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Markov models of molecular kinetics: Generation and Validation

Please always quote using this URN:urn:nbn:de:0296-matheon-11000
  • Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this article, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modeling molecular dynamics with an MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that an MSM reproduces the kinetics of the molecular dynamics data.

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Metadaten
Author:Jan-Hendrik Prinz, Hao Wu, Marco Sarich, Bettina Keller, Martin Fischbach, Martin Held, John D. Chodera, Christof Schütte, Frank Noe
URN:urn:nbn:de:0296-matheon-11000
Referee:Christof Schütte
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2012/02/27
Release Date:2012/02/27
Institute:Research Center Matheon
Freie Universität Berlin
MSC-Classification:62-XX STATISTICS / 62Mxx Inference from stochastic processes / 62M05 Markov processes: estimation
Preprint Number:942
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