Journal
IET SYSTEMS BIOLOGY
Volume 7, Issue 5, Pages 143-152Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-syb.2012.0062
Keywords
Bayes methods; biology computing; circadian rhythms; Gaussian processes; genetics; genomics; graphs; molecular biophysics; proteins; Gaussian graphical model; responsive regulatory networks; time course high-throughput data; biological molecules; dynamic gene expression proflling; circadian rhythm; consistency measurement; matching network structure; simulated time course microarray data; true time course microarray data; dynamic Bayesian network model; time course gene expression proflles; network architectures; documented regulatory networks; speciflc gene expression proflling data; phenotypes; proteins; functional linkages; databases; knowledge-based networks
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Funding
- National Natural Science Foundation of China (NSFC) [31100949, 91029301, 61134013, 61072149]
- Shanghai NSF [11ZR1443100]
- Knowledge Innovation Program of Shanghai Institutes for Biological Sciences (SIBS) of CAS [2011KIP203]
- Knowledge Innovation Program of CAS [KSCX2-EW-R-01]
- Chief Scientist Program of SIBS of CAS [2009CSP002]
- Shanghai Pujiang Program
- 863 project [2012AA020406]
- National Center for Mathematics and Interdisciplinary Sciences, CAS
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With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge-based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.
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