Teresa M. Przytycka obtained her Masters degree from Warsaw University, Poland, and her PhD from the University of British Columbia, Vancouver. She is currently a Senior Investigator at the National Center for Biotechnology Information, National Institutes of Health (NIH) where she heads the Algorithmic Methods in Computational and Systems Biology research section and an affiliate faculty of the University of Maryland Institute of Advanced Computer Studies. Her group at NIH focuses on modeling dynamical changes of gene expression in response to perturbations and disease. Dr. Przytycka was a recipient of several awards including the I.W. Killam Memorial Predoctoral Fellowship, the Sloan Foundation and the U.S. Department of Energy Postdoctoral Fellowship in Computational Biology, the Burroughs Wellcome Fellowship in Computational Biology and a K01 NIH research development award. She is a co-editor of a book on protein-protein interactions and an associated editor of several high impact computational biology journals including PLoS Computational Biology, BMC Bioinformatics, BMC Algorithms for Molecular Biology, and IEEE Transactions on Computational Biology and Bioinformatics.
Dr. Przytycka will present Methods for optimal utilization of high throughput selection (HT SELEX) data (abstract below) as part of the Distinguished Lecture Series at the Alliant-Lee Liu Auditorium at Howe Hall at 3:40 p.m. on Thursday, March 26, 2015. This event is co-sponsored by BCB.
Systematic Evolution of Ligands by EXponential Enrichment (SELEX) is a well established experimental procedure to identify aptamers - synthetic single-stranded (ribo)nucleic molecules that bind to a given molecular target. Aptamers have a broad spectrum of applications and are increasingly being used to develop new therapeutics and diagnostics. High Throughput (HT) SELEX combines with massively parallel sequencing technologies. It produces unprecedented amount of data that require suitable computational methods to analyze it. HT-SELEX opened the field to new computational opportunities and challenges that are yet to be addressed. Over the years, we have developed several computational methods to aid the analysis of the results of HT-SELEX and to advance the understanding of the selection process itself.