Parsimonious reconstruction of network evolution
1 Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
2 Department of Computer Science, University of Maryland, College Park, MD 20742, USA
3 Computational Biology, Bioinformatics and Genomics Concentration, Biological Sciences Graduate Program, University of Maryland, College Park, MD 20742, USA
4 Program in Applied Mathematics, Statistics, and Scientific Computation, University of Maryland, College Park, MD 20742, USA
5 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Algorithms for Molecular Biology 2012, 7:25 doi:10.1186/1748-7188-7-25Published: 19 September 2012
Understanding the evolution of biological networks can provide insight into how their modular structure arises and how they are affected by environmental changes. One approach to studying the evolution of these networks is to reconstruct plausible common ancestors of present-day networks, allowing us to analyze how the topological properties change over time and to posit mechanisms that drive the networks’ evolution. Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment.
We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of gene duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events to explain the observed present-day networks. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories and real biological networks both suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. A software package implementing our method is available under the Apache 2.0 license at http://cbcb.umd.edu/kingsford-group/parana webcite.
Our parsimony-based approach to ancestral network reconstruction is both efficient and accurate. We show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.