Travelling waves for a fast reaction limit of a discrete coagulation-fragmentation model with diffusion and proliferation.

IF 2.2 4区 数学 Q2 BIOLOGY
Maxime Estavoyer, Thomas Lepoutre
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Abstract

We study traveling wave solutions for a reaction-diffusion model, introduced in the article Calvez et al. (Regime switching on the propagation speed of travelling waves of some size-structured myxobacteriapopulation models, 2023), describing the spread of the social bacterium Myxococcus xanthus. This model describes the spatial dynamics of two different cluster sizes: isolated bacteria and paired bacteria. Two isolated bacteria can coagulate to form a cluster of two bacteria and conversely, a pair of bacteria can fragment into two isolated bacteria. Coagulation and fragmentation are assumed to occur at a certain rate denoted by k. In this article we study theoretically the limit of fast coagulation fragmentation corresponding mathematically to the limit when the value of the parameter k tends to + . For this regime, we demonstrate the existence and uniqueness of a transition between pulled and pushed fronts for a certain critical ratio θ between the diffusion coefficient of isolated bacteria and the diffusion coefficient of paired bacteria. When the ratio is below θ , the critical front speed is constant and corresponds to the linear speed. Conversely, when the ratio is above the critical threshold, the critical spreading speed becomes strictly greater than the linear speed.

Abstract Image

带有扩散和增殖的离散凝固-破碎模型快速反应极限的游波。
我们研究了一个反应-扩散模型的行波解,该模型在 Calvez 等人的文章(《一些规模结构化的黄曲霉菌种群模型的行波传播速度的制度转换》,2023 年)中介绍过,描述了社会性黄曲霉菌的传播。该模型描述了两种不同规模菌群的空间动态:孤立细菌和成对细菌。两个孤立的细菌可以凝结成一个由两个细菌组成的菌群,反之,一对细菌可以分裂成两个孤立的细菌。在本文中,我们从理论上研究了快速凝结破碎的极限,该极限在数学上与参数 k 值趋于 + ∞ 时的极限相对应。对于这一机制,我们证明了当孤立细菌的扩散系数与成对细菌的扩散系数之间的临界比率θ ⋆达到一定程度时,拉式前沿与推式前沿之间过渡的存在性和唯一性。当该比值低于 θ ⋆时,临界前沿速度恒定,与线速度一致。相反,当比率高于临界阈值时,临界扩散速度严格大于线性速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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