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

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...

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Hauptverfasser: Gjerga, Enio (VerfasserIn) , Trairatphisan, Panuwat (VerfasserIn) , Gabor, Attila (VerfasserIn) , Koch, Hermann (VerfasserIn) , Chevalier, Celine (VerfasserIn) , Ceccarelli, Franceco (VerfasserIn) , Dugourd, Aurélien (VerfasserIn) , Mitsos, Alexander (VerfasserIn) , Sáez Rodríguez, Julio (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 09 June 2020
In: Bioinformatics
Year: 2020, Jahrgang: 36, Heft: 16, Pages: 4523-4524
ISSN:1367-4811
DOI:10.1093/bioinformatics/btaa561
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/bioinformatics/btaa561
Verlag, lizenzpflichtig, Volltext: https://academic.oup.com/bioinformatics/article/36/16/4523/5855133
Volltext
Verfasserangaben:Enio Gjerga, Panuwat Trairatphisan, Attila Gabor, Hermann Koch, Celine Chevalier, Franceco Ceccarelli, Aurelien Dugourd, Alexander Mitsos and Julio Saez-Rodriguez
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 09.02.2021
Beschreibung:Online Resource
ISSN:1367-4811
DOI:10.1093/bioinformatics/btaa561

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