title: Network motif identification in stochastic networks.
abstract: 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.
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: Rui
authors.initials: R
authors.lastname: Jiang
authors.forename: Zhidong
authors.initials: Z
authors.lastname: Tu
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
authors.forename: Fengzhu
authors.initials: F
authors.lastname: Sun
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D005786:Gene Expression Regulation; D006801:Humans; D008954:Models, Biological; D011485:Protein Binding; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D013269:Stochastic Processes; D014158:Transcription, Genetic / Q000235:genetics