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Rethinking pattern formation in reaction–diffusion systems

Nature Physicsvolume 14pages507514 (2018) | Download Citation

Abstract

The present theoretical framework for the analysis of pattern formation in complex systems is mostly limited to the vicinity of fixed (global) equilibria. Here we present a new theoretical approach to characterize dynamical states arbitrarily far from (global) equilibrium. We show that reaction–diffusion systems that are driven by locally mass-conserving interactions can be understood in terms of local equilibria of diffusively coupled compartments. Diffusive coupling generically induces lateral redistribution of the globally conserved quantities, and the variable local amounts of these quantities determine the local equilibria in each compartment. We find that, even far from global equilibrium, the system is well characterized by its moving local equilibria. We apply this framework to in vitro Min protein pattern formation, a paradigmatic model for biological pattern formation. Within our framework we can predict and explain transitions between chemical turbulence and order arbitrarily far from global equilibrium. Our results reveal conceptually new principles of self-organized pattern formation that may well govern diverse dynamical systems.

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References

  1. 1.

    Cross, M. & Greenside, H. Pattern Formation and Dynamics in Nonequilibrium Systems. (Cambridge Univ. Press, Cambridge, 2009).

  2. 2.

    Cross, M. & Hohenberg, P. Pattern formation outside of equilibrium. Rev. Mod. Phys. 65, 851 (1993).

  3. 3.

    Turing, A. M. The chemical basis of morphogenesis. Phil. Trans. R. Soc. Lond. B 237, 37–72 (1952).

  4. 4.

    Bourgine, P. & Lesne, A. Morphogenesis: Origins of Patterns and Shapes. (Springer, Berlin, 2011).

  5. 5.

    Menck, P. J., Heitzig, J., Marwan, N. & Kurths, J. How basin stability complements the linear-stability paradigm. Nat. Phys. 9, 89–92 (2013).

  6. 6.

    Gualdi, S., Tarzia, M., Zamponi, F. & Bouchaud, J. P. Tipping points in macroeconomic agent-based models. J. Econ. Dyn. Control. 50, 29–61 (2015).

  7. 7.

    Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

  8. 8.

    Dai, L., Korolev, K. S. & Gore, J. Slower recovery in space before collapse of connected populations. Nature 496, 355–358 (2013).

  9. 9.

    Anstey, M., Rogers, S., Ott, S., Burrows, M. & Simpson, S. Serotonin mediates behavioral gregarization underlying swarm formation in desert locusts. Science 323, 627–630 (2009).

  10. 10.

    Mori, Y., Jilkine, A. & Edelstein-Keshet, L. Wave-pinning and cell polarity from a bistable reaction-diffusion system. Biophys. J. 94, 3684–3697 (2008).

  11. 11.

    Trong, P. K., Nicola, E. M., Goehring, N. W., Kumar, K. V. & Grill, S. W. Parameter-space topology of models for cell polarity. New. J. Phys. 16, 065009 (2014).

  12. 12.

    Huang, K. C., Meir, Y. & Wingreen, N. S. Dynamic structures in Escherichia coli: spontaneous formation of MinE rings and MinD polar zones. Proc. Natl. Acad. Sci. USA 100, 12724–12728 (2003).

  13. 13.

    Halatek, J. & Frey, E. Highly canalized MinD transfer and MinE sequestration explain the origin of robust MinCDE-protein dynamics. Cell. Rep. 1, 741–752 (2012).

  14. 14.

    Loose, M., Fischer-Friedrich, E., Ries, J., Kruse, K. & Schwille, P. Spatial regulators for bacterial cell division self-organize into surface waves in vitro. Science 320, 789–792 (2008).

  15. 15.

    Wu, F., van Schie, B. G. C., Keymer, J. E. & Dekker, C. Symmetry and scale orient Min protein patterns in shaped bacterial sculptures. Nat. Nanotech. 10, 719–726 (2015).

  16. 16.

    Wu, F. et al. Multistability and dynamic transitions of intracellular Min protein patterns. Mol. Syst. Biol. 12, 642–653 (2016).

  17. 17.

    Caspi, Y. & Dekker, C. Mapping out Min protein patterns in fully confined fluidic chambers. eLife 5, 1–53 (2016).

  18. 18.

    Ishihara, S., Otsuji, M. & Mochizuki, A. Transient and steady state of mass-conserved reaction-diffusion systems. Phys. Rev. E 75, 015203 (2007).

  19. 19.

    Goryachev, A. B. & Leda, M. Many roads to symmetry breaking: molecular mechanisms and theoretical models of yeast cell polarity. Mol. Biol. Cell. 28, 370–380 (2017).

  20. 20.

    Mori, Y., Jilkine, A. & Edelstein-Keshet, L. Asymptotic and bifurcation analysis of wave-pinning in a reaction-diffusion model for cell polarization. SIAM J. Appl. Math. 71, 1401–1427 (2011).

