Parsimonious reconstruction of network evolution.
Parsimonious reconstruction of network evolution.
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DOI：
10.1186/1748-7188-7-25
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发表时间：
2012-09-19
期刊：
影响因子：
--
通讯作者：
Kingsford C
中科院分区：
文献类型：
--
作者：
Patro R;Sefer E;Malin J;Marçais G;Navlakha S;Kingsford C
了解生物网络的进化可以深入了解它们的模块结构如何产生以及它们如何受到环境变化的影响。研究这些网络进化的一种方法是重建当今网络的合理共同祖先，使我们能够分析拓扑属性如何随着时间的推移而变化，并假设驱动网络进化的机制。此外，假定的祖先网络可以用来帮助解决计算生物学中的其他困难问题，例如网络对齐。我们引入了一个组合框架编码网络的历史，我们给出了一个快速的程序，给定一组基因重复的历史，在实践中发现网络历史与接近最小数量的相互作用增益或损失事件来解释所观察到的当今网络。与以前的研究相比，我们的方法不需要知道不相关的重复事件的相对顺序。模拟历史和真实的生物网络的结果都表明，共同的祖先网络可以准确地重建使用这种简约的方法。实现我们的方法的软件包可以在Apache 2.0许可证下在http://cbcb.umd.edu/kingsford-group/parana上获得。我们基于简约的祖先网络重建方法是高效和准确的。我们表明，通过不假设不相关重复事件的相对顺序来考虑更大的潜在祖先相互作用可以改善祖先网络推理。
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. 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.
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影响因子：
4.3
作者：
Navlakha S;Kingsford C
通讯作者：
Kingsford C
DOI：
10.1073/pnas.0409515102
发表时间：
2005-03-01
影响因子：
11.1
作者：
Middendorf, M;Ziv, E;Wiggins, CH
通讯作者：
Wiggins, CH
DOI：
10.1098/rspb.2009.0210
发表时间：
2009-07-07
影响因子：
4.7
作者：
Stewart, Alexander J.;Seymour, Robert M.;Pomiankowski, Andrew
通讯作者：
Pomiankowski, Andrew
影响因子：
2.4
作者：
Ispolatov, I;Krapivsky, PL;Yuryev, A
通讯作者：
Yuryev, A
影响因子：
3
作者：
Erten, Sinan;Li, Xin;Koyuturk, Mehmet
通讯作者：
Koyuturk, Mehmet