Cellular automaton models for time-correlated random walks: derivation and analysis

TL;DR

This paper develops non-Markovian cellular automaton models for simulating time-correlated random walks, capturing memory effects and anomalous diffusion observed in biological cell migration, with analytical derivations based on velocity autocorrelation functions.

Contribution

It introduces a data-driven approach to derive reorientation probabilities for cellular automata from velocity autocorrelation functions, enabling modeling of anomalous diffusion with memory effects.

Findings

Models can simulate exponential and power-law velocity correlations.

Analytical results facilitate efficient simulation of memory effects.

Framework applicable to biological cell migration and clustering studies.

Abstract

Many diffusion processes in nature and society were found to be anomalous, in the sense of being fundamentally different from conventional Brownian motion. An important example is the migration of biological cells, which exhibits non-trivial temporal decay of velocity autocorrelation functions. This means that the corresponding dynamics is characterized by memory effects that slowly decay in time. Motivated by this we construct non-Markovian lattice-gas cellular automata models for moving agents with memory. For this purpose the reorientation probabilities are derived from velocity autocorrelation functions that are given a priori; in that respect our approach is `data-driven'. Particular examples we consider are velocity correlations that decay exponentially or as power laws, where the latter functions generate anomalous diffusion. The computational efficiency of cellular automata combined with our analytical results paves the way to explore the relevance of memory and anomalous diffusion for the dynamics of interacting cell populations, like confluent cell monolayers and cell clustering.