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PLoS Comput Biol. 2017 Feb 13;13(2):e1005387. doi: 10.1371/journal.pcbi.1005387. eCollection 2017 Feb.

Comparing individual-based approaches to modelling the self-organization of multicellular tissues.

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School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.
School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom.
Bateson Centre, University of Sheffield, Sheffield, United Kingdom.
Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom.


The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. However, it remains unclear how these approaches compare when applied to the same biological problem, and what differences in behaviour are due to different model assumptions and abstractions. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste ( This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short- and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage.

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