Cooperativity to increase Turing pattern space for synthetic biology
- Autores
- Diambra, Luis Aníbal; Senthivel, Vivek Raj; Bárcena Menéndez, Diego; Isalan, Mark
- Año de publicación
- 2015
- 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. 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.
Centro Regional de Estudios Genómicos - Materia
-
Biología
Cooperativity
Parameter space
Synthetic biology
Turing patterns - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/85822
Ver los metadatos del registro completo
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Cooperativity to increase Turing pattern space for synthetic biologyDiambra, Luis AníbalSenthivel, Vivek RajBárcena Menéndez, DiegoIsalan, MarkBiologíaCooperativityParameter spaceSynthetic biologyTuring patternsIt 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. 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.Centro Regional de Estudios Genómicos2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf177-186http://sedici.unlp.edu.ar/handle/10915/85822enginfo:eu-repo/semantics/altIdentifier/issn/2161-5063info:eu-repo/semantics/altIdentifier/doi/10.1021/sb500233uinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:16:55Zoai:sedici.unlp.edu.ar:10915/85822Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:16:55.604SEDICI (UNLP) - Universidad Nacional de La Platafalse |
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 Aníbal Biología 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 Aníbal Senthivel, Vivek Raj Bárcena Menéndez, Diego Isalan, Mark |
author |
Diambra, Luis Aníbal |
author_facet |
Diambra, Luis Aníbal Senthivel, Vivek Raj Bárcena Menéndez, Diego Isalan, Mark |
author_role |
author |
author2 |
Senthivel, Vivek Raj Bárcena Menéndez, Diego Isalan, Mark |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Biología Cooperativity Parameter space Synthetic biology Turing patterns |
topic |
Biología Cooperativity Parameter space Synthetic biology Turing patterns |
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. 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. Centro Regional de Estudios Genómicos |
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. 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 |
2015 |
dc.date.none.fl_str_mv |
2015 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo 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://sedici.unlp.edu.ar/handle/10915/85822 |
url |
http://sedici.unlp.edu.ar/handle/10915/85822 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/2161-5063 info:eu-repo/semantics/altIdentifier/doi/10.1021/sb500233u |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
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application/pdf 177-186 |
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