From one pattern into another: analysis of Turing patterns in heterogeneous domains via WKBJ
Language English Country Great Britain, England Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
Grant support
BB/N006097/1
Biotechnology and Biological Sciences Research Council - United Kingdom
PubMed
31937231
PubMed Central
PMC7014807
DOI
10.1098/rsif.2019.0621
Knihovny.cz E-resources
- Keywords
- Turing instabilities, WKBJ, heterogeneity, pattern formation,
- MeSH
- Models, Biological * MeSH
- Diffusion MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Pattern formation from homogeneity is well studied, but less is known concerning symmetry-breaking instabilities in heterogeneous media. It is non-trivial to separate observed spatial patterning due to inherent spatial heterogeneity from emergent patterning due to nonlinear instability. We employ WKBJ asymptotics to investigate Turing instabilities for a spatially heterogeneous reaction-diffusion system, and derive conditions for instability which are local versions of the classical Turing conditions. We find that the structure of unstable modes differs substantially from the typical trigonometric functions seen in the spatially homogeneous setting. Modes of different growth rates are localized to different spatial regions. This localization helps explain common amplitude modulations observed in simulations of Turing systems in heterogeneous settings. We numerically demonstrate this theory, giving an illustrative example of the emergent instabilities and the striking complexity arising from spatially heterogeneous reaction-diffusion systems. Our results give insight both into systems driven by exogenous heterogeneity, as well as successive pattern forming processes, noting that most scenarios in biology do not involve symmetry breaking from homogeneity, but instead consist of sequential evolutions of heterogeneous states. The instability mechanism reported here precisely captures such evolution, and extends Turing's original thesis to a far wider and more realistic class of systems.
See more in PubMed
Turing AM. 1952. The chemical basis of morphogenesis. Phil. Trans. R Soc. Lond. B 237, 37–72. (10.1098/rstb.1952.0012) PubMed DOI PMC
De Kepper P, Castets V, Dulos E, Boissonade J. 1991. Turing-type chemical patterns in the chlorite–iodide–malonic acid reaction. Physica D 49, 161–169. (10.1016/0167-2789(91)90204-M) DOI
Cross MC, Hohenberg PC. 1993. Pattern formation outside of equilibrium. Rev. Mod. Phys. 65, 851–1112. (10.1103/RevModPhys.65.851) DOI
Kondo S, Miura T. 2010. Reaction–diffusion model as a framework for understanding biological pattern formation. Science 329, 1616–1620. (10.1126/science.1179047) PubMed DOI
Murray JD. 2004. Mathematical biology, interdisciplinary applied mathematics. New York, NY: Springer.
Green JBA, Sharpe J. 2015. Positional information and reaction–diffusion: two big ideas in developmental biology combine. Development 142, 1203–1211. (10.1242/dev.114991) PubMed DOI
Woolley T. 2014. Mighty morphogenesis. In 50 Visions of mathematics (ed. Parc S.), pp. 180–183. Oxford, UK: Oxford University Press.
Sheth R, Marcon L, Bastida MF, Junco M, Quintana L, Dahn R, Kmita M, Sharpe J, Ros MA. 2012. Hox genes regulate digit patterning by controlling the wavelength of a Turing-type mechanism. Science 338, 1476–1480. (10.1126/science.1226804) PubMed DOI PMC
Wolpert L. 2016. Positional information and pattern formation. In Current topics in developmental biology, vol. 117, pp. 597–608. London, UK: Academic Press. PubMed
Holloway DM. 1995. Reaction-diffusion theory of vertebrate organogenesis. PhD thesis, University of British Columbia.
