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

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dc.contributor.author Marcon, Luciano, 1983-
dc.contributor.author Diego, Xavier
dc.contributor.author Sharpe, James
dc.contributor.author Müller, Patrick
dc.date.accessioned 2017-01-16T12:00:22Z
dc.date.available 2017-01-16T12:00:22Z
dc.date.issued 2016
dc.identifier.citation Marcon L, Diego Iñiguez J, Sharpe J, Müller P. High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals. eLife. 2016;5:e14022. DOI: 10.7554/eLife.14022
dc.identifier.issn 2050-084X
dc.identifier.uri http://hdl.handle.net/10230/27905
dc.description.abstract 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher eLife
dc.relation.ispartof eLife. 2016;5:e14022
dc.rights © 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.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.7554/eLife.14022
dc.subject.keyword Differential diffusivity
dc.subject.keyword Diffusion-driven instability
dc.subject.keyword Mouse
dc.subject.keyword Pattern formation
dc.subject.keyword S. cerevisiae
dc.subject.keyword Self-organization
dc.subject.keyword Turing patterns
dc.subject.keyword Zebrafish
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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