/Advancing Biomolecular Modeling and Simulation: A Probabilistic 
Approach for Characterizing Complex Systems in the Presence of Constraints/

Thursday, October 17th, 2013
2:00pm
Research Hall, Room 163

Dr. Amarda Shehu
Department of Computer Science, George Mason University

/Abstract/

A fundamental issue in our understanding of biology and treatment of 
disease concerns elucidating the underlying determinants of biological 
function in biomolecules. The main biomolecules at the center of most 
chemical reactions in the living and diseased cell, DNA, RNA, and 
proteins, are complex modular systems composed of many heterogeneous and 
often highly-coupled building blocks operating under physics-based 
constraints. In DNA and RNA, building blocks at the sequence level 
combine in non-trivial ways to give rise to complex functional signals. 
Modeling proteins adds additional algorithmic challenges due to the need 
for spatial reasoning to capture the dynamic nature of these systems as 
they flex their structures to modulate function. Yet, modeling is a 
central tool in understanding the molecular basis of many 
proteinopathies, such as cancer and neurodegenerative disorders.

In this talk, I will provide an overview of algorithmic challenges and 
our contributions in physical algorithms for modeling states and state 
transitions in complex systems in the presence of constraints. The main 
focus of the talk will be on the novel probabilistic approach we have 
proposed for structure and motion computation. The underpinnings of this 
approach are in sampling-based robot motion planning and evolutionary 
computation to efficiently search high-dimensional configuration 
(solution) spaces with non-linear cost (objective) functions. The 
approach makes several innovations, including (1) novel use of 
connectivity and embeddings of the search space for an adaptive search 
of high exploration capability, (2) exploitation of the interplay 
between global and local search for better coverage, and (3) 
incorporation of multi-objective optimization to attenuate  reliance on 
noisy cost functions. Comprehensive evaluations show that this approach, 
when informed by a representation of molecular geometry grounded in 
biophysics, is highly effective. The talk will conclude with selected 
applications demonstrating the ability of this work to formulate 
hypotheses and guide biological research, followed by an outline of my 
future research agenda.

/Speaker Bio/

Amarda Shehu is an Assistant Professor in the Department of Computer 
Science with affiliated appointments in the Department of Bioengineering 
and the School of Systems Biology. Shehu received her Ph.D. in Computer 
Science from Rice University in Houston, TX, where she was an NIH fellow 
of the Nanobiology Training Program of the Gulf Coast Consortia. Shehu's 
general research interests are in the field of Artificial Intelligence. 
Her research contributions to date are in computational structural 
biology, biophysics, and bioinformatics with a focus on issues 
concerning the relationship between sequence, structure, dynamics, and 
function in biological molecules. Shehu's research is currently 
supported by the NSF, the Jeffress Trust Program in Interdisciplinary 
Research, and the Virginia Youth Tobacco Program. Shehu is also the 
recent recipient of an NSF CAREER award. She is a member of ACM, IEEE, 
Biophysical Society, International Society for Computational Biology, 
the American Chemical Society, and the Council on Undergraduate 
Research. Research and educational materials resulting from Shehu's 
work, including images, videos, publications, and software, can be found 
at:http://cs.gmu.edu/~ashehu <http://cs.gmu.edu/%7Eashehu>.