Quantifying biomolecule diffusivity using an optimal Bayesian method.

Area of Science:

• Biophysics
• Computational Biology
• Physical Chemistry

Background:

• Accurate measurement of biomolecule diffusivity is crucial for understanding cellular processes.
• Existing methods, like mean-square displacement analysis, have limitations in precision and robustness.
• Biomolecular motion can range from free Brownian motion to confined diffusion within membrane microdomains.

Purpose of the Study:

• To propose a novel Bayesian method for extracting biomolecule diffusivity.
• To provide a systematic scheme for estimating diffusivity under different motion models.
• To assess the method's performance, uncertainty quantification, and robustness to noise.

Main Methods:

• A Bayesian inference framework is applied to models of free Brownian motion and confined diffusion.
• The method systematically estimates diffusivity and its associated uncertainty.
• Analysis includes predicting the trajectory length needed for a desired precision and evaluating noise robustness.

Main Results:

• The proposed Bayesian method achieves theoretically optimal performance in diffusivity estimation.
• It demonstrates superior efficiency compared to traditional mean-square displacement methods.
• The approach provides direct access to uncertainty and predicts data requirements for precision.

Conclusions:

• The Bayesian method offers a powerful and accurate tool for measuring biomolecule diffusivity.
• It surpasses existing techniques in efficiency, precision, and provides valuable uncertainty information.
• The method is robust to noise, making it reliable for experimental data analysis.