Background and Objectives. Molecular-level understanding of virtually any process that occurs in cellular environment requires a kinetic description as well as the ability to measure and/or compute the associated timescales, making the subject of molecular kinetics essential in biochemistry, biophysics, and systems biology. Similarly, dynamic phenomena occurring in materials, such as nucleation or fracture growth, require a kinetic framework. The purpose of this schools is to introduce young researchers to the modern toolkit of chemical kinetics, with an emphasis to applications in life sciences, in which rigorous methods rooted in statistical physics are playing an increasing role. This toolkit enables prediction of kinetic phenomena through computer simulations, as well as interpretation of experimental kinetic data; it involves methods that range from atomistic simulations to physical theories of stochastic phenomena to data analysis. 

The school, taught by Ron Elber (University of Texas at Austin, USA), Dmitrii E Makarov (University of Texas at Austin, USA) and Henri Orland (Saclay, France) will offer its participants a pedagogical introduction into and a hands-on experience with modern techniques of research in chemical kinetics. An essential task of this school will be to provide a bridge between modern theoretical tools and concepts and experimental observations. This task is especially timely for the following reasons. First, a variety of theoretical tools and computational methods have recently emerged, allowing detailed characterization of complex dynamic networks and transition pathways encountered in large-scale biomolecular rearrangements. Second, comparably important progress has recently been achieved in the experimental field. In particular, recent single-molecule experimental studies went far beyond measuring kinetic rate coefficients and probed much finer dynamic details of, e.g. mechanisms behind the function of molecular machines and transition pathways in protein/RNA folding and binding. Yet experimental results are still commonly interpreted in terms of oversimplified, low-dimensionality models, which are not appropriate for the complex processes at hand. The significant advances in theory and in simulation techniques make it possible to interpret sophisticated experiments by a matching set of sophisticated theory and simulation tools. The school is intended to enhance future research of its participants by increasing their awareness of the state of the art in theory and computation.

Intended audience. This school is geared toward young researchers (graduate students and postdocs) working in fields related to molecular kinetics.  

Format and agenda. The school will include a series of lectures delivered by Profs. Ron Elber and Dmitrii E. Makarov from the University of Texas at Austin, and Prof. Henri Orland from Saclay, France. The lectures will be complemented by discussion/problem solving sessions and computer lab sessions. The school will also include a “Dynamic Poster Session”, which means that the students will present unsolved problems of interest. Each problem will be discussed from the perspective of the material presented in the course, and potential approaches to solving those problems will be delineated by the students jointly with the teachers

List of topics covered by the school

(i) Stochastic processes: Langevin dynamics, Fokker-Planck Equation, Path Integral formulation, Generalized Langevin Equation, Dominant Paths (Orland)
(ii) Crossing barriers: Correlation function expressions for the rate, Transition State Theory, Transmission Coefficient, Kramers’ equation, Grote-Hynes and Langer theories, Quantum effects on barrier crossing rates (Makarov)
(iii) Simulation techniques: Kinetic Monte Carlo, Molecular Dynamics, Umbrella Sampling and Generalized Ensembles (Elber, Makarov, Orland)
(iv) Enhanced Sampling Techniques for Kinetics: Transition Interface Sampling, Forward Flux, Weighted Ensembles, and Milestoning (Elber)
(v) Analyzing the results of simulations: Committors, Markov State Models, Transition Path Theory and Milestoning graphs, and Maxflux pathways (Elber)
(vi) Application to biomolecular folding and action (Orland, Makarov, Elber)