title: Stochastic Turing patterns: analysis of compartment-based approaches.
abstract: 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.
authors.affiliation: Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA, ycao@vt.edu.
authors.forename: Yang
authors.initials: Y
authors.lastname: Cao
authors.forename: Radek
authors.initials: R
authors.lastname: Erban
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004058:Diffusion; D007700:Kinetics; D055641:Mathematical Concepts; D008954:Models, Biological; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D013269:Stochastic Processes