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Regulatory Network inference and analysis

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    Currently, high-throughput data including genetic, transcriptomic, proteomic datasets are available for further analysis in order to decipher the biological function of the genes, pathways, and networks that drive complex phenotypes and diseases. The work to uncover the complex mechanisms is still in progress. Several studies have demonstrated that predictive networks can be built and used to determine how DNA variations influences affect gene expression and other molecular phenotypes. The reconstruction of networks based on integrating PPI data, metabolic data and literature data can help us capture the fundamental properties of complex systems.


    In 2008, Zhu et al integrated different yeast datasets using coexpression and Bayesian network reconstruction approaches and constructed the yeast gene regulatory networks capable of predicting complex system behaviors. They also showed that functional subnetworks are significantly enriched in the more integrated networks. Causal regulators for the different functional subnetworks could also be identified from the networks.   




    Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.

    Zhu, et al. Nature Genetics 40, 854 - 861 (2008). 
    http://www.nature.com/ng/journal/v40/n7/full/ng.167.html (PDF)




    1. Gene regulatory network inference: Data integration in dynamic models—A review.

    Hecker, et al. 2008. (Link)


    2. Computational methods for discovering gene networks from expression data.

    Lee, et al. 2009. (PDF)


    3. Detection and interpretation of expression quantitative trait loci (eQTL).

    Michaelson, et al. 2009. (Link)


    4. Toward a systems-level understanding of developmental regulatory networks.

     Busser, et al. 2008. (Link)

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