Non-Gaussianity in single-particle tracking

Use of kurtosis to learn the characteristics of a cage-type potential

Pavel M. Lushnikov, Petr Sulc, Konstantin S. Turitsyn

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Nonlinear interaction of membrane proteins with cytoskeleton and membrane leads to non-Gaussian structure of their displacement probability distribution. We propose a statistical analysis technique for learning the characteristics of the nonlinear potential from the time dependence of the cumulants of the displacement distribution. The efficiency of the approach is demonstrated on the analysis of the kurtosis of the displacement distribution of the particle traveling on a membrane in a cage-type potential. Results of numerical simulations are supported by analytical predictions. We show that the approach allows robust identification of some characteristics of the potential for the much lower temporal resolution compared with the mean-square displacement analysis and we demonstrate robustness to experimental errors in determining the particle positions.

Original languageEnglish (US)
Article number051905
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume85
Issue number5
DOIs
StatePublished - May 14 2012
Externally publishedYes

Fingerprint

Particle Tracking
kurtosis
Cage
Kurtosis
membranes
Membrane
Cytoskeleton
Membrane Protein
Nonlinear Interaction
Cumulants
Time Dependence
temporal resolution
statistical analysis
Mean Square
learning
time dependence
Statistical Analysis
Probability Distribution
Robustness
proteins

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

Non-Gaussianity in single-particle tracking : Use of kurtosis to learn the characteristics of a cage-type potential. / Lushnikov, Pavel M.; Sulc, Petr; Turitsyn, Konstantin S.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 85, No. 5, 051905, 14.05.2012.

Research output: Contribution to journalArticle

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