Warmflash A, Sorre B, Etoc F, Siggia ED, Brivanlou AH. 2014. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat. Methods 11, 847–854. (10.1038/nmeth.3016) PubMed DOI PMC
Weber EL, Woolley TE, Yeh C-Y, Ou K-L, Maini PK, Chuong C-M. 2019. Self-organizing hair peg-like structures from dissociated skin progenitor cells: new insights for human hair follicle organoid engineering and Turing patterning in an asymmetric morphogenetic field. Exp. Dermatol. 28, 355–366. (10.1111/exd.13891) PubMed DOI PMC
Meinhardt H. 1983. Cell determination boundaries as organizing regions for secondary embryonic fields. Dev. Biol. 96, 375–385. (10.1016/0012-1606(83)90175-6) PubMed DOI
Irvine KD, Rauskolb C. 2001. Boundaries in development: formation and function. Annu. Rev. Cell Dev. Biol. 17, 189–214. (10.1146/annurev.cellbio.17.1.189) PubMed DOI
Pickett STA, Cadenasso ML. 1995. Landscape ecology: spatial heterogeneity in ecological systems. Science 269, 331–334. (10.1126/science.269.5222.331) PubMed DOI
Clobert J, Le Galliard J-F, Cote J, Meylan S, Massot M. 2009. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209. (10.1111/j.1461-0248.2008.01267.x) PubMed DOI
Cobbold CA, Lutscher F, Sherratt JA. 2015. Diffusion-driven instabilities and emerging spatial patterns in patchy landscapes. Ecol. Complex. 24, 69–81. (10.1016/j.ecocom.2015.10.001) DOI
Bassett A, Krause AL, Van Gorder RA. 2017. Continuous dispersal in a model of predator–prey–subsidy population dynamics. Ecol. Modell. 354, 115–122. (10.1016/j.ecolmodel.2017.02.017) DOI
Kurowski L, Krause AL, Mizuguchi H, Grindrod P, Van Gorder RA. 2017. Two-species migration and clustering in two-dimensional domains. Bull. Math. Biol. 79, 2302–2333. (10.1007/s11538-017-0331-0) PubMed DOI PMC
Crampin EJ, Hackborn WW, Maini PK. 2002. Pattern formation in reaction–diffusion models with nonuniform domain growth. Bull. Math. Biol. 64, 747–769. (10.1006/bulm.2002.0295) PubMed DOI
Krause AL, Ellis MA, Van Gorder RA. 2019. Influence of curvature, growth, and anisotropy on the evolution of Turing patterns on growing manifolds. Bull. Math. Biol. 81, 759–799. (10.1007/s11538-018-0535-y) PubMed DOI PMC
Sun G-Q, Jusup M, Jin Z, Wang Y, Wang Z. 2016. Pattern transitions in spatial epidemics: mechanisms and emergent properties. Phys. Life Rev. 19, 43–73. (10.1016/j.plrev.2016.08.002) PubMed DOI PMC
Belmonte-Beitia J, Woolley TE, Scott JG, Maini PK, Gaffney EA. 2013. Modelling biological invasions: individual to population scales at interfaces. J. Theor. Biol. 334, 1–12. (10.1016/j.jtbi.2013.05.033) PubMed DOI
Breña Medina VF, Avitabile D, Champneys AR, Ward MJ. 2015. Stripe to spot transition in a plant root hair initiation model. SIAM J. Appl. Math. 75, 1090 (111910.1137/140964527) DOI
Avitabile D, Breña Medina VF, Ward MJ. 2018. Spot dynamics in a reaction–diffusion model of plant root hair initiation. SIAM J. Appl. Math. 78, 291–319. (10.1137/17M1120932) DOI
Benson DL, Sherratt JA, Maini PK. 1993. Diffusion driven instability in an inhomogeneous domain. Bull. Math. Biol. 55, 365–384. (10.1016/S0092-8240(05)80270-8) DOI
Page K, Maini PK, Monk NAM. 2003. Pattern formation in spatially heterogeneous Turing reaction–diffusion models. Physica D 181, 80–101. (10.1016/S0167-2789(03)00068-X) DOI
Page KM, Maini PK, Monk NAM. 2005. Complex pattern formation in reaction–diffusion systems with spatially varying parameters. Physica D 202, 95–115. (10.1016/j.physd.2005.01.022) DOI
Iron D, Ward MJ. 2001. Spike pinning for the Gierer–Meinhardt model. Math. Comput. Simul. 55, 419–431. (10.1016/S0378-4754(00)00303-7) DOI
