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Harnessing Data Science for Biophysical Modeling

Harnessing Data Science for Biophysical Modeling

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When Mar 06, 2019
from 12:45 pm to 02:00 pm
Speaker Xie Lei
Speaker Information Professor of Computer Science and Biology at Hunter College
Where 1311 HN
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Abstract

Molecular interactions are the basis of biophysics and condensed matter physics. The fundamental understanding of the function of a biomolecule requires not only the energetics and dynamics of its interactions with other molecules, but also the impact of these interactions on biological networks. Biophysics techniques such as Molecular Dynamics (MD) simulation play a critical role in studying the physical basis of the molecular interaction. However, it is a great challenge to extend computationally intensive biophysical techniques to a genome scale. In principle, although feasible conformational and functional states of the molecular interaction are enormous, their allowable solutions are much limited under evolutionary, spatial, environmental, and other constraints. The availability of diverse omics data provides us with great opportunities to study the evolutionary, structural, functional connections of molecular interactions. Such connections will significantly reduce the search space of macromolecule conformational and functional states. In this talk, I will introduce the opportunity and challenges of data science as well as present our work in integrating data science with biophysics and network science to study the emergent properties of genome-wide molecular interactions

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