Henry Frank Seminar Lecture 1 - Benoit Roux - University of Chicago

February 27, 2025 - 4:00pm to 5:00pm

Title: "Using Computer Simulations to Advance our Understanding of Biological Systems at the Atomic Level"

Abstract:Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology and chemistry. The approach consists of constructing detailed atomic models of the macromolecular system and, having described the microscopic forces with a potential function, using Newton's classical equation, F=MA, to literally "simulate" the dynamical motions of all the atoms as a function of time. The calculated trajectory, though an approximation to the real world, provides detailed information about the time course of the atomic motions, which is impossible to access experimentally. While great progress has been made, producing genuine knowledge about biological systems using MD simulations remains enormously challenging. Among the most difficult problems is the characterization of slow conformational transitions that underlies biological function. With a mixture of history and background to support the more technical presentation of Lecture 2 (Tuesday December 3), these concepts will be illustrated with previous computational studies of K+ channels, Src tyrosine kinases, and the P-type ion pumps. 

References for both Lectures:

1. A. C. Pan, D. Sezer & B. Roux. Finding transition pathways using the string method with swarms of trajectories, J. Phys. Chem. B 112, 3432-3440, (2008). 

2. A. C. Pan & B. Roux. Building Markov state models along pathways to determine free energies and rates of transitions, J. Chem. Phys. 129, 064107, (2008). 

3. B. Roux. String Method with Swarms-of-Trajectories, Mean Drifts, Lag Time, and Committor, J. Phys. Chem. A 125, 7558-7571, (2021). 

4. B. Roux. Transition rate theory, spectral analysis, and reactive paths, J. Chem. Phys. 156, 134111, (2022), 

5. Z. He, C. Chipot & B. Roux. Committor-Consistent Variational String Method, J. Phys. Chem. Lett. 13, 9263−9271, (2022). 

6. H. Chen, B. Roux & C. Chipot. Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning, Journal of chemical theory and computation 19, 4414-4426, (2023). 

See more of Dr. Dr. Roux's research on his website: https://chemistry.uchicago.edu/faculty/beno%C3%AEt-roux

Location and Address

Chevron 150