9 Setembro 2015, 14:47 - Lucília Abreu
Vai decorrer no próximo dia 11 de setembro de 2015, pelas 11h00, na sala 02.2 do Centro de Congressos do IST uma palestra intitulada "Predictive and Scalable MacroMolecular Modeling
" apresentada pelo Prof. Chandrajit Bajaj
do Department of Computer Science - Institute of Comp. Engg
& Sciences. A entrada é livre.
Most biomolecular complexes involve three or more molecules, forming macromolecules consisting of thousands to a million atoms. We consider fast molecular modeling algorithms and data structures to support automated prediction of bimolecular structure assemblies formulating it as the approximate solution of a nonconvex geometric optimization problem. The conformation of the macromolecules with respect to each other are optimized with respect to a hierarchical interface matching score based on molecular energetic potentials ((Lennard-Jones, Coulombic, generalized Born, Poisson Boltzmann ). The assembly prediction decision procedure involves both search and scoring over very high dimensional spaces, (O(6^n) for n rigid molecules) , and moreover is provably NP-hard. To make things even more complicated, predicting biomolecular complexes requires search optimization to include molecular flexibility and induced conformational changes as the assembly interfaces complementarily align. I shall also briefly present fast computation methods which run on commodity multicore CPUs and manycore GPUs. The key idea is to trade off accuracy of pairwise, long-range atomistic energetics for a higher speed of execution. Our CUDA kernel for GPU acceleration uses a cachefriendly, recursive and linear-space octree data structure to handle very large molecular structures with up to several million atoms. Based on this CUDA kernel, we utilize a hybrid method which simultaneously exploits both CPU and GPU cores to provide the best performance based on selected parameters of the approximation scheme.
Chandrajit Bajaj is a Professor of Computer Science, and the director of the Center for Computational Visualization in the Institute for Computational and Engineering Sciences (ICES) at the University of Texas at Austin. Bajaj holds the Computational Applied Mathematics Chair in Visualization. He is also an affiliate Faculty member of Mathematics, Biomedical Engineering, the Institute of Cell and Molecular Biology and Neurosciences. He currently serves on the editorial boards for the International Journal of Computational Geometry and Applications, and the ACM Computing Surveys. He is a fellow of the American Association for the Advancement of Science (AAAS), the Association of Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).