Cooperativity to increase Turing Pattern space for synthetic biology

Autores
Diambra, Luis Anibal; Senthivel, Vivek Raj; Barcena Menendez, Diego; Isalan, Mark
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction–diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose–response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction–diffusion systems and will help to guide projects to engineer synthetic Turing patterns.
Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Regulación Genómica; España
Fil: Senthivel, Vivek Raj. Centro de Regulación Genómica; España. Imperial College London; Reino Unido
Fil: Barcena Menendez, Diego. Centro de Regulación Genómica; España. Imperial College London; Reino Unido
Fil: Isalan, Mark. Centro de Regulación Genómica; España. Imperial College London; Reino Unido
Materia
Cooperativity
Parameter space
Synthetic biology
Turing patterns
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/13744
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/13744
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Cooperativity to increase Turing Pattern space for synthetic biologyDiambra, Luis AnibalSenthivel, Vivek RajBarcena Menendez, DiegoIsalan, MarkCooperativityParameter spaceSynthetic biologyTuring patternshttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction–diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose–response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction–diffusion systems and will help to guide projects to engineer synthetic Turing patterns.Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Regulación Genómica; EspañaFil: Senthivel, Vivek Raj. Centro de Regulación Genómica; España. Imperial College London; Reino UnidoFil: Barcena Menendez, Diego. Centro de Regulación Genómica; España. Imperial College London; Reino UnidoFil: Isalan, Mark. Centro de Regulación Genómica; España. Imperial College London; Reino UnidoAmerican Chemical Society2014-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/13744Diambra, Luis Anibal; Senthivel, Vivek Raj; Barcena Menendez, Diego; Isalan, Mark; Cooperativity to increase Turing Pattern space for synthetic biology; American Chemical Society; ACS Synthetic Biology; 4; 2; 8-2014; 177-1862161-5063enginfo:eu-repo/semantics/altIdentifier/doi/10.1021/sb500233uinfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/sb500233uinfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4384830/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:52:27Zoai:ri.conicet.gov.ar:11336/13744instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:52:28.054CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Cooperativity to increase Turing Pattern space for synthetic biology
title Cooperativity to increase Turing Pattern space for synthetic biology
spellingShingle Cooperativity to increase Turing Pattern space for synthetic biology
Diambra, Luis Anibal
Cooperativity
Parameter space
Synthetic biology
Turing patterns
title_short Cooperativity to increase Turing Pattern space for synthetic biology
title_full Cooperativity to increase Turing Pattern space for synthetic biology
title_fullStr Cooperativity to increase Turing Pattern space for synthetic biology
title_full_unstemmed Cooperativity to increase Turing Pattern space for synthetic biology
title_sort Cooperativity to increase Turing Pattern space for synthetic biology
dc.creator.none.fl_str_mv Diambra, Luis Anibal
Senthivel, Vivek Raj
Barcena Menendez, Diego
Isalan, Mark
author Diambra, Luis Anibal
author_facet Diambra, Luis Anibal
Senthivel, Vivek Raj
Barcena Menendez, Diego
Isalan, Mark
author_role author
author2 Senthivel, Vivek Raj
Barcena Menendez, Diego
Isalan, Mark
author2_role author
author
author
dc.subject.none.fl_str_mv Cooperativity
Parameter space
Synthetic biology
Turing patterns
topic Cooperativity
Parameter space
Synthetic biology
Turing patterns
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction–diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose–response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction–diffusion systems and will help to guide projects to engineer synthetic Turing patterns.
Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Regulación Genómica; España
Fil: Senthivel, Vivek Raj. Centro de Regulación Genómica; España. Imperial College London; Reino Unido
Fil: Barcena Menendez, Diego. Centro de Regulación Genómica; España. Imperial College London; Reino Unido
Fil: Isalan, Mark. Centro de Regulación Genómica; España. Imperial College London; Reino Unido
description It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction–diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose–response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction–diffusion systems and will help to guide projects to engineer synthetic Turing patterns.
publishDate 2014
dc.date.none.fl_str_mv 2014-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/13744
Diambra, Luis Anibal; Senthivel, Vivek Raj; Barcena Menendez, Diego; Isalan, Mark; Cooperativity to increase Turing Pattern space for synthetic biology; American Chemical Society; ACS Synthetic Biology; 4; 2; 8-2014; 177-186
2161-5063
url http://hdl.handle.net/11336/13744
identifier_str_mv Diambra, Luis Anibal; Senthivel, Vivek Raj; Barcena Menendez, Diego; Isalan, Mark; Cooperativity to increase Turing Pattern space for synthetic biology; American Chemical Society; ACS Synthetic Biology; 4; 2; 8-2014; 177-186
2161-5063
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/sb500233u
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/sb500233u
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4384830/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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