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.
