Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Stability Analysis of Spike Solutions to the Schnakenberg Model with Heterogeneity on Metric Graphs

  • Published:
Journal of Nonlinear Science Aims and scope Submit manuscript

Abstract

In this paper, we consider the linear stability of spiky stationary solutions on compact metric graphs for the Schnakenberg model with heterogeneity. The existence of spiky solutions has been shown by the author and Kurata in the work (Ishii and Kurata 2021). By studying the associated linearized eigenvalue problem, we establish the abstract theorem on the stability of the solutions for general compact metric graphs. In particular, the associated Green’s function plays an important role in calculating eigenvalues, and we reveal the several needed conditions for Green’s function on general graphs. To show the stability, we calculate two eigenvalues of order O(1) and of order o(1), respectively. The stability of eigenvalues of order O(1) is shown by using the lemma of Wei and Winter for non-local eigenvalue problem. The stability of eigenvalues of order o(1) is determined by the interaction of the heterogeneity with Green’s function. Moreover, based on the abstract theorem, we give precise stability thresholds with respect to diffusion constants for the solutions without heterogeneity function on the Y-shaped graph and the H-shaped graph. In particular, compared with the one-dimensional interval case, we obtain new phenomena on the stability of two-peak solutions by the effect of the geometry of these concrete graphs. In addition, we also present the effect of heterogeneity by using a typical example.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Ao, W., Liu, C.: The Schnakenberg model with precursors. Discrete Contin. Dyn. Syst. 39(4), 1923–1955 (2019)

    Article  MathSciNet  Google Scholar 

  • Below, Jv., Lubary, J.A.: Instability of stationary solutions of reaction-diffusion-equations on graphs. Results. Math. 68, 171–201 (2015)

    Article  MathSciNet  Google Scholar 

  • Brezis, H.: Functional analysis, Sobolev spaces and partial differential equations. Universitext, Springer, New York (2011)

    Book  Google Scholar 

  • Camilli, F., Corrias, L.: Parabolic models for chemotaxis on weighted networks. J. Math. Pures Appl. 108, 459–480 (2017)

    Article  MathSciNet  Google Scholar 

  • Du, Y., Lou, B., Peng, R., Zhou, M.: The fisher-KPP equation over simple graphs: varied persistence states in river networks. J. Math. Biol. 80, 1559–1616 (2020)

    Article  MathSciNet  Google Scholar 

  • Flores, J., Romero, A.M., Travasso, R.D.M., Poiré, E.C.: Flow and anastomosis in vascular networks. J. Theor. Biol. 317, 257–270 (2013)

    Article  MathSciNet  Google Scholar 

  • Gierer, A., Meinhardt, H.: A theory of biological pattern formation. Kybernetik 12, 30–39 (1972)

    Article  Google Scholar 

  • Gomez, D., Mei, L., Wei, J.: Stable and unstable periodic spiky solutions for the Gray-Scott system and the Schnakenberg system. J. Dyn. Diff. Eqns. 32, 441–481 (2020)

    Article  MathSciNet  Google Scholar 

  • Iron, D., Wei, J., Winter, M.: Stability analysis of turing patterns generated by the Schnakenberg model. J. Math. Biol. 49, 358–390 (2004)

    Article  MathSciNet  Google Scholar 

  • Ishii, Y.: Stability of multi-peak symmetric stationary solutions for the Schnakenberg model with periodic heterogeneity. Commun. Pure Appl. Anal. 19(6), 2965–3031 (2020)

    Article  MathSciNet  Google Scholar 

  • Ishii, Y.: The effect of heterogeneity on one-peak stationary solutions to the Schnakenberg model. J. Differential Equations 285(5), 321–382 (2021)

    Article  MathSciNet  Google Scholar 

  • Ishii, Y.: Concentration phenomena on \(Y\)-shaped metric graph for the Gierer-Meinhardt model with heterogeneity. Nonlinear Anal. 205, 112220 (2021)

    Article  MathSciNet  Google Scholar 

  • Ishii, Y., Kurata, K.: Existence and stability of one-peak symmetric stationary solutions for Schnakenberg model with heterogeneity. Discrete Contin. Dyn. Syst. 39(5), 2807–2875 (2019)

