Presented by Computational Biology and Bioinformatics Discussion Group
Listening to Geronemo: A Modular Approach to Regulatory Networks
Posted April 23, 2007
Gene expression varies significantly between individuals of the same species. Some of this variation arises from changes in nucleotide sequences of regulatory regions of target genes. Variation also arises from changes in the coding sequence of genes whose protein products form transcriptional and other regulatory molecules. These changes in turn affect the regulatory network's targets.
At an Academy meeting, computational biologist Dana Pe'er described a probabilistic method called Geronemo (Genetic Regulatory Network of Modules), which identifies mechanisms by which polymorphisms influence gene regulatory networks. She argued that Geronemo avoids the pitfalls of classical genetic linkage analysis, which can lead to "blind predictions" from a genetic marker to statistical behavior of a quantitative trait.
Use the tabs above to find a meeting report and multimedia from this event.
Dana Pe'er Lab of Computational Systems Biology
Learn more about the Pe'er lab's efforts to understand the organization, function, and evolution of molecular networks. Supporting materials for the Geronemo learning algorithm are available here.
Friedman N, Linial M, Nachman I, Pe'er D. 2000. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7: 601-620.
Lee SI, Pe'er D, Dudley AM, et al. 2006. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc. Natl. Acad. Sci. USA 103: 14062-14067. FULL TEXT
Pe'er D. 2005. Bayesian network analysis of signaling networks: a primer. Sci STKE. 26: l4.
Pe'er D, Regev A, Tanay A. 2002. Minreg: inferring an active regulator set. Bioinformatics 18 Suppl 1: S258-S267. (PDF, 154 KB) FULL TEXT
Pe'er D, Regev A, Elidan G, et al. 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl 1: S215-S224. (PDF, 191 KB) FULL TEXT
Sachs K, Perez O, Pe'er D, et al. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308: 523-529.
Segal E, Shapira M, Regev A, et al. 2003. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34:166-76.
Dana Pe'er, PhD
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Dana Pe'er is assistant professor in the biological sciences department of Columbia University. Her lab endeavors to understand the organization, function, and evolution of molecular networks. Pe'er obtained her PhD from the School of Computer Science and Engineering at Hebrew University of Jerusalem and did her postdoc at Harvard University.
Angelo DePalma is a freelance science writer living in Newton, New Jersey. His e-mail address is firstname.lastname@example.org.