Network design meets in silico evolutionary biology

Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary appr...

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Detalles Bibliográficos
Autores: Rodrigo, Guillermo, Carrera, Javier, Elena, Santiago F.
Tipo de recurso: artículo
Fecha de publicación:2010
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/25233
Acceso en línea:http://hdl.handle.net/10261/25233
Access Level:acceso abierto
Palabra clave:evolutionary optimization
regulatory networks
systems biology
Synthetic biology
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spelling Network design meets in silico evolutionary biologyRodrigo, GuillermoCarrera, JavierElena, Santiago F.evolutionary optimizationregulatory networkssystems biologySynthetic biologyCell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.Supported by fellowships from Generalitat Valenciana and the European Molecular Biology Organization to G. R. and by grants from the Spanish Ministerio de Ciencia e Innovación to J.C. and S.F.E.Peer reviewedElsevier201020102010info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501256835 bytesapplication/pdfhttp://hdl.handle.net/10261/25233reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1016/j.biochimi.2010.04.003info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/252332026-05-22T06:33:51Z
dc.title.none.fl_str_mv Network design meets in silico evolutionary biology
title Network design meets in silico evolutionary biology
spellingShingle Network design meets in silico evolutionary biology
Rodrigo, Guillermo
evolutionary optimization
regulatory networks
systems biology
Synthetic biology
title_short Network design meets in silico evolutionary biology
title_full Network design meets in silico evolutionary biology
title_fullStr Network design meets in silico evolutionary biology
title_full_unstemmed Network design meets in silico evolutionary biology
title_sort Network design meets in silico evolutionary biology
dc.creator.none.fl_str_mv Rodrigo, Guillermo
Carrera, Javier
Elena, Santiago F.
author Rodrigo, Guillermo
author_facet Rodrigo, Guillermo
Carrera, Javier
Elena, Santiago F.
author_role author
author2 Carrera, Javier
Elena, Santiago F.
author2_role author
author
dc.subject.none.fl_str_mv evolutionary optimization
regulatory networks
systems biology
Synthetic biology
topic evolutionary optimization
regulatory networks
systems biology
Synthetic biology
description Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010
2010
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/25233
url http://hdl.handle.net/10261/25233
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.biochimi.2010.04.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 256835 bytes
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
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