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

Bioinformatics, vol. 36, pp. 4523–4524

Abstract

SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: R-package(s): https://github.com/saezlab/cellnopt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors 8

1. Enio Gjerga

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany

   Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

2. Panuwat Trairatphisan

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany

3. Attila Gábor

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany

4. Hermann Koch Aachen Aachener Verfahrenstechnik (AVT)

   RWTH Aachen University

   Affiliation as printed

   Aachener Verfahrenstechnik, Process Systems Engineering, RWTH Aachen University , Aachen, Germany

   Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

5. Franceco Ceccarelli

   University of Cambridge

   Affiliation as printed

   Computer Laboratory, University of Cambridge , Cambridge CB2 1TN, UK

   Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

6. Aurélien J. F. Dugourd

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany

   Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

7. Alexander Mitsos Aachen Aachener Verfahrenstechnik (AVT)

   RWTH Aachen University

   Affiliation as printed

   Aachener Verfahrenstechnik, Process Systems Engineering, RWTH Aachen University , Aachen, Germany

8. Julio Sáez-Rodríguez corresponding

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany

   Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)