    Article  MathSciNet  Google Scholar 

  • Ishii, Y., Kurata, K.: Existence of multi-peak solutions to the Schnakenberg model with heterogeneity on metric graphs. Commun. Pure Appl. Anal. 20(4), 1633–1679 (2021)

    Article  MathSciNet  Google Scholar 

  • Iwasaki, S.: Asymptotic convergence of solutions of Keller-Segel equations in network shaped domains. Nonlinear Anal. 197, 111839 (2020)

    Article  MathSciNet  Google Scholar 

  • Jimbo, S., Morita, Y.: Asymptotic behavior of entire solutions to reaction-diffusion equations in an infinite star graph. Discrete Contin. Dyn. Syst. 41(9), 4013–4039 (2021)

    Article  MathSciNet  Google Scholar 

  • Jin, Y., Peng, R., Shi, J.: Population dynamics in river networks. J. Nonlinear Sci. 29, 2501–2545 (2019)

    Article  MathSciNet  Google Scholar 

  • Kurata, K., Shibata, M.: Least energy solutions to semi-linear elliptic problems on metric graphs. J. Math. Anal. Appl. 491(1), 124297 (2020)

    Article  MathSciNet  Google Scholar 

  • Li, Y., Li, F., Shi, J.: Ground states of nonlinear Schrödinger equation on star metric graphs. J. Math. Anal. Appl. 459, 661–685 (2018)

    Article  MathSciNet  Google Scholar 

  • Schnakenberg, J.: Simple chemical reaction system with limit cycle behaviour. J. Theor. Biol. 81, 389–400 (1979)

    Article  MathSciNet  Google Scholar 

  • Turing, A.M.: The chemical basis of morphogenesis. Philos. Trans. R. Soc. B237, 37–72 (1952)

    MathSciNet  MATH  Google Scholar 

  • Vasilyeva, O.: Population dynamics in river networks: analysis of steady states. J. Math. Biol. 79, 63–100 (2019)

    Article  MathSciNet  Google Scholar 

  • Ward, M.J., Wei, J.: The existence and stability of asymmetric spike patterns for the Schnakenberg model. Stud. Appl. Math. 109, 229–264 (2002)

    Article  MathSciNet  Google Scholar 

  • Wei, J., Winter, M.: Existence, classification and stability analysis of multiple-peaked solutions for the Gierer-Meinhardt system in \({\cal{R}}^1\). Meth. Appl. Anal. 14, 119–164 (2007)

    Article  Google Scholar 

  • Wei, J., Winter, M.: Stationary multiple spots for reaction-diffusion system. J. Math. Biol. 57, 53–89 (2008)

    Article  MathSciNet  Google Scholar 

  • Wei, J., Winter, M.: On the Gierer-Meinhardt system with precursors. Discrete Contin. Dyn. Syst. 25(1), 363–398 (2009)

    Article  MathSciNet  Google Scholar 

  • Wei, J., Winter, M.: Mathematical Aspects of Pattern Formation in Biological Systems, Applied Mathematical Sciences, vol. 189. Springer, London (2014)

    MATH  Google Scholar 

  • Wei, J., Winter, M.: Stable spike clusters for the one-dimensional Gierer-Meinhardt system. Eur. J. Appl. Math. 28, 576–635 (2016)

    Article  MathSciNet  Google Scholar 

  • Yanagida, E.: Stability of nonconstant steady states in reaction-diffusion systems on graphs. Japan J. Indust. Appl. Math. 18, 25–42 (2001)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers 20J12212 and 21K20341. The author would like to thank Professor Kazuhiro Kurata for his guidance and useful comments. The author also would like to thank the referees for the careful reading of the manuscript and many useful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuta Ishii.

Additional information

Communicated by Michael Wardm.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In this section, we check the two assumptions (GS1) and (GS2) for Lemma 3.1 and Lemma 4.1, respectively.

