Beyond turing: far-from-equilibrium patterns and mechano-chemical feedback

Turing patterns are commonly understood as specific instabilities of a spatially homogeneous steady state, resulting from activator-inhibitor interaction destabilized by diffusion. We argue that this view is restrictive and its agreement with biological observations is problematic. We present two al...

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Hauptverfasser: Veerman, Frits (VerfasserIn) , Mercker, Moritz (VerfasserIn) , Marciniak-Czochra, Anna (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 08 November 2021
In: Philosophical transactions of the Royal Society. A, Mathematical, physical and engineering sciences
Year: 2021, Jahrgang: 379, Heft: 2213, Pages: 1-18
ISSN:1471-2962
DOI:10.1098/rsta.2020.0278
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1098/rsta.2020.0278
Verlag, lizenzpflichtig, Volltext: https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0278
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Verfasserangaben:Frits Veerman, Moritz Mercker, Anna Marciniak-Czochra
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Zusammenfassung:Turing patterns are commonly understood as specific instabilities of a spatially homogeneous steady state, resulting from activator-inhibitor interaction destabilized by diffusion. We argue that this view is restrictive and its agreement with biological observations is problematic. We present two alternatives to the classical Turing analysis of patterns. First, we employ the abstract framework of evolution equations to enable the study of far-from-equilibrium patterns. Second, we introduce a mechano-chemical model, with the surface on which the pattern forms being dynamic and playing an active role in the pattern formation, effectively replacing the inhibitor. We highlight the advantages of these two alternatives vis-à-vis the classical Turing analysis, and give an overview of recent results and future challenges for both approaches. - - This article is part of the theme issue ‘Recent progress and open frontiers in Turing’s theory of morphogenesis’.
Beschreibung:Gesehen am 06.12.2021
Beschreibung:Online Resource
ISSN:1471-2962
DOI:10.1098/rsta.2020.0278