Skip to main content
Log in

When Hopf meets saddle: bifurcations in the diffusive Selkov model for glycolysis

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

We study the linear instabilities and bifurcations in the Selkov model for glycolysis with diffusion. We show that this model has a zero wave-vector, finite frequency Hopf bifurcation, which is a forward or supercritical bifurcation, to a growing oscillatory but spatially homogeneous state and a saddle-node bifurcation, which is a backward or subcritical bifurcation, to a growing inhomogeneous state with a steady pattern characterised by a finite wavevector. We further demonstrate that by tuning the relative diffusivity of the two concentrations, it is possible to make both the instabilities to occur at the same point in the parameter space, leading to an unusual type of codimension-two bifurcation. We then show that in the vicinity of this codimension-two bifurcation, the initial conditions decide whether a spatially uniform oscillatory or a spatially periodic steady pattern emerges in the long time limit. It is also possible to form a co-existing patterned and time-periodic state by fine-tuning the diffusivity ratio for moderate values, in qualitative agreement with recent experimental studies.

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

Access this article

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of data and material

Not applicable.

References

  1. Chandrasekhar, S.: Hydrodynamic and Hydromagnetic Stability. Dover Publications, New York (1981)

    MATH  Google Scholar 

  2. Platten, J.K., Legros, J.C.: Convection in Liquids. Springer, Heidelberg (1984)

    MATH  Google Scholar 

  3. Palm, E.: Nonlinear thermal convection. Annu. Rev. Fluid Mech. 7, 39 (1975)

    MATH  Google Scholar 

  4. Kuznetsov, Y. A.: Elements of applied bifurcation theory, Springer, Berlin

  5. Strogatz, S.H.: Nonlinear dynamics. CRC Press, Florida (2000)

    Google Scholar 

  6. Bhattacharjee, J.K.: Convection and Chaos in Fluids. World Scientific, Singapore (1987)

    MATH  Google Scholar 

  7. Platten, J.K.: The soret effect: a review of recent experimental results. J. Appl. Mech. 73, 5 (2006)

    MATH  Google Scholar 

  8. Silber, M., Knobloch, E.: Pattern selection in steady binary-fluid convection. Phys. Rev. A 38, 1468 (1988)

    Google Scholar 

  9. Knobloch, E., Moore, D.R.: Minimal model of binary fluid convection. Phys. Rev. A 42, 4693 (1990)

    Google Scholar 

  10. Hollinger, St., Lücke, M.: Strongly nonlinear convection in binary fluids: minimal model using symmetry decomposed modes. Z. Phys. B 103, 531 (1997)

    Google Scholar 

  11. Fütterer, C.: Growth of nonlinear patterns in binary-fluid convection, analysis of models. Theor. Comput. Fluid Dyn. 16, 467 (2003)

    MATH  Google Scholar 

  12. Gallaire, F., Brun, P.-T.: Fluid dynamic instabilities: theory and application to pattern forming in complex media. Phil. Trans. R. Soc. A 375, 20160155 (2017)

    MATH  Google Scholar 

  13. Turing, A.M.: The chemical basis of morphogenesis. Philos. Trans. A Ser. B 237, 37 (1952)

    MATH  Google Scholar 

  14. Ricard, M.R., Mischler, S.: Turing Instabilities at Hopf Bifurcation. J. Nonlinear Sci. 19, 467 (2009)

    MATH  Google Scholar 

  15. Zhang, T., Xing, Y., Zang, H., Han, M.: Spatio-temporal dynamics of a reaction-diffusion system for a predator-prey model with hyperbolic mortality. Nonlinear Dyn. 78, 265 (2014)

    Google Scholar 

  16. Liu, B., Wu, R., Chen, L.: Turing-Hopf bifurcation analysis in a superdiffusive predator-prey model. Chaos 28, 113118 (2018)

