Repository: Freie Universität Berlin, Math Department

Non- markovian data-driven modeling of single-cell motility

Mitterwallner, Bernhard G. and Schreiber, Christoph and Daldrop, Jan O. and Netz, Roland R. and Rädler, Joachim O. (2020) Non- markovian data-driven modeling of single-cell motility. PHYSICAL REVIEW E, 101 . ISSN 032408

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Official URL: http://DOI:10.1103/PhysRevE.101.032408


Trajectories of human breast cancer cells moving on one-dimensional circular tracks are modeled by the non-Markovian version of the Langevin equation that includes an arbitrary memory function. When averaged over cells, the velocity distribution exhibits spurious non-Gaussian behavior, while single cells are characterized by Gaussian velocity distributions. Accordingly, the data are described by a linear memory model which includes different random walk models that were previously used to account for various aspects of cell motility such as migratory persistence, non-Markovian effects, colored noise, and anomalous diffusion. The memory function is extracted from the trajectory data without restrictions or assumptions, thus making our approach truly data driven, and is used for unbiased single-cell comparison. The cell memory displays time-delayed single-exponential negative friction, which clearly distinguishes cell motion from the simple persistent random walk model and suggests a regulatory feedback mechanism that controls cell migration. Based on the extracted memory function we formulate a generalized exactly solvable cell migration model which indicates that negative friction generates cell persistence over long timescales. The nonequilibrium character of cell motion is investigated by mapping the non-Markovian Langevin equation with memory onto a Markovian model that involves a hidden degree of freedom and is equivalent to the underdamped active Ornstein-Uhlenbeck process.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
ID Code:2782
Deposited By: Monika Drueck
Deposited On:23 Feb 2022 14:56
Last Modified:23 Feb 2022 14:56

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