Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.

Area of Science:

• Biophysics
• Statistical Mechanics
• Biochemistry

Background:

• Single-molecule fluorescence experiments are increasingly common, requiring advanced statistical analysis.
• Current kinetic models make restrictive assumptions unsuitable for uncharacterized molecular dynamics.

Purpose of the Study:

• To develop a novel, kinetic model-independent Bayesian nonparametric framework for analyzing single-molecule fluorescence data.
• To provide a versatile tool for accurate fluorescence data analysis, especially for systems with complex dynamics.

Main Methods:

• Developed a Bayesian nonparametric framework for single-molecule fluorescence data analysis.
• Created four Markov Chain Monte Carlo (MCMC) samplers, from basic to advanced, to evaluate the framework.
• Applied the methods to experimental data from TIRF photobleaching assays of GFP-tagged EphA2 receptor.

Main Results:

• Demonstrated the necessity of sophisticated MCMC samplers for accurate analysis.
• Validated the framework using synthetic data under various signal-to-noise ratios.
• Showcased the framework's ability to recover ground truth in both high- and low-signal conditions.

Conclusions:

• The proposed Bayesian nonparametric framework is a versatile and accurate tool for single-molecule fluorescence data analysis.
• The method overcomes limitations of kinetic models, particularly for systems with uncharacterized dynamics.
• This approach enhances the statistical rigor and applicability of single-molecule fluorescence measurements.