Learning the nonlinear interactions from particle trajectories

Abstract

Nonlinear interaction of membrane proteins with cytoskeleton and membrane leads to non-Gaussian structure of their displacement probability distribution. We propose a novel statistical analysis technique for learning the characteristics of the nonlinear potential from the cumulants of the displacement distribution. The efficiency of the approach is demonstrated on the analysis of 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 the potential for the much lower temporal resolution compare with the mean square displacement analysis.

5 pages, 4 figures

References in corpus (4)

• In vivo anomalous diffusion and weak ergodicity breaking of lipid granules
• Quantitative analysis of single particle trajectories: mean maximal excursion method
• Inferring maps of forces inside cell membrane microdomains
• Reconstruction of potential energy profiles from multiple rupture time distributions