In recent years evidence has accumulated that single biological cells often move in a way that is unlike Brownian motion, a paradigmatic model of random dynamics. Instead, cells migrate faster and exhibit so-called superdiffusion. This article reports on a new method to efficiently simulate superdiffusively moving cells. The method employs lattice gas cellular automata, numerical algorithms which emulate single cells as particles moving in time-discrete steps on periodic lattices. The new method paves the way to study large groups of cells and how they interact with each other, which is important to understand biological processes such as the development of organs or wound healing.
In the era of “Big Data”, there is an abundance of biological data. Single or collective cell motility can be measured in vitro or in vivo via various experimental methods such as two-photon imaging or cell cytometry. In this regard, there is a need for “data-driven” modeling frameworks. Our work contributes towards the “data-driven” modeling of single particle superdiffusive behavior without prior knowledge of the mechanisms at work. Such an approach is vital for the study of phenomena whose driving mechanisms are currently unknown or challenging to model.
J.M. Nava-Sedeno, H.Hatzikirou, R.Klages, A. Deutsch, Cellular automaton models for time-correlated random walks: derivation and analysis, Scientific Reports 7, 16952 (2017)