Analysis of single particle diffusion with transient binding using particle filtering

Jason Bernstein, John Fricks

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Diffusion with transient binding occurs in a variety of biophysical processes, including movement of transmembrane proteins, T cell adhesion, and caging in colloidal fluids. We model diffusion with transient binding as a Brownian particle undergoing Markovian switching between free diffusion when unbound and diffusion in a quadratic potential centered around a binding site when bound. Assuming the binding site is the last position of the particle in the unbound state and Gaussian observational error obscures the true position of the particle, we use particle filtering to predict when the particle is bound and to locate the binding sites. Maximum likelihood estimators of diffusion coefficients, state transition probabilities, and the spring constant in the bound state are computed with a stochastic Expectation-Maximization (EM) algorithm.

Original languageEnglish (US)
Pages (from-to)109-121
Number of pages13
JournalJournal of Theoretical Biology
Volume401
DOIs
StatePublished - Jul 21 2016
Externally publishedYes

Fingerprint

Particle Filtering
Binding sites
binding sites
Binding Sites
Biophysical Phenomena
Markovian Switching
Cell Adhesion
transmembrane proteins
T-cells
Stochastic Algorithms
Cell adhesion
Expectation-maximization Algorithm
Diffusion Model
State Transition
Transition Probability
Bound States
cell adhesion
diffusivity
Maximum Likelihood Estimator
Diffusion Coefficient

Keywords

  • Particle filter
  • Particle tracking
  • Switching model

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modeling and Simulation
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

Cite this

Analysis of single particle diffusion with transient binding using particle filtering. / Bernstein, Jason; Fricks, John.

In: Journal of Theoretical Biology, Vol. 401, 21.07.2016, p. 109-121.

Research output: Contribution to journalArticle

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