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Analysis of single particle diffusion with transient binding using particle filtering.

Jason Bernstein¹, John Fricks¹

¹Department of Statistics, Pennsylvania State University, University Park, PA 16802, United States.

Journal of Theoretical Biology

|April 25, 2016

Summary

This summary is machine-generated.

This study models diffusion with transient binding, a common biophysical process. Particle filtering and a stochastic EM algorithm accurately predict binding events and estimate key parameters.

Keywords:

Particle filter Particle tracking Switching model

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Area of Science:

Biophysics
Statistical Mechanics
Physical Chemistry

Background:

Diffusion with transient binding is crucial in biological and colloidal systems.
Examples include protein movement, T cell adhesion, and colloidal fluid caging.

Purpose of the Study:

To model diffusion with transient binding using a Brownian particle undergoing Markovian switching.
To develop methods for predicting binding events and locating binding sites.
To estimate diffusion coefficients, transition probabilities, and binding potential parameters.

Main Methods:

A Brownian particle model with Markovian switching between free diffusion and potential-based diffusion.
Particle filtering to predict binding states and identify binding sites.
Stochastic Expectation-Maximization (EM) algorithm for parameter estimation.

Main Results:

Successful prediction of particle binding states using particle filtering.
Accurate localization of binding sites.
Computation of maximum likelihood estimators for diffusion coefficients, state transition probabilities, and spring constant.

Conclusions:

The developed model and methods provide a robust framework for analyzing diffusion with transient binding.
This approach is applicable to various biophysical processes involving transient interactions.