Network motif identification in stochastic networks

被引:37
作者
Jiang, Rui [1 ]
Tu, Zhidong [1 ]
Chen, Ting [1 ]
Sun, Fengzhu [1 ]
机构
[1] Univ So Calif, Mol & Computat Biol Program, Los Angeles, CA 90089 USA
关键词
expectation-maximization algorithm; mixture model; transcriptional regulatory network; protein-protein interaction network;
D O I
10.1073/pnas.0507841103
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Network motifs have been identified in a wide range of networks across many scientific disciplines and are suggested to be the basic building blocks of most complex networks. Nonetheless, many networks come with intrinsic and/or experimental uncertainties and should be treated as stochastic networks. The building blocks in these networks thus may also have stochastic properties. In this article, we study stochastic network motifs derived from families of mutually similar but not necessarily identical patterns of interconnections. We establish a finite mixture model for stochastic networks and develop an expectation-maximization algorithm for identifying stochastic network motifs. We apply this approach to the transcriptional regulatory networks of Escherichia coli and Saccharomyces cerevisiae, as well as the protein-protein interaction networks of seven species, and identify several stochastic network motifs that are consistent with current biological knowledge.
引用
收藏
页码:9404 / 9409
页数:6
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