dc.creator | García Nieto, José Manuel | es |
dc.creator | Nebro, Antonio J. | es |
dc.creator | Aldana Montes, José F. | es |
dc.date.accessioned | 2021-05-10T07:55:41Z | |
dc.date.available | 2021-05-10T07:55:41Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | García Nieto, J.M., Nebro, A.J. y Aldana Montes, J.F. (2019). Inference of gene regulatory networks with multi-objective cellular genetic algorithm. Computational Biology and Chemistry, 80 (June 2019), 409-418. | |
dc.identifier.issn | 1476-9271 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108739 | |
dc.description.abstract | Reverse engineering of biochemical networks remains an important open challenge in computational systems
biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological
structure and parameters that quantitatively understand and reproduce the dynamics of biological systems.
In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene
Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of
the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multiobjective
formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the
kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty
weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of
parameter learning and network topology inference for the proposed approach. The resulting software, called
MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the
DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness
to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach
offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of
trade-off solutions involving GRNs with different typologies and MSEs. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2017-86049-R | es |
dc.description.sponsorship | Junta de Andalucía P12- TIC-1519 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Computational Biology and Chemistry, 80 (June 2019), 409-418. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multi-objective optimization | es |
dc.subject | Cellular genetic algorithms | es |
dc.subject | Gene regulatory networks | es |
dc.subject | DREAM challenge | es |
dc.title | Inference of gene regulatory networks with multi-objective cellular genetic algorithm | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | 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 | TIN2017-86049-R | es |
dc.relation.projectID | P12- TIC-1519 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1476927118305097 | es |
dc.identifier.doi | 10.1016/j.compbiolchem.2019.05.003 | es |
dc.journaltitle | Computational Biology and Chemistry | es |
dc.publication.volumen | 80 | es |
dc.publication.issue | June 2019 | es |
dc.publication.initialPage | 409 | es |
dc.publication.endPage | 418 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |