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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Bayesian inference for Brownian dynamics.

Daniel L Ensign¹, Vijay S Pande

¹Department of Chemistry, Stanford University, Stanford, California 94305, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 28, 2010

Summary

We developed a Bayesian approach to determine potential energy for Brownian dynamics. This method identifies the best polynomial fit and estimates coefficients from particle trajectory data.

Area of Science:

Physics
Statistical Mechanics
Computational Science

Background:

Brownian dynamics describes particle motion influenced by random forces.
Inferring potential energy from observed trajectories is crucial in various scientific fields.
Existing methods may struggle with complex potentials or limited data.

Purpose of the Study:

To present a robust Bayesian method for inferring potential energy from Brownian dynamics.
To enable determination of the optimal polynomial order for potential energy functions.
To provide a framework for estimating potential energy coefficients from trajectory data.

Main Methods:

Utilizing a Bayesian inference framework.
Employing analytical computation of Bayes factors to select polynomial order.

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Estimating coefficients via marginal posterior distributions.

Main Results:

The proposed Bayesian method effectively infers potential energy for Brownian dynamics.
Optimal polynomial order for potential functions can be analytically determined.
Coefficients of the potential energy function are accurately estimated.

Conclusions:

The developed Bayesian method offers a powerful tool for analyzing particle dynamics.
It is applicable to a wide range of single degree-of-freedom trajectories with Gaussian noise.
This approach advances the understanding of potential energy landscapes in physical systems.