|Stochastic Systems Group|
Discussion on Gene Regulatory Networks
Matt Johnson and Ying Liu
Gene regulatory networks (GRNs) describe repression and induction interactions between genes; that is, the expression of one gene (the fabrication of its corresponding mRNA and protein) may increase or decrease the expression of other genes, and the set of all such interactions forms a regulatory network. Understanding GRNs is of great interest, since they play a key role in the fundamental control mechanisms of the cell. Recently, advances in experimental techniques have caused a proliferation of disparate data concerning GRNs, and the amount of data along with the complexity of the regulatory networks has motivated the use of statistical inference approaches.
We will present a summary of the paper "Reverse Engineering Gene Regulatory Networks" by Huang, Tienda-Luna, and Wang, which surveys several modeling approaches, inference algorithms, and relevant data types. The paper sets up an overall graphical model framework, into which particular models from existing literature fit as sub-models. The paper compares the existing models at a high level, and we will provide slightly more details on some models that should be familiar to the group.
Reference: Y. Huang, I. M. Tienda-Luna, and Y. Wang, Reverse engineering gene regulatory networks: A survey of statistical models, IEEE Signal Processing Magazine, vol. 26, pp. 76-97, Jan. 2009.
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