/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>.