ArticleScientific reports2017
Cellular automaton models for time-correlated random walks: derivation and analysis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case.Entropy (Basel, Switzerland) · 2021Article
- BIO-LGCA: A cellular automaton modelling class for analysing collective cell migration.PLoS computational biology · 2021Article
- Article
- A least microenvironmental uncertainty principle (LEUP) as a generative model of collective cell migration mechanisms.Scientific reports · 2020Article
- Modelling collective cell motion: are on- and off-lattice models equivalent?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
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4 authors.
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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.
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