  21. 21.

    Halatek, J. & Frey, E. Effective 2D model does not account for geometry sensing by self-organized proteins patterns. Proc. Natl. Acad. Sci. USA 111, E1817 (2014).

  22. 22.

    Raskin, D. M. & de Boer, P. A. J. Rapid pole-to-pole oscillation of a protein required for directing division to the middle of Escherichia coli. Proc. Natl. Acad. Sci. USA 96, 4971–4976 (1999).

  23. 23.

    Kretschmer, S. & Schwille, P. Pattern formation on membranes and its role in bacterial cell division. Curr. Opin. Cell. Biol. 38, 52–59 (2016).

  24. 24.

    Lutkenhaus, J. Assembly dynamics of the bacterial MinCDE system and spatial regulation of the Z ring. Annu. Rev. Biochem. 76, 539–562 (2007).

  25. 25.

    Strogatz, S. H. Nonlinear Dynamics and Chaos. (Westview, Boulder, CO, 1994).

  26. 26.

    Schöll, E. & Schuster, H. G. Handbook of Chaos Control. 2nd edn, (Wiley-VCH, Weinheim, 2008).

  27. 27.

    Kuramoto, Y. & Battogtokh, D. Coexistence of coherence and incoherence in nonlocally coupled phase oscillators. Nonlinear Phenom. Complex Syst. 5, 380–385 (2002).

  28. 28.

    Abrams, D. M. & Strogatz, S. H. Chimera states for coupled oscillators. Phys. Rev. Lett. 93, 174102 (2004).

  29. 29.

    Eigenberger, G. Kinetic instabilities in heterogeneously catalyzed reactions – II. Oscillatory instabilities with Langmuir-type kinetics. Chem. Eng. Sci. 33, 1263–1268 (1978).

  30. 30.

    Kim, M. et al. Controlling chemical turbulence by global delayed feedback: pattern formation in catalytic CO oxidation on Pt(110). Science 292, 1357–1360 (2001).

  31. 31.

    Mikhailov, A. S. & Showalter, K. Control of waves, patterns and turbulence in chemical systems. Phys. Rep. 425, 79–194 (2006).

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Acknowledgements

This research was supported by the German Excellence Initiative via the programme ‘NanoSystems Initiative Munich’ (NIM), and by the Deutsche Forschungsgemeinschaft (DFG) via project B02 within the SFB 1032 ‘Nanoagents for Spatio-Temporal Control of Molecular and Cellular Reactions’. The authors thank F. Brauns, C. Dekker and G. Pawlik for detailed feedback on the manuscript. The authors thank F. Brauns, J. Denk and D. Thalmeier for discussions and F. Brauns for his preliminary work on the CO–Pt system, which has significantly advanced our understanding of the mass-redistribution framework presented here.

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Affiliations

  1. Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universität München, München, Germany

    • J. Halatek
    •  & E. Frey

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Contributions

J.H. and E.F. conceived and performed the research. J.H. developed the analytical and numerical results. J.H. and E.F. discussed and analysed the results and wrote the manuscript.

Corresponding author

Correspondence to E. Frey.

Supplementary information

  1. Supplementary Information

    Supplementary Notes, Supplementary Tables 1 and 2, Supplementary Figures 1–12, Supplementary References 1–18

Videos

  1. Onset of chemical turbulence

    Top: membrane profile of MinD during the transition from small amplitude standing waves to chemical turbulence for a control parameter (bulk height) h = 20µm. (Also, see main text Fig. 3b.) Bottom: temporal evolution of the system’s distribution in control space corresponding to the time step shown in the top wave profiles. (Also, see main text Fig. 3a.)

  2. Emergence of standing wave order

    Top: membrane profile of MinD at the onset of standing wave order for a control parameter (bulk height) h = 25µm. (Also, see main text Fig. 4c.) Bottom: temporal evolution of the system’s distribution in control space corresponding to the time step shown in the top wave profiles. (Also, see Supplementary Fig. 11b.)

  3. Spiral wave patterns in a 3D box geometry

    Spatiotemporal dynamics of membrane bound MinD in the simulation of the full 3D box geometry for a control parameter (bulk height) h = 100µm.

  4. Chimera transition from standing to travelling waves

    Top: Membrane profile of MinD during the chimera transition from from standing to travelling waves (light grey highlighted area) for a control parameter (bulk height) h = 33µm. (Also, see main text Fig. 5b.) Bottom: temporal evolution of the system’s distribution in control space corresponding to the time step shown in the top wave profiles. (Also, see main text Fig. 5e.) Data points corresponding to the spatial domain in the grey highlighted area (in the top wave profile) are plotted opaque to show the contraction of the distribution in control space to a well 67 defined cycle.

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DOI

https://doi.org/10.1038/s41567-017-0040-5

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