title: Bayesian single-exponential kinetics in single-molecule experiments and simulations.
abstract: In this work, we develop a fully Bayesian method for the calculation of probability distributions of single-exponential rates for any single-molecule process. These distributions can even be derived when no transitions from one state to another have been observed, since in that case the data can be used to estimate a lower bound on the rate. Using a Bayesian hypothesis test, one can easily test whether a transition occurs at the same rate or at different rates in two data sets. We illustrate these methods with molecular dynamics simulations of the folding of a beta-sheet protein. However, the theory presented here can be used on any data from simulation or experiment for which a two-state description is appropriate.
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California 94305, USA.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Ensign
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D003198:Computer Simulation; D007700:Kinetics