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dc.contributor.authorNava-Sedeño, JMen_US
dc.contributor.authorHatzikirou, Hen_US
dc.contributor.authorKlages, Ren_US
dc.contributor.authorDeutsch, Aen_US
dc.date.accessioned2017-12-08T10:18:36Z
dc.date.available2017-11-22en_US
dc.date.issued2017-12-05en_US
dc.date.submitted2017-12-01T19:34:40.187Z
dc.identifier.other16952
dc.identifier.other16952en_US
dc.identifier.other16952en_US
dc.identifier.other16952en_US
dc.identifier.other16952en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/29183
dc.description.abstractMany 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.en_US
dc.description.sponsorshipThe authors thank the Centre for Information Services and High Performance Computing (ZIH) at TU Dresden for providing an excellent infrastructure. The authors acknowledge support by the German Research Foundation and the Open Access Publication Funds of the TU Dresden.The authors would like to thank Anja Voß-Böhme, Lutz Brusch, Fabian Rost, Osvaldo Chara, Simon Syga, and Oleksandr Ostrenko for their helpful comments and fruitful discussions. Andreas Deutsch is grateful to the Deutsche Krebshilfe for support. Andreas Deutsch is supported by the German Research Foundation (Deutsche Forschungsgemeinschaft) within the projects SFB-TR 79 “Materials for tissue regeneration within systemically altered bones” and Research Cluster of Excellence “Center for Advancing Electronics Dresden” (cfaed). Haralampos Hatzikirou would like to acknowledge the SYSMIFTA ERACoSysMed grant (031L0085B) for the financial support of this work and the German Federal Ministry of Education and Research within the Measures for the Establishment of Systems Medicine, project SYSIMIT (BMBF eMed project SYSIMIT, FKZ: 01ZX1308D). Josué Manik Nava-Sedeño is supported by the joint scolarship program DAAD-CONACYT-Regierungsstipendien (50017046) by the German Academic Exchange Service and the National Council on Science and Technology of Mexico.en_US
dc.format.extent16952 - ?en_US
dc.languageengen_US
dc.relation.ispartofSci Repen_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.subjectCell Movementen_US
dc.subjectModels, Biologicalen_US
dc.subjectProbabilityen_US
dc.subjectRandom Allocationen_US
dc.subjectTime Factorsen_US
dc.titleCellular automaton models for time-correlated random walks: derivation and analysis.en_US
dc.typeArticle
dc.rights.holder© 2017 The Author(s)
dc.identifier.doi10.1038/s41598-017-17317-xen_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/29209065en_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume7en_US
dcterms.dateAccepted2017-11-22en_US


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