Analysis of Single‐Molecule Fluorescence Spectroscopic Data with a Markov‐Modulated Poisson Process

Abstract

We present a photon-by-photon analysis framework for the evaluation of data from single-molecule fluorescence spectroscopy (SMFS) experiments using a Markov-modulated Poisson process (MMPP). A MMPP combines a discrete (and hidden) Markov process with an additional Poisson process reflecting the observation of individual photons. The algorithmic framework is used to automatically analyze the dynamics of the complex formation and dissociation of Cu2+ ions with the bidentate ligand 2,2'-bipyridine-4,4'dicarboxylic acid in aqueous media. The process of association and dissociation of Cu2+ ions is monitored with SMFS. The dcbpy-DNA conjugate can exist in two or more distinct states which influence the photon emission rates. The advantage of a photon-by-photon analysis is that no information is lost in preprocessing steps. Different model complexities are investigated in order to best describe the recorded data and to determine transition rates on a photon-by-photon basis. The main strength of the method is that it allows to detect intermittent phenomena which are masked by binning and that are difficult to find using correlation techniques when they are short-lived.

The chemical dynamics of nanosensors capable of single-molecule detection

“…54 As described in earlier work, 33,[37][38][39][40][41][42][43][44][45] steps are fit to the simulated traces in a manner that maximizes probability of the stochastic quenching and dequenching events.…”

“…As we have shown previously, 37 these reactions obey rate laws of orders greater than 0, and an increase in analyte concentration results in an increase in the forward rate. Recent studies have focused on using a variety of techniques for extracting rate information from single-molecule fluorescence events, including variations of hidden Markov modeling 33,[37][38][39][40][41][42][43][44][45] and information theory. 46,47 In this study, we exploit the parallelism of these different methods for extracting rate information and compare their accuracies under various kinetic conditions in the presence of simulated noise.…”

Statistical identification of structural rearrangement events in molecular dynamics trajectories

“…Numerous methods that are based on specific kinetic systems were introduced to characterize the dynamics in these experimental trajectories under the assumption of hidden Markov chains, for example through Bayesian analysis [16][17][18], photon counting histograms [19], or maximum likelihood analysis [20,21]. These methods are appropriate for experiments with a well-known number of accessible states, for example in the case of single molecule blinking, or enzymatic turnovers, which can be approximated as ''on'' and ''off'' states.…”

Far‐Field Nanoscopy with Reversible Chemical Reactions

“…[22,23] The bipyridine ligand is coupled to the amino-modified 5'-end of one DNA strand while the complementary strand is labeled with TMR at its 3'-end. Additionally, the complementary strand is also 5'-biotinylated for labeling with streptavidin.…”