Converting networks to predictive logic models from perturbation signalling data with CellNOpt

• Verfasst von: Gjerga, Enio [VerfasserIn]
• Verfasst von: Trairatphisan, Panuwat [VerfasserIn]
• Verfasst von: Gabor, Attila [VerfasserIn]
• Verfasst von: Koch, Hermann [VerfasserIn]
• Verfasst von: Chevalier, Celine [VerfasserIn]
• Verfasst von: Ceccarelli, Franceco [VerfasserIn]
• Verfasst von: Dugourd, Aurélien [VerfasserIn]
• Verfasst von: Mitsos, Alexander [VerfasserIn]
• Verfasst von: Sáez Rodríguez, Julio [VerfasserIn]
• Titel: Converting networks to predictive logic models from perturbation signalling data with CellNOpt
• Verf.angabe: Enio Gjerga, Panuwat Trairatphisan, Attila Gabor, Hermann Koch, Celine Chevalier, Franceco Ceccarelli, Aurelien Dugourd, Alexander Mitsos and Julio Saez-Rodriguez
• E-Jahr: 2020
• Jahr: 09 June 2020
• Umfang: 2 S.
• Fussnoten: Gesehen am 09.02.2021
• Titel Quelle: Enthalten in: Bioinformatics
• Ort Quelle: Oxford : Oxford Univ. Press, 1998
• Jahr Quelle: 2020
• Band/Heft Quelle: 36(2020), 16, Seite 4523-4524
• ISSN Quelle: 1367-4811
• DOI: doi:10.1093/bioinformatics/btaa561
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1747797979

Abstract

The molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large datasets is increasing steadily. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. CellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones to keep up with the computational demand of increasingly large datasets, including (i) an efficient integer linear programming, (ii) a probabilistic logic implementation for semi-quantitative datasets, (iii) the integration of a stochastic Boolean simulator, (iv) a tool to identify missing links, (v) systematic post-hoc analyses and (vi) an R-Shiny tool to run CellNOpt interactively.R-package(s): https://github.com/saezlab/cellnopt.Supplementary data are available at Bioinformatics online.