title: Analyzing single-molecule time series via nonparametric Bayesian inference.
abstract: 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.
authors: Center for Learning and Memory and Department of Neuroscience, University of Texas at Austin, Austin, Texas.
authors: Keegan E
authors: KE
authors: Hines
authors: Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, Washington.
authors: John R
authors: JR
authors: Bankston
authors: Center for Learning and Memory and Department of Neuroscience, University of Texas at Austin, Austin, Texas. Electronic address: raldrich@austin.utexas.edu.
authors: Richard W
authors: RW
authors: Aldrich
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D057809:HEK293 Cells; D006801:Humans; D051036:Large-Conductance Calcium-Activated Potassium Channels / Q000378:metabolism; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D018709:Statistics, Nonparametric; D013997:Time Factors; D014981:Xenopus
chemical_list: D051036:Large-Conductance Calcium-Activated Potassium Channels
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