2009
DOI: 10.1016/j.bpj.2009.09.031
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Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

Abstract: Time series data provided by single-molecule Förster resonance energy transfer (smFRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g., rate constants, but also information about the model itself, e.g., the number of conformational states. Resolving whether such states exist or how many of them exist requires a careful approach to the problem of model selection, here meaning discrimination among models with differing numbers of states. The most straigh… Show more

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Cited by 393 publications
(527 citation statements)
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“…Data were analyzed with custom MATLAB (The Mathworks) image-processing software. A three E FRETstate model was fit with program vbFRET (30). See SI Materials and Methods for more details.…”
Section: Methodsmentioning
confidence: 99%
“…Data were analyzed with custom MATLAB (The Mathworks) image-processing software. A three E FRETstate model was fit with program vbFRET (30). See SI Materials and Methods for more details.…”
Section: Methodsmentioning
confidence: 99%
“…The fluorescence intensity changes (Figs. 1B and 2A) were detected statistically using a hidden Markov model, analyzed with the variational Bayes method (38).…”
Section: Methodsmentioning
confidence: 99%
“…By using this approach, we could achieve a fluorescence intensity of approximately 100 photons ms −1 , which is sufficient for 0.3 ms resolution on the FRET time trace (Figure 3). We selected time traces longer than 8 ms and then applied a hidden Markov model (HMM) analysis to the time traces 18. Figure 3 a shows typical time traces for R40S30 with a 0.3 ms temporal resolution.…”
mentioning
confidence: 99%