title: Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses.
abstract: First-passage times (FPTs) are widely used to characterize stochastic processes such as chemical reactions, protein folding, diffusion processes or triggering a stock option. In previous work (Suarez et al., JCTC 2014;10:2658-2667), we demonstrated a non-Markovian analysis approach that, with a sufficient subset of history information, yields unbiased mean first-passage times from weighted-ensemble (WE) simulations. The estimation of the distribution of the first-passage times is, however, a more ambitious goal since it cannot be obtained by direct observation in WE trajectories. Likewise, a large number of events would be required to make a good estimation of the distribution from a regular "brute force" simulation. Here, we show how the previously developed non-Markovian analysis can generate approximate, but highly accurate, FPT distributions from WE data. The analysis can also be applied to any other unbiased trajectories, such as from standard molecular dynamics simulations. The present study employs a range of systems with independent verification of the distributions to demonstrate the success and limitations of the approach. By comparison to a standard Markov analysis, the non-Markovian approach is less sensitive to the user-defined discretization of configuration space.
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pennsylvania.
authors.forename: Ernesto
authors.initials: E
authors.lastname: Suárez
authors.affiliation: Department of Chemistry, University of Pittsburgh, Pennsylvania.
authors.forename: Adam J
authors.identifier: http://orcid.org/0000-0002-5275-1020
authors.initials: AJ
authors.lastname: Pratt
authors.affiliation: Department of Chemistry, University of Pittsburgh, Pennsylvania.
authors.forename: Lillian T
authors.initials: LT
authors.lastname: Chong
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pennsylvania.
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Zuckerman
mesh_terms: D000409:Alanine / Q000737:chemistry; D000465:Algorithms; D008697:Methane / Q000737:chemistry; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry*; D012965:Sodium Chloride / Q000737:chemistry; D013269:Stochastic Processes; D013997:Time Factors
chemical_list: D010455:Peptides; D012965:Sodium Chloride; D000409:Alanine; D008697:Methane
keywords: first-passage time; non-Markovian; rare event; weighted ensemble