Abstract

We consider the Gierer–Meinhardt system with precursor inhomogeneity and two small diffusivities in an interval$$\begin{equation*} \left\{ \begin{array}{ll} A_t=\epsilon^2 A''- \mu(x) A+\frac{A^2}{H}, &x\in(-1, 1),\,t>0,\\[3mm] \tau H_t=D H'' -H+ A^2, & x\in (-1, 1),\,t>0,\\[3mm] A' (-1)= A' (1)= H' (-1) = H' (1) =0, \end{array} \right. \end{equation*}$$$$\begin{equation*}\mbox{where } \quad 0<\epsilon \ll\sqrt{D}\ll 1, \quad \end{equation*}$$$$\begin{equation*} \tau\geq 0 \mbox{ and $\tau$ is independent of $\epsilon$. } \end{equation*}$$Aspike clusteris the combination of several spikes which all approach the same point in the singular limit. We rigorously prove the existence of a steady-state spike cluster consisting ofNspikes near a non-degenerate local minimum pointt0of the smooth positive inhomogeneity μ(x), i.e. we assume that μ′(t0) = 0, μ″(t0) > 0 and we have μ(t0) > 0. Here,Nis an arbitrary positive integer. Further, we show that this solution is linearly stable. We explicitly compute all eigenvalues, both large (of orderO(1)) and small (of ordero(1)). The main features of studying the Gierer–Meinhardt system in this setting are as follows: (i) it is biologically relevant since it models a hierarchical process (pattern formation of small-scale structures induced by a pre-existing large-scale inhomogeneity); (ii) it contains three different spatial scales two of which are small: theO(1) scale of the precursor inhomogeneity μ(x), the$O(\sqrt{D})$scale of the inhibitor diffusivity and theO(ε) scale of the activator diffusivity; (iii) the expressions can be made explicit and often have a particularly simple form.

Highlights

  • In his pioneering work [31] in 1952, Turing studied how pattern formation could start from an unpatterned state

  • Since many reaction–diffusion systems in biological modelling have been proposed and the occurrence of pattern formation has been investigated based on the approach of Turing instability [31]

  • We study the dynamics of pattern formation, even outside the regime covered by the results

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Summary

Introduction

In his pioneering work [31] in 1952, Turing studied how pattern formation could start from an unpatterned state. (3) In [15] (using matched asymptotic expansions) and [33] (based on rigorous analysis), the following existence and stability results have been shown: For a certain parameter range of D, the Gierer–Meinhardt system (1.1) with μ(x) = 1 has asymmetric N-spike steady-state solutions, which consist of exact copies of precisely two different spikes with distinct amplitudes. They can be considered as bifurcating solutions from those in item 1 such that the amplitudes start to differ at the bifurcation point (saddle-node bifuraction). In Appendix B, we perform the technical analysis needed to derive the small eigenvalues

Main results on existence and stability
Existence proof I: approximate solutions
Existence proof II – error of approximate solution
Existence proof IV: reduced problem
Stability proof I: large eigenvalues
Stability proof II: characterization of small eigenvalues
10 Conclusion
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