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

View ORCID ProfileEnio Gjerga, View ORCID ProfilePanuwat Trairatphisan, View ORCID ProfileAttila Gabor, Hermann Koch, Celine Chevalier, Francesco Ceccarelli, View ORCID ProfileAurelien Dugourd, View ORCID ProfileAlexander Mitsos, View ORCID ProfileJulio Saez-Rodriguez
doi: https://doi.org/10.1101/2020.03.04.976852
Enio Gjerga
1Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg
2RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
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Panuwat Trairatphisan
1Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg
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Attila Gabor
1Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg
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Hermann Koch
2RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
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Celine Chevalier
3University Paris-Saclay, Espace Technologique Bat. Discovery, 91190 Saint-Aubin, France
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Francesco Ceccarelli
2RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
5University of Cambridge, Cambridge CB2 1TN, United Kingdom
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Aurelien Dugourd
1Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg
2RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
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Alexander Mitsos
4Aachener Verfahrenstechnik – Process Systems Engineering, RWTH Aachen University, Aachen, Germany
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Julio Saez-Rodriguez
1Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF 267, 69120 Heidelberg
2RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
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  • ORCID record for Julio Saez-Rodriguez
  • For correspondence: julio.saez@bioquant.uni-heidelberg.de
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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{at}bioquant.uni-heidelberg.de

Supplementary information Supplemental Text.

Footnotes

  • ↵# Co-first authors

  • https://github.com/saezlab/cellnopt

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 05, 2020.
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Converting networks to predictive logic models from perturbation signalling data with CellNOpt
Enio Gjerga, Panuwat Trairatphisan, Attila Gabor, Hermann Koch, Celine Chevalier, Francesco Ceccarelli, Aurelien Dugourd, Alexander Mitsos, Julio Saez-Rodriguez
bioRxiv 2020.03.04.976852; doi: https://doi.org/10.1101/2020.03.04.976852
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Converting networks to predictive logic models from perturbation signalling data with CellNOpt
Enio Gjerga, Panuwat Trairatphisan, Attila Gabor, Hermann Koch, Celine Chevalier, Francesco Ceccarelli, Aurelien Dugourd, Alexander Mitsos, Julio Saez-Rodriguez
bioRxiv 2020.03.04.976852; doi: https://doi.org/10.1101/2020.03.04.976852

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