Communication: A multiscale Bayesian inference approach to analyzing subdiffusion in particle trajectories

Area of Science:

• Physics
• Physical Chemistry
• Biophysics

Background:

• Anomalous diffusion exhibits complex asymptotic behavior (t → ∞).
• Finite-length experimental or simulation trajectories hinder accurate detection and characterization of anomalous diffusion.
• Understanding particle dynamics is crucial in various scientific fields.

Purpose of the Study:

• To develop a novel method for robustly detecting and describing anomalous diffusion from finite trajectories.
• To apply Bayesian inference directly to observed particle trajectories across multiple time scales.
• To validate the proposed method using simulated data and analyze real biological systems.

Main Methods:

• Bayesian inference framework applied to particle trajectory data.
• Analysis of trajectories sampled at different time scales.
• Validation using random walk simulations with known statistical properties.

Main Results:

• The proposed Bayesian approach accurately detects anomalous diffusion in finite-length trajectories.
• The method effectively characterizes diffusion properties across various time scales.
• Successful application to analyze lipid molecule motion in a lipid bilayer.

Conclusions:

• Bayesian inference offers a powerful tool for analyzing anomalous diffusion in practical scenarios.
• The developed method overcomes limitations of traditional approaches for finite trajectory data.
• This technique enhances the understanding of molecular dynamics in biological membranes.