Literature DB >> 25484005

Investigating the Turing conditions for diffusion-driven instability in the presence of a binding immobile substrate.

K Korvasová1, E A Gaffney2, P K Maini3, M A Ferreira4, V Klika5.   

Abstract

Turing's diffusion-driven instability for the standard two species reaction-diffusion system is only achievable under well-known and rather restrictive conditions on both the diffusion rates and the kinetic parameters, which necessitates the pairing of a self-activator with a self-inhibitor. In this study we generalize the standard two-species model by considering the case where the reactants can bind to an immobile substrate, for instance extra-cellular matrix, and investigate the influence of this dynamics on Turing's diffusion-driven instability. Such systems have been previously studied on the grounds that binding of the self-activator to a substrate may effectively reduce its diffusion rate and thus induce a Turing instability for species with equal diffusion coefficients, as originally demonstrated by Lengyel and Epstein (1992) under the assumption that the bound state dynamics occurs on a fast timescale. We, however, analyse the full system without any separation of timescales and demonstrate that the full system also allows a relaxation of the standard constraints on the reaction kinetics for the Turing instability, increasing the type of interactions that could give rise to spatial patterning. In particular, we show that two self-activators can undertake a diffusively driven instability in the presence of a binding immobile substrate, highlighting that the interactions required of a putative biological Turing instability need not be associated with a self-activator-self-inhibitor morphogen pair.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords:  Morphogen; Non-diffusive substrate; Pattern formation; Turing instability

Mesh:

Year:  2014        PMID: 25484005     DOI: 10.1016/j.jtbi.2014.11.024

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  7 in total

1.  High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals.

Authors:  Luciano Marcon; Xavier Diego; James Sharpe; Patrick Müller
Journal:  Elife       Date:  2016-04-08       Impact factor: 8.140

2.  History dependence and the continuum approximation breakdown: the impact of domain growth on Turing's instability.

Authors:  Václav Klika; Eamonn A Gaffney
Journal:  Proc Math Phys Eng Sci       Date:  2017-03-15       Impact factor: 2.704

3.  Hierarchical patterning modes orchestrate hair follicle morphogenesis.

Authors:  James D Glover; Kirsty L Wells; Franziska Matthäus; Kevin J Painter; William Ho; Jon Riddell; Jeanette A Johansson; Matthew J Ford; Colin A B Jahoda; Vaclav Klika; Richard L Mort; Denis J Headon
Journal:  PLoS Biol       Date:  2017-07-11       Impact factor: 8.029

Review 4.  Modern perspectives on near-equilibrium analysis of Turing systems.

Authors:  Andrew L Krause; Eamonn A Gaffney; Philip K Maini; Václav Klika
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-11-08       Impact factor: 4.226

Review 5.  Turing pattern design principles and their robustness.

Authors:  Sean T Vittadello; Thomas Leyshon; David Schnoerr; Michael P H Stumpf
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-11-08       Impact factor: 4.226

6.  Turing instability in quantum activator-inhibitor systems.

Authors:  Yuzuru Kato; Hiroya Nakao
Journal:  Sci Rep       Date:  2022-09-16       Impact factor: 4.996

7.  Turing Patterning in Stratified Domains.

Authors:  Andrew L Krause; Václav Klika; Jacob Halatek; Paul K Grant; Thomas E Woolley; Neil Dalchau; Eamonn A Gaffney
Journal:  Bull Math Biol       Date:  2020-10-15       Impact factor: 1.758

  7 in total

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