    MATH  Google Scholar 

  17. Tyson, J., Kauffman, S.: Control of mitosis by a continuous biochemical oscillation: Synchronization

  18. Tyson, J., Kauffman, S.: spatially inhomogeneous oscillations. J. Math. Biol. 1, 289 (1975)

    MATH  Google Scholar 

  19. Uecker, H., Wetzel, D.: Numerical Results for Snaking of Patterns over Patterns in Some 2D Selkov-Schnakenberg Reaction-Diffusion Systems. SIAM J. Appl. Dyn. Syst. 13, 94 (2014)

    MATH  Google Scholar 

  20. Ghergu, M., Radulescu, V.: A singular Gierer-Meinhardt system with different source terms. Proc. R. Soc. Edinburgh A 138, 1215 (2008)

    MATH  Google Scholar 

  21. Kepper, P.D., Castets, V., Dulos, E., Boissonade, J.: Turing-type chemical patterns in the chlorite-iodide-malonic acid reaction. Phys. D 49, 161 (1991)

    Google Scholar 

  22. Lengyel, I., Epstein, I.R.: A chemical approach to designing Turing patterns in reactiondiffusion system. Proc. Natl. Acad. Sci. (USA) 89, 3977 (1992)

    MATH  Google Scholar 

  23. Yi, F.Q., Wei, J.J., Shi, J.P.: Diffusion-driven instability and bifurcation in the Lengyel-Epstein system. Nonlinear Anal.: RWA 9, 1038 (2008)

    MATH  Google Scholar 

  24. Ghorai, S., Chakraborty, P., Poria, S., Bairagi, N.: Dispersal-induced pattern-forming instabilities in host-parasitoid metapopulations. Nonlinear Dyn. 100, 749 (2020)

    Google Scholar 

  25. Kumari, N., Mohan, N.: Positive solutions and pattern formation in a diffusive tritrophic system with Crowley-Martin functional response. Nonlinear Dyn. 100, 763 (2020)

    MATH  Google Scholar 

  26. Karaoglu, E., Merdan, H.: Hopf bifurcations of a ratio-dependent predator-prey model involving two discrete maturation time delays. Chaos Sol. Frac. 68, 159 (2014)

    MATH  Google Scholar 

  27. Li, C., Liu, H., Zhang, T., Yan, F.: Network mediated by small noncoding RNA with time delays and diffusion. Int. J. Bifurc. Chaos 27, 175 (2017)

    MATH  Google Scholar 

  28. Rovinsky, A., Menzinger, M.: Interaction of turing and Hopf bifurcations in chemical systems. Phys. Rev. A 46, 6315 (1998)

    Google Scholar 

  29. Ruan, S.G.: Diffusion-driven instability in the Gierer-Meinhardt model of morphogenesis. Nat. Resour. Model. 11, 131 (1998)

    Google Scholar 

  30. De Wit, A., Lima, D., Dewel, G., Borckmans, P.: Spatiotemporal dynamics near a codimension-two point. Phys. Rev. E 54, 261 (1996)

    Google Scholar 

  31. Kong, L., Zhu, C.: Diffusion-driven codimension-two Turing-Hopf bifurcation in the general Brusselator model. Math. Meth. Appl. Sci. 2021, 1 (2021)

    MATH  Google Scholar 

  32. Mazin, W., et al.: Pattern formation in the bistable Gray-Scott model. Math. Comput. Simul. 40, 371 (1996)

    Google Scholar 

  33. Yi, F., Gaffney, E.A., Seirin-Lee, S.: The bifurcation analysis of turing pattern formation induced by delay and diffusion in the Schnakenberg system. Discrete Contin. Dyn. Syst. - B 22, 647 (2017)

    MATH  Google Scholar 

  34. Jiang, W., Wang, H., Cao, X.: Turing Instability and Turing-Hopf Bifurcation in Diffusive Schnakenberg Systems with Gene Expression Time Delay. J. Dyn. Diff. Equat. 31, 2223 (2019)

