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

The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely...

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Detalles Bibliográficos
Autores: Marcon, Luciano, 1983-, Diego, Xavier, Sharpe, James, Müller, Patrick
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/27905
Acceso en línea:http://hdl.handle.net/10230/27905
http://dx.doi.org/10.7554/eLife.14022
Access Level:acceso abierto
Palabra clave:Differential diffusivity
Diffusion-driven instability
Mouse
Pattern formation
S. cerevisiae
Self-organization
Turing patterns
Zebrafish
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spelling High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signalsMarcon, Luciano, 1983-Diego, XavierSharpe, JamesMüller, PatrickDifferential diffusivityDiffusion-driven instabilityMousePattern formationS. cerevisiaeSelf-organizationTuring patternsZebrafishThe Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems.eLife201720172016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/27905http://dx.doi.org/10.7554/eLife.14022reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)IngléseLife. 2016;5:e14022© Copyright Marcon et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/279052026-05-29T05:05:01Z
dc.title.none.fl_str_mv High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
title High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
spellingShingle High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
Marcon, Luciano, 1983-
Differential diffusivity
Diffusion-driven instability
Mouse
Pattern formation
S. cerevisiae
Self-organization
Turing patterns
Zebrafish
title_short High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
title_full High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
title_fullStr High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
title_full_unstemmed High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
title_sort High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
dc.creator.none.fl_str_mv Marcon, Luciano, 1983-
Diego, Xavier
Sharpe, James
Müller, Patrick
author Marcon, Luciano, 1983-
author_facet Marcon, Luciano, 1983-
Diego, Xavier
Sharpe, James
Müller, Patrick
author_role author
author2 Diego, Xavier
Sharpe, James
Müller, Patrick
author2_role author
author
author
dc.subject.none.fl_str_mv Differential diffusivity
Diffusion-driven instability
Mouse
Pattern formation
S. cerevisiae
Self-organization
Turing patterns
Zebrafish
topic Differential diffusivity
Diffusion-driven instability
Mouse
Pattern formation
S. cerevisiae
Self-organization
Turing patterns
Zebrafish
description The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/27905
http://dx.doi.org/10.7554/eLife.14022
url http://hdl.handle.net/10230/27905
http://dx.doi.org/10.7554/eLife.14022
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv eLife. 2016;5:e14022
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv eLife
publisher.none.fl_str_mv eLife
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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