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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/13744
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:19:47Zoai: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:34982025-09-29 10:19:47.334CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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