    MATH  Google Scholar 

  35. E. E. Selkov, Self-oscillations in glycolysis. I. A simple kinetic model, Eur. J. Biochem. 4, 79 (1968)

  36. Murray, J.D.: Mathematical biology II: spatial models and biomedical applications. Springer, New York City (2003)

    MATH  Google Scholar 

  37. Zheng, S., Shen, J.: Turing Instability and Amplitude Equation of Reaction-Diffusion System with Multivariable 2020, Article ID 1381095 (2020)

  38. Meron, E.: Nonlinear Physics of Ecosystems. CRC Press, Florida (2015)

    MATH  Google Scholar 

  39. Ledesma-Durán, A., Aragón, J.L.: Spatio-temporal secondary instabilities near the Turing-Hopf bifurcation. Sci. Rep. 9, 11287 (2019)

    Google Scholar 

  40. Guckenheimer, J., Kuznetsov, Y. A.: Fold-Hopf bifurcation, http://www.scholarpedia.org/article/Fold-Hopf_bifurcation

  41. Guckenheimer, J., Holmes, P.: Nonlinear Oscillations. Dynamical systems and Bifurcations of Vector Fields. Springer, Heidelberg (1983)

    MATH  Google Scholar 

  42. Kuznetsov, Yu.A.: Elements of Applied Bifurcation Theory. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  43. Dangelmayr, G., Knobloch, E.: The Takens-Bogdanov Bifurcation with \(O(2)\)-Symmetry. Phil. Trans. R Soc. Lond. A 322, 243 (1987)

    MATH  Google Scholar 

  44. At the same order in \(\epsilon \), one would also have higher order modes with either frequency \(2\omega _0\) (“frequency doubling”), or spatial modulations given by \(2k_c\) (“wavevector doubling”), which get generated at the nonlinear orders. We ignored these modes here for simplicity

  45. Cross, M.C., Greenside, H.: Pattern Formation and Dynamics in Nonequilibrium Systems. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

  46. Hohenberg, P.C., Krekhov, A.P.: An introduction to the Ginzburg-Landau theory of phase transitions and nonequilibrium patterns. Phys. Rep. 572, 1 (2015)

    MATH  Google Scholar 

  47. Bhattacharya, A.: Spirals and targets in reaction-diffusion systems. Phys. Rev. E 64, 016113 (2001)

    Google Scholar 

  48. Ghosh, S., Roy, D.S.: Selecting spatio-temporal patterns by substrate injection in a reaction-diffusion system. Eur. Phys. J. B 88, 180 (2015)

    Google Scholar 

  49. Schneider, G.: Hopf Bifurcation in Spatially Extended Reaction-Diffusion Systems. J. Nonlinear Sci. 8, 17 (1998)

    MATH  Google Scholar 

  50. Fullwood, T. B.: Pattern formation and travelling waves in reaction diffusion systems, PhD thesis submitted to the University of Warwick (1995)

  51. de Keeper, P., Ferraud, J.-J., Rudovics, B., Dulos, E.: Experimental study of stationary Turing patterns and their interactions with traveling waves in a chemical system. Int. J. Bifur. Chaos 4, 1215 (1994)

    MATH  Google Scholar 

  52. Xu-Jin, Y., Xin, S., Hui-Min, L., Qi, O.: Pattern Formation in the Turing-Hopf Codimension-two Phase Space in a Reaction-Diffusion System. Chin. Phys. Lett. 26, 024702 (2009)

    Google Scholar 

  53. Arnold, L., Namachchivaya, N.S., Schenk-Hoppe, K.S.: Toward an understanding of the stochastic Hopf bifurcation: a case study. Int. J. Bifurcation and Chaos 6, 1947 (1996)

    MATH  Google Scholar 

  54. Juel, A., Darbyshire, A.G., Mullin, T.: The Effect of Noise on Pitchfork and Hopf Bifurcations. Proc. Roy. Soc A 453, 2627 (1997)

    MATH  Google Scholar 

  55. Leppanen, T., Karttunen, M., Barrio, R.A., Kaski, K.: Morphological transitions in Turing systems. Prog. Theo. Phys. (Supplement) 150, 376 (2003)

    Google Scholar 

  56. Samanta, H.S., Bhattacharjee, J.K., Bhattacharyay, A., Chakraborty, S.: On noise induced Poincare-Andronov-Hopf bifurcation. Chaos 24, 043122 (2014)

    MATH  Google Scholar 

Download references

Acknowledgements

AB thanks the SERB, DST (India) for partial financial support through the MATRICS scheme [file no.: MTR/2020/000406].

