title: Data driven Langevin modeling of biomolecular dynamics.
abstract: Based on a given time series, the data-driven Langevin equation proposed by Hegger and Stock [J. Chem. Phys. 130, 034106 (2009)] aims to construct a low-dimensional dynamical model of the system. Adopting various simple model problems of biomolecular dynamics, this work presents a systematic study of the theoretical virtues and limitations as well as of the practical applicability and performance of the method. As the method requires only local information, the input data need not to be Boltzmann weighted in order to warrant that the Langevin model yields correct Boltzmann-distributed results. Moreover, a delay embedding of the state vector allows for the treatment of memory effects. The robustness of the modeling with respect to wrongly chosen model parameters or low sampling is discussed, as well as the treatment of inertial effects. Given sufficiently sampled input data, the Langevin modeling is shown to successfully recover the correct statistics (such as the probability distribution) and the dynamics (such as the position autocorrelation function) of all considered problems.
authors.affiliation: Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany.
authors.forename: Norbert
authors.initials: N
authors.lastname: Schaudinnus
authors.forename: Andrzej J
authors.initials: AJ
authors.lastname: Rzepiela
authors.forename: Rainer
authors.initials: R
authors.lastname: Hegger
authors.forename: Gerhard
authors.initials: G
authors.lastname: Stock
mesh_terms: D000465:Algorithms; D056004:Molecular Dynamics Simulation