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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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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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.)
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.)
Spatiotemporal dynamics of membrane bound MinD in the simulation of the full 3D box geometry for a control parameter (bulk height) h = 100µm.
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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Halatek, J., Frey, E. Rethinking pattern formation in reaction–diffusion systems. Nature Phys 14, 507–514 (2018). https://doi.org/10.1038/s41567-017-0040-5
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