Funding

The work was supported by Science and engineering research board (Grant No. MTR/2020/000406).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abhik Basu.

Ethics declarations

Conflicts of interest

None.

Code availability

Not applicable.

Additional information

Publisher's Note

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

Appendices

Appendix A: Explicit values of the different coefficients

The coefficients \({\tilde{C}}_1,{\tilde{C}}_2,{\tilde{D}}_1,{\tilde{D}}_2, {\tilde{F}}_2,{\tilde{G}}_1,{\tilde{G}}_2,{\tilde{H}}_1,{\tilde{H}}_2\) are given by

$$\begin{aligned} {\tilde{C}}_1= & {} -\frac{i\omega _0}{3\omega _0^2}(1+\omega _0^2),\,{\tilde{C}}_2=\frac{(1+2i\omega _0)}{6\omega _0^2}(1+\omega _0^2) , \end{aligned}$$
(A1)
$$\begin{aligned} {\tilde{D}}_1= & {} \frac{(D-1)}{9}(1+\omega _0^2),\nonumber \\ {\tilde{D}}_2= & {} -\frac{(D+7)(D-1)}{72D}(1+\omega _0^2),\end{aligned}$$
(A2)
$$\begin{aligned} F_1= & {} 0,\,{\tilde{F}}_2=-\frac{(1+\omega _0^2)}{\omega _0^2},\end{aligned}$$
(A3)
$$\begin{aligned} {\tilde{G}}_1= & {} \frac{i\omega _0+2D/(D-1)}{-\omega _0^2+2i\omega _0(D+1)/(D-1)}(1+\omega _0^2)\nonumber \\{} & {} \left[ 1+\omega _0^2 \left( -\frac{3D+1}{2D}+\frac{i\omega _0}{\omega _0^2}\right) \right] ,\end{aligned}$$
(A4)
$$\begin{aligned} {\tilde{G}}_2= & {} -\frac{i\omega _0+1+2/(D-1)}{-\omega _0^2+2i\omega _0(D+1)/(D-1)}(1+\omega _0^2)\nonumber \\{} & {} \left[ 1+\omega _0^2 \left( -\frac{3D+1}{2D}+\frac{i\omega _0}{\omega _0^2}\right) \right] ,\end{aligned}$$
(A5)
$$\begin{aligned} {\tilde{H}}_1= & {} \frac{i\omega _0+2D/(D-1)}{-\omega _0^2+2i\omega _0(D+1)/(D-1)}(1+\omega _0^2)\nonumber \\{} & {} \left[ 1+\omega _0^2 \left( -\frac{3D+1}{2D}+\frac{i\omega _0}{\omega _0^2}\right) \right] ,\end{aligned}$$
(A6)
$$\begin{aligned} {\tilde{H}}_2= & {} -\frac{i\omega _0+1+2/(D-1)}{-\omega _0^2+2i\omega _0(D+1)/(D-1)}(1+\omega _0^2)\nonumber \\{} & {} \left[ 1+\omega _0^2 \left( -\frac{3D+1}{2D}+\frac{i\omega _0}{\omega _0^2}\right) \right] . \end{aligned}$$
(A7)

