Science;Center for Bioinformatics and Computational Biology and Department of Computer Science;Center for Bioinformatics and Computational Biology and Computational Biology, Bioinformatics and Genomics Concentration, Biological Sciences Graduate Program;Center for Bioinformatics and Computational Biology and Program in Applied Mathematics, Statistics, and Scientific Computation, University of Maryland, College Park, MD;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Center for Bioinf. and Computational Biology and Dept. of Comp. Sci. and Computational Biology, Bioinf. and Genomics Concentration, Biological Sciences Graduate Program and Program in Applied Math ...
Venue:
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Year:
2011

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Abstract

We consider the problem of reconstructing a maximally parsimonious history of network evolution under models that support gene duplication and loss and independent interaction gain and loss. We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories suggest that common ancestral networks can be accurately reconstructed using this parsimony approach.