Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

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dc.contributor.authorTian, Yeen
dc.contributor.authorZhang, Baien
dc.contributor.authorHoffman, Eric P.en
dc.contributor.authorClarke, Roberten
dc.contributor.authorZhang, Zhenen
dc.contributor.authorShih, Ie-Mingen
dc.contributor.authorXuan, Jianhuaen
dc.contributor.authorHerrington, David M.en
dc.contributor.authorWang, Yueen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2015-11-04T19:33:56Zen
dc.date.available2015-11-04T19:33:56Zen
dc.date.issued2014-07-24en
dc.description.abstractModeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size; highly complex networks; imperfect prior knowled≥ missing significance assessment; and heuristic structural parameter learning. To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to "random" knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://www.cbil.ece.vt.edu/software.htm. Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological networks. The method efficiently leverages data-driven evidence and existing biological knowledge while remaining robust to the false positive edges in the prior knowledge. The identified network rewiring events are supported by previous studies in the literature and also provide new mechanistic insight into the biological systems. We expect the knowledge-fused differential dependency network analysis, together with the open-source R package, to be an important and useful bioinformatics tool in biological network analyses.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBMC Systems Biology. 2014 Jul 24;8(1):87en
dc.identifier.doihttps://doi.org/10.1186/s12918-014-0087-1en
dc.identifier.urihttp://hdl.handle.net/10919/63916en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderTian et al.en
dc.rights.holderlicensee BioMed Centralen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleKnowledge-fused differential dependency network models for detecting significant rewiring in biological networksen
dc.title.serialBMC Systems Biologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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