The coefficients \(l_1,\,l_2,\,l_3,\,l_4\) are given by

$$\begin{aligned} l_1= & {} b_c\left( {\tilde{C}}_2 + {\tilde{C}}_2^*+2{\tilde{F}}_2+{\tilde{C}}_1\frac{i\omega _0-\omega _0^2}{\omega _0^2}\right. \nonumber \\{} & {} \left. + {\tilde{C}}_1^* \frac{-i\omega _0-\omega _0^2}{\omega _0^2} + \frac{{\tilde{C}}_1+{\tilde{C}}_1^*}{\omega _0^2}\right) -3,\end{aligned}$$
(A8)
$$\begin{aligned} l_2= & {} b_c\left[ \left( {\tilde{G}}_2+{\tilde{G}}_2^*+{\tilde{H}}_2+{\tilde{H}}_2^*-\frac{-k_c^2}{\omega _0^2}\right) \right. \nonumber \\{} & {} \left. + \frac{{\tilde{G}}_1+{\tilde{G}}_1^*+{\tilde{H}}_1+{\tilde{H}}_1^*}{\omega _0^2}\right] -\left( 4+\frac{2}{D}\right) ,\end{aligned}$$
(A9)
$$\begin{aligned} l_3= & {} 2b_c\left[ {\tilde{D}}_2+{\tilde{F}}_2-\frac{\omega _0^2-k_c^2}{\omega _0^2}{\tilde{D}}_1\right] +\frac{2b_c}{\omega _0^2} {\tilde{D}}_1\nonumber \\{} & {} -\frac{3}{2}\left( 1+\frac{1}{D}\right) ,\end{aligned}$$
(A10)
$$\begin{aligned} l_4= & {} 2b_c\left( {\tilde{G}}_2^*+{\tilde{H}}_2^*+{\tilde{F}}_2\right) -\frac{i\omega _0+\omega _0^2}{\omega _0^2} {\tilde{G}}_1^*\nonumber \\{} & {} +\frac{i\omega _0-\omega _0^2}{\omega _0^2}{\tilde{H}}_1+ \frac{{\tilde{G}}_1^*+{\tilde{H}}_1}{\omega _0^2}-\left( 5+\frac{1}{D}\right) . \end{aligned}$$
(A11)

Linear instabilities

The linear stability of FP2 can be easily ascertained by taking Eq. (40) and setting \(R=0\). This gives

$$\begin{aligned} \dot{A}_2= & {} \left[ \frac{1}{2}(1-\omega _0^4)+\frac{2D}{(D-1)^4}(D^2-6D+1)\right] \epsilon _1A_2 \nonumber \\{} & {} -\left[ (1-\omega _0)^4 + \frac{4D(D+1)^2}{(D-1)^4}\right] \epsilon _2A_2\nonumber \\{} & {} +2b_cA_2^3(1+\omega _0^2)\left[ -\frac{1}{\omega _0^2} -\frac{(D+7)(D-1)}{72D}\right. \nonumber \\{} & {} \left. + 2\frac{D-1}{9\omega _0^2} \frac{D+1}{(D-1)^2} \right. \nonumber \\{} & {} \left. + \frac{2}{\omega _0^2}\frac{D-1}{9}\right] \nonumber \\- & {} \frac{3}{2D}(D+1)A_2^3. \end{aligned}$$
(B1)

Including the fifth-order term, the dynamical equation for \(A_2\) is given by (\(R=0\))

$$\begin{aligned} \dot{A}_2= & {} \left[ \frac{1}{2}(1-\omega _0^4)+\frac{2D}{(D-1)^4}(D^2-6D+1)\right] \epsilon _1A_2\nonumber \\{} & {} -\left[ (1-\omega _0)^4 + \frac{4D(D+1)^2}{(D-1)^4}\right] \epsilon _2A_2\nonumber \\{} & {} +l_3 A_2^3 - \Gamma A_2^5, \end{aligned}$$
(B2)

with \(\Gamma >0\).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Basu, A., Bhattacharjee, J.K. When Hopf meets saddle: bifurcations in the diffusive Selkov model for glycolysis. Nonlinear Dyn 111, 3781–3795 (2023). https://doi.org/10.1007/s11071-022-07977-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-022-07977-4

Keywords

Navigation