Friday, September 20, 2013

eScience Seminar with Orly Alter (Utah); Monday, September 30th, 4:00 PM, EEB-303

Please join the eScience Institute on Monday, September 30, 4:00 pm in EEB-303. Refreshments will be provided.

*Orly Alter (Utah):*
Orly Alter, Ph.D. is a USTAR Associate Professor of Bioengineering and Human Genetics at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She was awarded a National Science Foundation CAREER Award in 2009, and a National Human Genome Research Institute (NHGRI) R01 grant in 2007. She was selected to give the Linear Algebra and its Applications Lecture of the International Linear Algebra Society in 2005, and received an NHGRI Individual Mentored Research Scientist Development Award in Genomic Research and Analysis in 2000, and a Sloan Foundation/Department of Energy Postdoctoral Fellowship in Computational Molecular Biology in 1999. Additional support for her work comes from the Utah Science, Technology and Research (USTAR) Initiative.

Discovery of Principles of Nature from Matrix and Tensor Modeling of Large-Scale Molecular Biological Data

In my Genomic Signal Processing Lab, we are breaking new ground in mathematics, at the interface of mathematics, biology and medicine, and in biology and medicine. In mathematics, we develop generalizations of the mathematical frameworks that underlie the theoretical description of the physical world [1]. At the interface, we use these frameworks to create models that compare and integrate different types of large-scale molecular biological data. In biology and medicine, we use the models to computationally predict previously unknown physical, cellular and evolutionary mechanisms that govern the activity of DNA and RNA. We believe that future discovery and control in biology and medicine will come from the mathematical modeling of large-scale molecular biological data, just as Kepler discovered the laws of planetary motion by using mathematics to describe trends in astronomical data [2].

At the interface, our recent generalized singular value decomposition (GSVD) comparison of two patient-matched genomic datasets uncovered a global pattern of DNA aberrations that is correlated with, and possibly causally related to, brain cancer survival [3]. This new link between a glioblastoma multiforme (GBM) tumor’s genome and a patient’s prognosis offers insights into the cancer’s formation and growth, and suggests promising drug targets. The best prognostic predictor of GBM prior to this discovery was the patient’s age at diagnosis. In mathematics, the higher-order GSVD we formulated is the only framework to date that enables comparison of more than two patient-matched but probe-independent datasets, and, in general, more than two datasets arranged in matrices of the same column dimensions but different row dimensions [4]. In biology, our experiments [5] verified our prediction [6] of a global causal coordination between DNA replication origin activity and mRNA expression, demonstrating that matrix and tensor modeling of DNA microarray data [7] can be used to correctly predict previously unknown biological modes of regulation. Ultimately we hope to bring physicians a step closer to one day being able to predict and control the progression of cell division and cancer as readily as NASA engineers plot the trajectories of spacecraft today.