Thom H. Dunning, Jr.,
Joint Institute for Computational Science,
University of Tennessee and Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831

Topic : Opportunities and Challenges in Computational Science

Computational modeling and simulation are among the most significant developments in the practice of scientific inquiry in the 20th Century. It is now a significant contributor to many scientific and engineering research programs and is finding increasing use in a broad range of industrial applications. We are presently in the midst of a revolution in computing with an order of magnitude increase in computing power being realized every five years. The most pressing question now is: What must computational scientists and engineers do to harness the power of terascale and beyond computers to solve the most critical problems in science and engineering?

The benefits of harnessing the power of terascale and beyond computers for simulating complex natural and engineered systems will be enormous, but the task will not be easy. It will require advances in theoretical and mathematical science to create mathematical models of great predictive capability and utility. It will require close collaboration among computational scientists, computer scientists, and applied mathematicians to translate these advanced mathematical models into applications that can realize the full potential of today's and tomorrow's high-end computers. It will require educating a new generation of scientists and engineers who can confidently work at the interface of theoretical, mathematical, computational, and computer science.

In this presentation we will discuss the opportunities and challenges in using advanced computing technologies to simulate physical, chemical and biological systems. Whenever possible, we will illustrate the issues confronting computational science with examples from computational molecular science.

Peter Cummings,
Vanderbilt University and ORNL.

Topic : Computational Nanoscience

Nanoscience and computational science are two of the main focus areas of federal research investment in the United States today. In terms of software, at their intersection lies the emerging field of computational nanoscience. Computational nanoscience plays an important role in many areas of nanoscience, such as understanding experiments, designing new nanostructures and nanostructured materials, and elucidating self-assembly to enable directed self-assembly. We will review some examples of this from our own work and that of others. In terms of computing hardware, nanoscience is also expected play a major role in the next generation of computing devices – namely, computers based on molecular electronics devices. We will discuss some of the issues involved in molecular electronics devices and our efforts to model them.

Brian D. Athey
University of Michigan,
Medical School Office of the Vice President of Research

Ravi Radhakrishnan and Tamar Schlick
Department of Chemistry and Courant Institute of Mathematical Sciences,
251 Mercer Street,
New York University, New York, NY 10012.

Abstract:

We describe the application of the Transition Path Sampling (TPS) method of Chandler and coworkers [Annu. Rev. Phys. Chem., 53, 291, 2002] for the first time to a complex biomolecular system in explicit solvent, namely the closing conformational transition of DNA polymerase beta occurring on the millisecond time frame. TPS is a general method for sampling slow processes using a combination of dynamics trajectories and Monte Carlo random walks that connect free energy basins in the conformational space; in this way, it produces information on barrier crossing pathways and energies, which collectively can yield a complete reaction profile. Our protocol to adapt TPS to biomolecular systems is based on a divide and conquer strategy for trajectory generation and analysis and to treat the multiple transition states occurring in the free energy landscape. Vital information on biologically interesting regions of conformation space have been gleaned from prior molecular and Langevin dynamics trajectories. To describe the system's reaction profile for the closing motion which preceeds, and is essential for, the chemical reaction of nucleotide incorporation, we compute the free energy (as a function of critical parameters identified in the sampling) using a histogram-based method by employing umbrella sampling. Together, the five identified transition states and associated free energy barriers describe the cooperative dynamics associated with the conformational transition of pol beta, highlight key residues that play a critical role in the enzyme's function, and begin to yield clues into the relation between conformational and chemical barriers.