Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Saturday, July 23, 2005

Gene Networks

Understanding how genes interact with one another through vast, interconnected networks will play a very important role in characterizing the genetic architecture of disease susceptibility. A recent paper by Basso et al. in Nature Genetics presents a new method for inferring gene networks from gene expression profiles.

Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005 Apr;37(4):382-90. [PubMed]


Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

A nice introduction to scale-free networks is given by Barabasi in Scientific American.

Barabasi AL, Bonabeau E. Scale-free networks. Sci Am. 2003 May;288(5):60-9. [PubMed]

A very interesting endomesodermal gene network can be found at the Davidson Lab web page.

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See related posts on July 16, June 12, and February 16


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