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Tues., Feb. 21, 2012
10 a.m., Osborne Conference Room
(ECSS 3.503)












"RNA regulatory networks help propagate the effects of genetic alterations"
Pavel Sumazin, Center for Computational Biology and Bioinformatics,
Joint Centers for Systems Biology, Columbia University

Biomedical researchers profile DNA and chromatin of large patient cohorts in an attempt to identify common alterations that drive pathology and can point to diagnostic and therapeutic biomarkers. Increasingly, however, it is clear that genetic and epigenetic alterations can regulate pathology combinatorially, and that different combinations of alterations may generate the same phenotype. To make full use of molecular profiling data, we need to understand how alterations affect cellular programs.

I will describe two new types of computationally predicted post-transcriptional regulatory networks. Computational and experimental evidence suggest that interactions in these networks may alter the expression of known drivers of high-grade glioma. I will describe regulators of microRNA activity, which modify the activity of microRNAs without necessarily altering their expression. These regulators may channel the effects of genomic deletions to distally downregulate established tumor suppressors. Conversely, post-transcriptional regulators of microRNA biogenesis alter the expression of known drivers of gliomagenesis by regulating the abundance of the microRNAs that target them. Alterations to these regulators lead to widespread changes to the expression of microRNAs that target known drivers of glioma.

Taken together, our results suggest that post-transcriptional regulation in the cell is both extensive and complex. We present evidence that genetic and epigenetic alterations may be amplified and propagated by post-transcriptional interactions to affect both disease initiation and outcome. Our work provides some of the building blocks necessary for reverse engineering integrated regulatory networks that will help identify driver alterations and explain their effects on cellular programs and pathology.

Pavel Sumazin is a research scientist at Columbia Medical Center. He graduated from Stony Brook University with a PhD in computer science with a focus on design and analysis of algorithms. He taught computer science theory at Portland State University, was an NSF fellow in human genetics at Cold Spring Harbor Laboratory, and served as Associate Director for bioinformatics at Columbia University’s Genome Center.