Bayesian detection of intensity changes in single molecule and molecular dynamics trajectories.
Single molecule spectroscopy experiments and molecular dynamics simulations have several profound features in common, chief among which is that both follow the dynamics of some degrees of freedom of a single molecule over time. The analysis is essentially the same: one investigates the changes in the degrees of freedom followed. For instance, in a single molecule fluorescence experiment, the degree of freedom is often the number of photons detected in some time period. In this article, we introduce a straightforward Bayesian method for detecting if and when changes occurred. In contrast to methods based upon maximum likelihood estimates, a Bayesian approach allows for a more systematic means not only to change point detection but also to cluster the data into states. Most importantly, the Bayesian method supplies a simpler hypothesis testing framework. Although we focus on Poisson-distributed data, the Bayesian methods outlined here can in principle be applied to data sampled from any distribution.
Ensign
Daniel L
DL
Department of Chemistry, Stanford University, Stanford, California 94305, USA.
Pande
Vijay S
VS
D000465:Algorithms; D001499:Bayes Theorem; D008956:Models, Chemical; D056004:Molecular Dynamics Simulation; D013050:Spectrometry, Fluorescence