Check of (GS1) and (GS2) for Lemma 3.1. Let \((t_1,t_2) \in e_1\times e_2\) (or \(e_1\times e_1\) with \(t_1<t_2\)). Moreover, let \(y,z \in (-r(\varvec{t}),r(\varvec{t}))\). We first check (GS1). By (3.1), we deduce

$$\begin{aligned} K_{ii}(y,z)=\frac{1}{2D}(|y-z|-|z|)-\frac{y^2}{2DL}, \quad K_{ij}(y,z)=-\frac{y^2}{2DL}\;(i \ne j),\qquad \end{aligned}$$
(9.49)

\(K_{ij}(y,z)\) is given in (2.7). Thus, \({\hat{K}}_{ij}(u,z)=-y^2/(2DL)\). Then, we obtain \(\partial _y{\hat{K}}_{ij}(y,z)=-y/(DL)\) and \(\partial _z{\hat{K}}_{ij}(y,z)=0\). From (3.16) and (3.26), we find that

$$\begin{aligned} \partial _{t_1}m_{21}(\varvec{t})=\partial _{t_2}m_{12}(\varvec{t})=0 ,\quad \partial _{t_1}m_{1m}(\varvec{t})=\partial _{t_2}m_{2m}(\varvec{t})=-1/(DL), \end{aligned}$$
(9.50)

for \(m=1,2\). Thus, we complete the check of (GS1). Next, we check (GS2). Put \(\sigma (\varvec{t},s):=G(t_1,s)-G(t_2,s)\). In particular, \(\sigma (\varvec{t},t_j)=\sigma _j(\varvec{t})\). By (3.1), we can calculate

$$\begin{aligned} \begin{aligned} D\sigma (\varvec{t},z+t_j)&= \frac{1}{2}\bigr [|t_1-(z+t_j)|-(t_1+z+t_j)\bigl ]\chi _{e_1}(t_j) \\&\quad -\frac{1}{2}\bigr [|t_2-(z+t_j)|-(t_2+z+t_j)\bigl ]\chi _{e_2}(t_j) \\&\quad -\frac{1}{2L}(t_1-l_1)^2+\frac{1}{2L}(t_2-l_2)^2+\frac{l_1^2}{2L}-\frac{l_2^2}{2L}. \end{aligned} \end{aligned}$$
(9.51)

Therefore, we arrive at

$$\begin{aligned} \begin{aligned} D(\sigma (\varvec{t},z+t_j)-\sigma _j(\varvec{t}))&= \frac{1}{2}\bigr [|t_1-(z+t_j)|-|t_1-t_j|-z \bigl ]\chi _{e_1}(t_j) \\&\quad -\frac{1}{2}\bigr [|t_2-(z+t_j)|-|t_2-t_j|-z \bigl ]\chi _{e_2}(t_j) . \end{aligned} \end{aligned}$$
(9.52)

If \((t_1,t_2) \in e_1\times e_2\), then

$$\begin{aligned} D(\sigma (\varvec{t},z+t_j)-\sigma _j(\varvec{t}))=2^{-1}(-1)^{j}\bigr [z-|z| \bigl ]. \end{aligned}$$
(9.53)

Also, (3.16) implies \((Dq_1(\varvec{t}),Dq_2(\varvec{t}))=(-2^{-1},2^{-1})\). Thus we obtain

$$\begin{aligned} G(t_1,z+t_j)-G(t_2,z+t_j)=\sigma _j(\varvec{t})+q_j(\varvec{t})z+\tau _j(z), \end{aligned}$$
(9.54)

where \(D\tau _j(z):=-2^{-1}(-1)^{j}|z|\) is an even function on \((-r(\varvec{t}),r(\varvec{t}))\). On the other hand, if \((t_1,t_2) \in e_1\times e_1\) with \(t_1<t_2\), then by combining (9.52) and (3.26), it holds that

$$\begin{aligned} G(t_1,z+t_j)-G(t_2,z+t_j)=\sigma _j(\varvec{t})+q_j(\varvec{t})z+\tau _j(z), \end{aligned}$$
(9.55)

where \((Dq_1(\varvec{t}),Dq_2(\varvec{t}))=(2^{-1},2^{-1})\) and \(D\tau _j(z):=-2^{-1}(-1)^{j}|z|\). Hence, we finish the check of (GS2). \(\square \)

