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

Bioinformatics
Enio GjergaJulio Saez-Rodriguez

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.

References

Jun 27, 2012·Bioinformatics·Federica EduatiJulio Saez-Rodriguez
Aug 31, 2012·BMC Systems Biology·Gautier StollLaurence Calzone
Nov 30, 2016·Nature Methods·Dénes TüreiJulio Saez-Rodriguez

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Citations

Jan 1, 2021·Bioinformatics·David Merrell, Anthony Gitter
Mar 9, 2021·Journal of Proteome Research·Enio GjergaJulio Saez-Rodriguez
Jun 3, 2021·International Journal of Molecular Sciences·Gianluca SelvaggioFlorence Janody
Oct 13, 2021·Bioinformatics·Mukta G PalshikarJuilee Thakar

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Software Mentioned

ILP
CellNOpt
CellNOptR
Dynamic
FALCON
R
CNORprob
Feeder
Shiny
CellNetOptimiser

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