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

Area of Science:

• Biophysics
• Statistical Analysis
• Molecular Biology

Background:

• Single-molecule fluorescence experiments are increasingly common.
• Existing analysis methods often require specific kinetic models, limiting their use.
• There is a need for versatile statistical tools for analyzing complex molecular dynamics.

Purpose of the Study:

• To develop a kinetic model-independent Bayesian nonparametric framework for single-molecule fluorescence data analysis.
• To create and evaluate advanced Markov Chain Monte Carlo (MCMC) samplers for accurate data analysis.
• To provide a robust method for step counting applications with uncharacterized dynamics.

Main Methods:

• Developed a novel Bayesian nonparametric framework.
• Implemented four MCMC samplers of varying complexity.
• Applied the framework to experimental total internal reflection fluorescent photobleaching data (EphA2 receptor-GFP).
• Validated with synthetic data under various signal-to-noise ratios.

Main Results:

• The proposed framework is independent of kinetic models.
• Increased MCMC sampler complexity is crucial for accurate analysis.
• The method successfully recovered ground truth from both high and low signal-to-noise ratio data.
• Demonstrated applicability to real-world biological data.

Conclusions:

• The novel Bayesian nonparametric framework offers a versatile and accurate approach for single-molecule fluorescence data analysis.
• This method overcomes limitations of kinetic model-dependent analyses.
• The developed MCMC samplers are essential for robust performance, especially with complex systems.