dc.creator | Cao, Hongqing | es |
dc.creator | Romero Campero, Francisco José | es |
dc.creator | Heeb, Stephan | es |
dc.creator | Cámara, Miguel | es |
dc.creator | Krasnogor, Natalio | es |
dc.date.accessioned | 2021-05-28T10:15:02Z | |
dc.date.available | 2021-05-28T10:15:02Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Cao, H., Romero Campero, F.J., Heeb, S., Cámara, M. y Krasnogor, N. (2010). Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology, 4 (1), 55-84. | |
dc.identifier.issn | 1872-5325 | es |
dc.identifier.uri | https://hdl.handle.net/11441/110941 | |
dc.description.abstract | This paper proposes a new methodology for the
automated design of cell models for systems and synthetic
biology. Our modelling framework is based on P systems, a
discrete, stochastic and modular formal modelling language.
The automated design of biological models comprising
the optimization of the model structure and its
stochastic kinetic constants is performed using an evolutionary
algorithm. The evolutionary algorithm evolves
model structures by combining different modules taken
from a predefined module library and then it fine-tunes the
associated stochastic kinetic constants. We investigate four
alternative objective functions for the fitness calculation
within the evolutionary algorithm: (1) equally weighted
sum method, (2) normalization method, (3) randomly
weighted sum method, and (4) equally weighted product
method. The effectiveness of the methodology is tested on
four case studies of increasing complexity including negative
and positive autoregulation as well as two gene networks
implementing a pulse generator and a bandwidth
detector. We provide a systematic analysis of the evolutionary
algorithm’s results as well as of the resulting
evolved cell models. | es |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) EP/ E017215/1 | es |
dc.description.sponsorship | Biotechnology and Biological Sciences Research Council (BBSRC) BB/F01855X/1 | es |
dc.format | application/pdf | es |
dc.format.extent | 30 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Systems and Synthetic Biology, 4 (1), 55-84. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Systems biology | es |
dc.subject | Synthetic biology | es |
dc.subject | P systems | es |
dc.subject | Evolutionary algorithms | es |
dc.subject | Automated model design | es |
dc.title | Evolving cell models for systems and synthetic biology | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | EP/ E017215/1 | es |
dc.relation.projectID | BB/F01855X/1 | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007%2Fs11693-009-9050-7 | es |
dc.identifier.doi | 10.1007/s11693-009-9050-7 | es |
dc.journaltitle | Systems and Synthetic Biology | es |
dc.publication.volumen | 4 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 55 | es |
dc.publication.endPage | 84 | es |
dc.identifier.sisius | 6600099 | es |
dc.contributor.funder | Engineering and Physical Sciences Research Council (EPSRC) | es |
dc.contributor.funder | Biotechnology and Biological Sciences Research Council (BBSRC) | es |