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

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Motivation The molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large data-sets is increasing steadily as new experimental approaches are developed. 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. Logic models scale better with network size than alternative kinetic models, while keeping the interpretation of the model simple, making them particularly suitable for large datasets. Results 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. These updates include (i) an Integer Linear Programming (ILP) formulation which guarantees efficient optimisation for Boolean models, (ii) a probabilistic logic implementation for semi-quantitative datasets and (iii) the integration of MaBoSS, a stochastic Boolean simulator. Furthermore, we introduce Dynamic-Feeder, a tool to identify missing links not present in the prior knowledge. We have also implemented systematic post-hoc analyses to highlight the key components and parameters of our models. Finally, we provide an R-Shiny tool to run CellNOpt interactively. Availability R-package(s): https://github.com/saezlab/cellnopt Contact julio.saez@bioquant.uni-heidelberg.de Supplementary information Supplemental Text.

Authors 8

1. Enio Gjerga Aachen Faculty of Medicine Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   Heidelberg University · RWTH Aachen University

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg

   RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 6920 Heidelberg

2. Panuwat Trairatphisan

   Heidelberg University

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 6920 Heidelberg

3. Attila Gábor

   Heidelberg University

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 6920 Heidelberg

4. Hermann Koch Aachen Faculty of Medicine Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany

5. Francesco Ceccarelli Aachen Faculty of Medicine Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   University of Cambridge · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany

   University of Cambridge, Cambridge CB2 1TN, United Kingdom

6. Aurélien J. F. Dugourd Aachen Faculty of Medicine Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   Heidelberg University · RWTH Aachen University

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg

   RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 6920 Heidelberg

7. Alexander Mitsos Aachen Aachener Verfahrenstechnik - Process Systems Engineering

   RWTH Aachen University

   Affiliation as printed

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

8. Julio Sáez-Rodríguez corresponding Aachen Faculty of Medicine Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   Heidelberg University · RWTH Aachen University

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg

   RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany

   Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 6920 Heidelberg