Stochastic Turing Patterns: Analysis of Compartment-Based Approaches
KAUST Grant NumberKUK-C1-013-04
Online Publication Date2014-11-25
Print Publication Date2014-12
Permanent link to this recordhttp://hdl.handle.net/10754/599739
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Abstract© 2014, Society for Mathematical Biology. Turing patterns can be observed in reaction-diffusion systems where chemical species have different diffusion constants. In recent years, several studies investigated the effects of noise on Turing patterns and showed that the parameter regimes, for which stochastic Turing patterns are observed, can be larger than the parameter regimes predicted by deterministic models, which are written in terms of partial differential equations (PDEs) for species concentrations. A common stochastic reaction-diffusion approach is written in terms of compartment-based (lattice-based) models, where the domain of interest is divided into artificial compartments and the number of molecules in each compartment is simulated. In this paper, the dependence of stochastic Turing patterns on the compartment size is investigated. It has previously been shown (for relatively simpler systems) that a modeler should not choose compartment sizes which are too small or too large, and that the optimal compartment size depends on the diffusion constant. Taking these results into account, we propose and study a compartment-based model of Turing patterns where each chemical species is described using a different set of compartments. It is shown that the parameter regions where spatial patterns form are different from the regions obtained by classical deterministic PDE-based models, but they are also different from the results obtained for the stochastic reaction-diffusion models which use a single set of compartments for all chemical species. In particular, it is argued that some previously reported results on the effect of noise on Turing patterns in biological systems need to be reinterpreted.
CitationCao Y, Erban R (2014) Stochastic Turing Patterns: Analysis of Compartment-Based Approaches. Bull Math Biol 76: 3051–3069. Available: http://dx.doi.org/10.1007/s11538-014-0044-6.
SponsorsThe research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC Grant Agreement No. 239870. This publication was based on work supported in part by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). Radek Erban would also like to thank the Royal Society for a University Research Fellowship; Brasenose College, University of Oxford, for a Nicholas Kurti Junior Fellowship; and the Leverhulme Trust for a Philip Leverhulme Prize. Yang Cao's work was supported by the National Science Foundation under awards DMS-1225160 and CCF-0953590, and the National Institutes of Health under award GM078989.
JournalBulletin of Mathematical Biology
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