论文标题

反应扩散系统中浓度依赖性域的演变

Concentration-Dependent Domain Evolution in Reaction-Diffusion Systems

论文作者

Krause, Andrew L., Gaffney, Eamonn A., Walker, Benjamin J.

论文摘要

近年来,在不断发展的(时间依赖性)领域的背景下进行了广泛的研究,除了在发育生物学中进行更现实的建模外,域的生长涉及改善模式鲁棒性和选择的问题。迄今为止,大多数工作都认为规定的域是给定时间功能的,而不是浓度依赖性动态的情况,这在发育环境中也高度相关。在这里,我们研究了反应扩散系统的这种浓度依赖性域的演化,以阐明这些更复杂的模型的基本方面。我们提出了一维域演化的一般形式,并将其扩展到在温和的本构假设下,以代替开发完整的组织机械模型,将其扩展到$ n $维的歧管。在1D情况下,我们能够扩展围绕均质均衡的线性稳定性分析,尽管这在理解快速增长方案的复杂模式动力学方面的效用有限。我们从数值上证明了1D和2D平面几何形状的各种动力学行为,从而产生了几种新现象,尤其是临近关键分叉边界的近距离界限,例如峰值分裂不稳定性。对于足够快的生长和收缩,浓度依赖性可能会对系统的非线性动力学产生巨大影响,既有定性和定量。我们重点介绍了1D演化与更高维模型之间的关键差异,解释了线性分析的障碍物,并强调了仔细的本构选择在定义较高维度中域进化方面的重要性。我们在生物系统的建模和分析中提出了重要的问题,除了在一维环境中似乎可以处理的许多数学问题,但对于高维模型来说更加困难。

Pattern formation has been extensively studied in the context of evolving (time-dependent) domains in recent years, with domain growth implicated in ameliorating problems of pattern robustness and selection, in addition to more realistic modelling in developmental biology. Most work to date has considered prescribed domains evolving as given functions of time, but not the scenario of concentration-dependent dynamics, which is also highly relevant in a developmental setting. Here, we study such concentration-dependent domain evolution for reaction-diffusion systems to elucidate fundamental aspects of these more complex models. We pose a general form of one-dimensional domain evolution, and extend this to $N$-dimensional manifolds under mild constitutive assumptions in lieu of developing a full tissue-mechanical model. In the 1D case, we are able to extend linear stability analysis around homogeneous equilibria, though this is of limited utility in understanding complex pattern dynamics in fast growth regimes. We numerically demonstrate a variety of dynamical behaviours in 1D and 2D planar geometries, giving rise to several new phenomena, especially near regimes of critical bifurcation boundaries such as peak-splitting instabilities. For sufficiently fast growth and contraction, concentration-dependence can have an enormous impact on the nonlinear dynamics of the system both qualitatively and quantitatively. We highlight crucial differences between 1D evolution and higher dimensional models, explaining obstructions for linear analysis and underscoring the importance of careful constitutive choices in defining domain evolution in higher dimensions. We raise important questions in the modelling and analysis of biological systems, in addition to numerous mathematical questions that appear tractable in the one-dimensional setting, but are vastly more difficult for higher-dimensional models.

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