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Biophys J
2015 Feb 03;1083:540-56. doi: 10.1016/j.bpj.2014.12.016.
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Analyzing single-molecule time series via nonparametric Bayesian inference.
Hines KE
,
Bankston JR
,
Aldrich RW
.
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The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data.
Andrec,
Direct Determination of Kinetic Rates from Single-Molecule Photon Arrival Trajectories Using Hidden Markov Models.
2003, Pubmed
Andrec,
Direct Determination of Kinetic Rates from Single-Molecule Photon Arrival Trajectories Using Hidden Markov Models.
2003,
Pubmed
Ball,
Ion-channel gating mechanisms: model identification and parameter estimation from single channel recordings.
1989,
Pubmed
Bankston,
Structure and stoichiometry of an accessory subunit TRIP8b interaction with hyperpolarization-activated cyclic nucleotide-gated channels.
2012,
Pubmed
,
Xenbase
Bronson,
Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data.
2009,
Pubmed
Bruno,
Using independent open-to-closed transitions to simplify aggregated Markov models of ion channel gating kinetics.
2005,
Pubmed
Calderhead,
Bayesian approaches for mechanistic ion channel modeling.
2013,
Pubmed
Colquhoun,
On the stochastic properties of single ion channels.
1981,
Pubmed
Conti,
Non-stationary fluctuations of the potassium conductance at the node of ranvier of the frog.
1984,
Pubmed
Cox,
Allosteric gating of a large conductance Ca-activated K+ channel.
1997,
Pubmed
,
Xenbase
Csanády,
Statistical evaluation of ion-channel gating models based on distributions of log-likelihood ratios.
2006,
Pubmed
Flomenbom,
Utilizing the information content in two-state trajectories.
2006,
Pubmed
Hamill,
Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches.
1981,
Pubmed
Hines,
A primer on Bayesian inference for biophysical systems.
2015,
Pubmed
Hines,
Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach.
2014,
Pubmed
Horn,
Estimating kinetic constants from single channel data.
1983,
Pubmed
Horn,
Statistical methods for model discrimination. Applications to gating kinetics and permeation of the acetylcholine receptor channel.
1987,
Pubmed
Horrigan,
Coupling between voltage sensor activation, Ca2+ binding and channel opening in large conductance (BK) potassium channels.
2002,
Pubmed
,
Xenbase
Kienker,
Equivalence of aggregated Markov models of ion-channel gating.
1989,
Pubmed
Landowne,
Exponential sum-fitting of dwell-time distributions without specifying starting parameters.
2013,
Pubmed
Li,
Aggregated markov model using time series of single molecule dwell times with minimum excessive information.
2013,
Pubmed
Liebovitch,
The akaike information criterion (AIC) is not a sufficient condition to determine the number of ion channel states from single channel recordings.
1990,
Pubmed
McKinney,
Analysis of single-molecule FRET trajectories using hidden Markov modeling.
2006,
Pubmed
Milescu,
Maximum likelihood estimation of ion channel kinetics from macroscopic currents.
2005,
Pubmed
Millonas,
Nonequilibrium response spectroscopy of voltage-sensitive ion channel gating.
1998,
Pubmed
Qin,
Maximum likelihood estimation of aggregated Markov processes.
1997,
Pubmed
Ramaswamy,
Role of conformational dynamics in α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptor partial agonism.
2012,
Pubmed
Rosales,
MCMC for hidden Markov models incorporating aggregation of states and filtering.
2004,
Pubmed
Rosales,
Allosteric control of gating mechanisms revisited: the large conductance Ca2+-activated K+ channel.
2009,
Pubmed
Rothberg,
Voltage and Ca2+ activation of single large-conductance Ca2+-activated K+ channels described by a two-tiered allosteric gating mechanism.
2000,
Pubmed
Siekmann,
MCMC can detect nonidentifiable models.
2012,
Pubmed
Siekmann,
MCMC estimation of Markov models for ion channels.
2011,
Pubmed
Sigworth,
Data transformations for improved display and fitting of single-channel dwell time histograms.
1987,
Pubmed
Sigworth,
Covariance of nonstationary sodium current fluctuations at the node of Ranvier.
1981,
Pubmed
Svoboda,
Direct observation of kinesin stepping by optical trapping interferometry.
1993,
Pubmed
Talukder,
Complex voltage-dependent behavior of single unliganded calcium-sensitive potassium channels.
2000,
Pubmed
Taylor,
Denoising single-molecule FRET trajectories with wavelets and Bayesian inference.
2010,
Pubmed
Wagner,
Model selection in non-nested hidden Markov models for ion channel gating.
2001,
Pubmed
Weiss,
Measuring conformational dynamics of biomolecules by single molecule fluorescence spectroscopy.
2000,
Pubmed
van de Meent,
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data.
2013,
Pubmed