Identifying the mechanism for superdiffusivity in mouse fibroblast motility

Journal article

Publication Details

Author(s): Passucci G, Brasch ME, Henderson JH, Zaburdaev V, Manning ML
Journal: PLoS Computational Biology
Publication year: 2019
Volume: 15
Journal issue: 2
ISSN: 1553-734X
eISSN: 1553-7358


We seek to characterize the motility of mouse fibroblasts on 2D substrates. Utilizing automated tracking techniques, we find that cell trajectories are super-diffusive, where displacements scale faster than t(1/2) in all directions. Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with Levy-distributed run times, and ensembles of cells with heterogeneous speed and rotational noise. We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model. However, neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution. We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs, which correctly matches the short-timescale behaviors and indicates that the run times are not Levy distributed. The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments.

FAU Authors / FAU Editors

Zaburdaev, Vasily Prof.
Lehrstuhl für Mathematik in den Lebenswissenschaften

External institutions with authors

Syracuse University

How to cite

Passucci, G., Brasch, M.E., Henderson, J.H., Zaburdaev, V., & Manning, M.L. (2019). Identifying the mechanism for superdiffusivity in mouse fibroblast motility. PLoS Computational Biology, 15(2).

Passucci, Giuseppe, et al. "Identifying the mechanism for superdiffusivity in mouse fibroblast motility." PLoS Computational Biology 15.2 (2019).


Last updated on 2019-03-06 at 08:53