On the speed of propagation in Turing patterns for reaction-diffusion systems

被引:1
|
作者
Klika, Vaclav [1 ]
Gaffney, Eamonn A. [2 ]
Maini, Philip K. [2 ]
机构
[1] Czech Tech Univ, Dept Math, FNSPE, Trojanova 13, Prague 12000, Czech Republic
[2] Univ Oxford, Math Inst, Wolfson Ctr Math Biol, Oxford OX2 6GG, England
关键词
Pattern formation; Pulled fronts; Travelling waves; Front propagation; Marginal stability; Envelope equation; FRONT PROPAGATION; INSTABILITIES; SELECTION; MECHANISM; ABSOLUTE; WAVES;
D O I
10.1016/j.physd.2024.134268
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This study investigates transient wave dynamics in Turing pattern formation, focusing on waves emerging from localised disturbances. While the traditional focus of diffusion-driven instability has primarily centred on stationary solutions, considerable attention has also been directed towards understanding spatio-temporal behaviours, particularly the propagation of patterning from localised disturbances. We analyse these waves of patterning using both the well-established marginal stability criterion and weakly nonlinear analysis with envelope equations. Both methods provide estimates for the wave speed but the latter method, in addition, approximates the wave profile and amplitude. We then compare these two approaches analytically near a bifurcation point and reveal that the marginal stability criterion yields exactly the same estimate for the wave speed as the weakly nonlinear analysis. Furthermore, we evaluate these estimates against numerical results for Schnakenberg and CDIMA (chlorine dioxide-iodine-malonic acid) kinetics. In particular, our study emphasises the importance of the characteristic speed of pattern propagation, determined by diffusion dynamics and a complex relation with the reaction kinetics in Turing systems. This speed serves as a vital parameter for comparison with experimental observations, akin to observed pattern length scales. Furthermore, more generally, our findings provide systematic methodologies for analysing transient wave properties in Turing systems, generating insight into the dynamic evolution of pattern formation.
引用
收藏
页数:17
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