Check of (GS1) and (GS2) for Lemma 4.1. Since (GS1) and (GS2) for Lemma 4.1 can be checked by same argument above, we check only the case of \((t_1, t_2) \in e_1 \times e_3\). Let \(y,z\in (-r(\varvec{t}),r(\varvec{t}))\). We first check (GS1). By using (4.3), we obtain

$$\begin{aligned} G(y+t_i,z+t_j)-G(t_i,z+t_j)=m_{ij}(\varvec{t})y+K_{ij}(y,z), \end{aligned}$$
(9.56)

where

$$\begin{aligned} m_{11}(\varvec{t})= & {} -\frac{1}{2D}-\frac{t_1-l_1}{DL} ,\quad m_{22}(\varvec{t})=-\frac{1}{2D}-\frac{t_2-l_3}{DL}+\frac{l_4+l_5}{DL}, \end{aligned}$$
(9.57)
$$\begin{aligned} m_{12}(\varvec{t})= & {} -\frac{t_1-l_1}{DL} ,\quad m_{21}(\varvec{t})=-\frac{t_2-l_3}{DL}+\frac{l_4+l_5}{DL}, \end{aligned}$$
(9.58)

and

$$\begin{aligned} K_{ii}(y,z)=\frac{1}{2D}(|y-z|-|z|)-\frac{y^2}{2DL},\quad K_{ij}(y,z)=-\frac{y^2}{2DL} \; (i\ne j).\qquad \end{aligned}$$
(9.59)

Then, since \({\hat{K}}_{ij}(y,z)=-y^2/(2DL)\), we have \(\partial _y{\hat{K}}_{ij}(y,z)=-y/(DL)\) and \(\partial _z{\hat{K}}_{ij}(y,z)=0\). Therefore, combining (9.57), (9.58), and two formulas above, we obtain

$$\begin{aligned} \partial _{t_1}m_{21}(\varvec{t})=\partial _{t_2}m_{12}(\varvec{t})=0 ,\quad \partial _{t_1}m_{1m}(\varvec{t})=\partial _{t_2}m_{2m}(\varvec{t})=-1/(DL), \end{aligned}$$
(9.60)

for \(m=1,2\). Thus, we complete the check of (GS1). Next, we check (GS2). From (4.3), we deduce

$$\begin{aligned} \begin{aligned}&D\sigma (\varvec{t},z+t_j)= \frac{1}{2}\bigr [|t_1-(z+t_j)|-(t_1+z+t_j)\bigl ]\delta _{1j} \\&\quad -\frac{1}{2}\bigr [|t_2-(z+t_j)|-(t_2+z+t_j)\bigl ]\delta _{2j}-\frac{1}{2L}(t_1-l_1)^2 \\&\quad +\frac{1}{2L}(t_2-l_3)^2+\frac{l_1^2}{2L}-\frac{l_2^2}{2L} -\frac{l_4+l_5}{L}(t_2-l_3+l_1), \end{aligned} \end{aligned}$$
(9.61)

where \(\sigma (\varvec{t},z+t_j):=G(t_1,z+t_j)-G(t_2,z+t_j)\). Noting that \(\sigma (\varvec{t},t_j)=\sigma _j(\varvec{t})\), we can calculate

$$\begin{aligned} \begin{aligned}&D(\sigma (\varvec{t},z+t_j)-\sigma _j(\varvec{t}))= \frac{1}{2}\bigr [|t_1-(z+t_j)|-|t_1-t_j|-z \bigl ]\delta _{1j} \\&\quad -\frac{1}{2}\bigr [|t_2-(z+t_j)|-|t_2-t_j|-z \bigl ]\delta _{2j} =\frac{(-1)^{j}}{2}\bigr [z-|z| \bigl ]. \end{aligned} \end{aligned}$$
(9.62)

Since \((Dq_1(\varvec{t}),Dq_2(\varvec{t}))=(-2^{-1},2^{-1})\) follows from (9.57) and (9.58), we have

$$\begin{aligned} G(t_1,z+t_j)-G(t_2,z+t_j)=\sigma _j(\varvec{t})+q_j(\varvec{t})z+\tau _j(z), \end{aligned}$$
(9.63)

where \(D\tau _j(z):=-2^{-1}(-1)^{j}|z|\) is an even function on \((-r(\varvec{t}),r(\varvec{t}))\). Thus, we finish the check of (GS2). \(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ishii, Y. Stability Analysis of Spike Solutions to the Schnakenberg Model with Heterogeneity on Metric Graphs. J Nonlinear Sci 32, 11 (2022). https://doi.org/10.1007/s00332-021-09762-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00332-021-09762-w

Keywords

Mathematics Subject Classification