Title: Algorithms for understanding gene regulation and its evolution

Slide set [PDF]

Abstract: With the completion (and anticipated completion) of genomes of a wide variety of organisms, there is now an abundance of sequence data that needs to be analyzed in order to extract useful biological knowledge. In many cases, several closely related species (such as different varieties of fruit flies) have been sequenced, with the explicit goal of cross-species comparison to facilitate analysis. Therefore, development of accurate and efficient algorithms to perform this multi-species analysis of genomic data has become imperative.

In this talk, we will explore some probabilistic approaches to the above-mentioned problem, and in particular, to the understanding of gene regulation. Probabilistic constructs such as Hidden Markov Models will be shown to have a natural application in this context. When combined with a realistic mathematical model of evolution, the HMM will present itself as a powerful framework for multi-species genome analysis, with provision to systematically deal with biological noise and missing data. Some new experimental results cataloguing evolution of cis-regulation will be presented.

Finally, the talk will chalk out potential research projects that address many pressing questions in bioinformatics today, within the probabilistic framework presented.
 
Biography: Saurabh Sinha received his Ph.D. in Computer Science from the University of Washington, Seattle, in 2002. He was a post-doctoral fellow at the Rockefeller University, New York, from 2002-2005, before joining UIUC this Fall. His interests include algorithms in bioinformatics, particularly on solving problems in gene regulation and evolution.