Ward MJ, McInerney D, Houston P, Gavaghan D, Maini P. 2002. The dynamics and pinning of a spike for a reaction–diffusion system. SIAM J. Appl. Math. 62, 1297–1328. (10.1137/S0036139900375112) DOI
Wei J, Winter M, Yang W. 2017. Stable spike clusters for the precursor Gierer–Meinhardt system in PubMed DOI PMC
Krause AL, Klika V, Woolley TE, Gaffney EA. 2018. Heterogeneity induces spatiotemporal oscillations in reaction–diffusion systems. Phys. Rev. E 97, 052206 (10.1103/PhysRevE.97.052206) PubMed DOI
Kolokolnikov T, Wei J. 2018. Pattern formation in a reaction–diffusion system with space-dependent feed rate. SIAM Rev. 60, 626–645. (10.1137/17M1116027) DOI
Auchmuty JFG, Nicolis G. 1975. Bifurcation analysis of nonlinear reaction–diffusion equations—I. Evolution equations and the steady state solutions. Bull. Math. Biol. 37, 323–365. (10.1016/s0092-8240(75)80036-x) DOI
Doelman A, van Heijster P, Shen J. 2018. Pulse dynamics in reaction–diffusion equations with strong spatially localized impurities. Phil. Trans. R. Soc. A 376, 20170183 (10.1098/rsta.2017.0183) PubMed DOI PMC
Kozák M, Gaffney EA, Klika V. 2019. Pattern formation in reaction-diffusion systems with piece-wise kinetic modulation: an example study of heterogeneous kinetics. Phys. Rev. E 100, 042220 (10.1103/PhysRevE.100.042220) PubMed DOI
Lengyel I, Epstein IR. 1991. Modeling of Turing structures in the chlorite–iodide–malonic acid–starch reaction system. Science 251, 650 (65210.1126/science.251.4994.650) PubMed DOI
Epstein IR, Showalter K. 1996. Nonlinear chemical dynamics: oscillations, patterns, and chaos. J. Phys. Chem. 100, 13132–13147. (10.1021/jp953547m) DOI
Rüdiger S, Míguez DG, Munuzuri AP, Sagués F, Casademunt J. 2003. Dynamics of Turing patterns under spatiotemporal forcing. Phys. Rev. Lett. 90, 128301 (10.1103/PhysRevLett.90.128301) PubMed DOI
Míguez DG, Pérez-Villar V, Muñuzuri AP. 2005. Turing instability controlled by spatiotemporal imposed dynamics. Phys. Rev. E 71, 066217 (10.1103/PhysRevE.71.066217) PubMed DOI
Rüdiger S, Nicola EM, Casademunt J, Kramer L. 2007. Theory of pattern forming systems under traveling-wave forcing. Phys. Rep. 447, 73–111. (10.1016/j.physrep.2007.02.017) DOI
Yang L, Dolnik M, Zhabotinsky AM, Epstein IR. 2002. Spatial resonances and superposition patterns in a reaction–diffusion model with interacting Turing modes. Phys. Rev. Lett. 88, 208303 (10.1103/PhysRevLett.88.208303) PubMed DOI
Peter R, Hilt M, Ziebert F, Bammert J, Erlenkämper C, Lorscheid N, Weitenberg C, Winter A, Hammele M, Zimmermann W. 2005. Stripe–hexagon competition in forced pattern-forming systems with broken up-down symmetry. Phys. Rev. E 71, 046212 (10.1103/PhysRevE.71.046212) PubMed DOI
Haim L, Hagberg A, Meron E. 2015. Non-monotonic resonance in a spatially forced Lengyel-Epstein model. Chaos 25, 064307 (10.1063/1.4921768) PubMed DOI
Dewel G, Borckmans P. 1989. Effects of slow spatial variations on dissipative structures. Phys. Lett. A 138, 189–192. (10.1016/0375-9601(89)90025-X) DOI
Kuske R, Eckhaus W. 1997. Pattern formation in systems with slowly varying geometry. SIAM J. Appl. Math. 57, 112–152. (10.1137/S0036139994277531) DOI
Otsuji M, Ishihara S, Co C, Kaibuchi K, Mochizuki A, Kuroda S. 2007. A mass conserved reaction–diffusion system captures properties of cell polarity. PLoS Comput. Biol. 3, e108 (10.1371/journal.pcbi.0030108) PubMed DOI PMC
Mendez V, Fedotov S, Horsthemke W. 2010. Reaction-transport systems: mesoscopic foundations, fronts, and spatial instabilities. New York, NY: Springer-Verlag.
