Dresden 2020 – wissenschaftliches Programm
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BP: Fachverband Biologische Physik
BP 19: Poster VII
BP 19.13: Poster
Dienstag, 17. März 2020, 14:00–16:00, P2/3OG
Finding Pathways of Markov State Models — Daniel Nagel and •Gerhard Stock — Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany
In numerous fields of research, the population dynamics of states is described in terms of a master equation or a Markov state model (MSM). Given Markovian dynamics, we can define a transition matrix Tij for a certain lagtime τlag which determines completely the time evolution of the system. Often we are interested in the pathways of the MSM, that lead from an initial to a final state. E.g., the in protein folding these paths account for the mechanism the molecular reaction evolves. These paths naturally arise in a Markov chain Monte Carlo simulation, where we draw random numbers which determine the next step depending on the transition matrix Tij. The catch is the slow convergence. As a remedy, Vanden Eijnden and co-workers have proposed transition path theory. However, this method is designed to only give the most important pathways correctly. In systems of biological interest, e.g. protein folding, allostery etc., many pathways may arise and may also be important to understand the mechanism. To cope with these problems, we suggest a new method which directly considers the path probabilities. In contrast to Markov chain Monte Carlo, it samples the path space more efficiently and gives a well-defined error. We demonstrate the performance and discuss the insights revealed by adopting the folding of villin headpiece.