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IDESS: a toolbox for identification and automated design of stochastic gene circuits
dc.contributor.author | Sequeiros Ferreiro, Carlos Xosé | |
dc.contributor.author | Pájaro Diéguez, Manuel | |
dc.contributor.author | Vázquez, Carlos | |
dc.contributor.author | Banga, Julio R. | |
dc.contributor.author | Otero-Muras, Irene | |
dc.date.accessioned | 2024-05-06T14:29:14Z | |
dc.date.available | 2024-05-06T14:29:14Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | Carlos Sequeiros, Manuel Pájaro, Carlos Vázquez, Julio R Banga, Irene Otero-Muras, IDESS: a toolbox for identification and automated design of stochastic gene circuits, Bioinformatics, Volume 39, Issue 11, November 2023, btad682, https://doi.org/10.1093/bioinformatics/btad682 | es_ES |
dc.identifier.issn | 1367-4811 | |
dc.identifier.uri | http://hdl.handle.net/2183/36413 | |
dc.description.abstract | [Abstract]: Motivation One of the main causes hampering predictability during the model identification and automated design of gene circuits in synthetic biology is the effect of molecular noise. Stochasticity may significantly impact the dynamics and function of gene circuits, specially in bacteria and yeast due to low mRNA copy numbers. Standard stochastic simulation methods are too computationally costly in realistic scenarios to be applied to optimization-based design or parameter estimation. Results In this work, we present IDESS (Identification and automated Design of Stochastic gene circuitS), a software toolbox for optimization-based design and model identification of gene regulatory circuits in the stochastic regime. This software incorporates an efficient approximation of the Chemical Master Equation as well as a stochastic simulation algorithm—both with GPU and CPU implementations—combined with global optimization algorithms capable of solving Mixed Integer Nonlinear Programming problems. The toolbox efficiently addresses two types of problems relevant in systems and synthetic biology: the automated design of stochastic synthetic gene circuits, and the parameter estimation for model identification of stochastic gene regulatory networks. Availability and implementation IDESS runs under the MATLAB environment and it is available under GPLv3 license at https://doi.org/10.5281/zenodo.7788692. | es_ES |
dc.description.sponsorship | This work was supported by grant PID2020-117271RB-C22 (BIODYNAMICS) funded by MCIN/AEI/10.13039/501100011033 and the CSIC intramural project grant PIE DAOBIO [PIE 202070E036] to J.R.B.; the PID2019-108584RB-I00, funded by Spanish MCIN/AEI to C.V.; the Galician Government [ED431C 2022/47, ED431G 2019/01, both including FEDER funds] to M.P., C.S., and C.V.; and grant PID2021-127888NA-I00 (COMPSYNBIO) funded by MCIN/AEI/10.13039/501100011033, grant TED2021-131049B-I00 (BioEcoDBTL) funded by MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR and grant CIAICO/2021/159 (SmartBioFab) funded by Generalitat Valenciana to I.O.-M. The funding bodies played no role in the study design, the data collection and analysis, or the manuscript writing. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/47 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Generalitat Valenciana; CIAICO/2021/159 | es_ES |
dc.description.sponsorship | CSIC; PIE 202070E036 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Oxford University Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117271RB-C22/ES/REGULACION DINAMICA EN VARIAS ESCALAS DE INGENIERIA METABOLICA: INFERENCIA MULTIMODELO Y OPTIMALIDAD DINAMICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108584RB-I00/ES/METODOS MATEMATICOS Y COMPUTACIONALES PARA NUEVOS RETOS EN FINANZAS CUANTITATIVAS, MEDIAMBIENTE, BIOTECNOLOGIA E INGENIERIA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127888NA-I00/ES/DISEÑO, ANALISIS Y CONTROL AVANZADOS DE CIRCUITOS BIOMOLECULARES SINTETICOS EN PRESENCIA DE RUIDO MOLECULAR | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-131049B-I00/ES/BIODISEÑO PARA LA BIOECONOMIA: BIOMANUFACTURA EFICIENTE BASADA EN EL CICLO DISEÑO-IMPLEMENTACION-EVALUACION-ANALISIS | es_ES |
dc.relation.uri | https://doi.org/10.1093/bioinformatics/btad682 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Bioinformatics | es_ES |
dc.subject | Computational Biology | es_ES |
dc.subject | IDESS | es_ES |
dc.subject | Automated design | es_ES |
dc.subject | Stochastic synthetic gene circuits | es_ES |
dc.subject | Simulation methods | es_ES |
dc.title | IDESS: a toolbox for identification and automated design of stochastic gene circuits | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Bioinformatics | es_ES |
UDC.volume | 39 | es_ES |
UDC.issue | 11 | es_ES |
UDC.startPage | btad682 | es_ES |
dc.identifier.doi | 10.1093/bioinformatics/btad682 |
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