Klika V, Kozák M, Gaffney EA. 2018. Domain size driven instability: self-organization in systems with advection. SIAM J. Appl. Math. 78, 2298–2322. (10.1137/17M1138571) DOI
Klika V. 2017. Significance of non-normality-induced patterns: Transient growth versus asymptotic stability. Chaos 27, 073120 (10.1063/1.4985256) PubMed DOI
Maini PK, Woolley TE, Baker RE, Gaffney EA, Lee SS. 2012. Turing’s model for biological pattern formation and the robustness problem. Interface Focus 2, 487–496. (10.1098/rsfs.2011.0113) PubMed DOI PMC
Woolley TE, Baker RE, Maini PK. 2017. In The incomputable, pp. 219–235. Berlin, Germany: Springer.
Dwyer HI, Zettl A. 1995. Electronic Journal of Differential Equations (EJDE)[electronic only] 1995.
Malamud MM. 2005. In Sturm-Liouville Theory, pp. 237–270. Berlin, Germany: Springer.
Bremmer H. 1951. The W.K.B. approximation as the first term of a geometric-optical series. Commun. Pure Appl. Math. 4, 105–115. (10.1002/cpa.3160040111) DOI
Alder K, Pauli HKA. 1969. Quantal corrections and the WKB approximation of multiple Coulomb excitation. Nucl. Phys. A 128, 193–208. (10.1016/0375-9474(69)90985-3) DOI
Griffiths DJ, Schroeter DF. 2018. Introduction to quantum mechanics. Cambridge, UK: Cambridge University Press.
Bender CM, Orszag SA. 2013. Advanced mathematical methods for scientists and engineers I: asymptotic methods and perturbation theory. Springer Science & Business Media.
Tennyson CN, Klamut HJ, Worton RG. 1995. The human dystrophin gene requires 16 hours to be transcribed and is cotranscriptionally spliced. Nat. Genet. 9, 184–190. (10.1038/ng0295-184) PubMed DOI
Singh J, Padgett RA. 2009. Rates of in situ transcription and splicing in large human genes. Nat. Struct. Mol. Biol. 16, 1128–1133. (10.1038/nsmb.1666) PubMed DOI PMC
Sekine R, Shibata T, Ebisuya M. 2018. Synthetic mammalian pattern formation driven by differential diffusivity of Nodal and Lefty. Nat. Commun. 9, 5456 (10.1038/s41467-018-07847-x) PubMed DOI PMC
Crawford JD. 1991. Introduction to bifurcation theory. Rev. Mod. Phys. 63, 991 (103710.1103/RevModPhys.63.991) DOI
Wollkind DJ, Manoranjan VS, Zhang L. 1994. Weakly nonlinear stability analyses of prototype reaction–diffusion model equations. SIAM Rev. 36, 176–214. (10.1137/1036052) DOI
Stephenson LE, Wollkind DJ. 1995. Weakly nonlinear stability analyses of one-dimensional Turing pattern formation in activator–inhibitor/immobilizer model systems. J. Math. Biol. 33, 771–815. (10.1007/BF00187282) DOI
Chen Y, Buceta J. 2019. A non-linear analysis of Turing pattern formation. PLoS ONE 14, e0220994 (10.1371/journal.pone.0220994) PubMed DOI PMC
Gierer A, Meinhardt H. 1972. A theory of biological pattern formation. Kybernetik 12, 30–39. (10.1007/BF00289234) PubMed DOI
Turing AM. 1950. I.–Computing machinery and intelligence. Mind 59, 433–460. (10.1093/mind/LIX.236.433) DOI
Turing Instabilities are Not Enough to Ensure Pattern Formation
Modern perspectives on near-equilibrium analysis of Turing systems
Isolating Patterns in Open Reaction-Diffusion Systems
Turing Patterning in Stratified Domains