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Logo of amolbioBioMed CentralBiomed Central Web Sitesearchsubmit a manuscriptregisterthis articleAlgorithms for Molecular Biology : AMB
 
Algorithms Mol Biol. 2012; 7: 25.
Published online 2012 September 19. doi:  10.1186/1748-7188-7-25
PMCID: PMC3492119

Parsimonious reconstruction of network evolution

Abstract

Background

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.

Results

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.

Conclusions

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.

Keywords: Network evolution, Arsimony, Ancestral network reconstruction, Interaction networks, Regulatory networks

Articles from Algorithms for Molecular Biology : AMB are provided here courtesy of BioMed Central