Reference : FALCON: A Toolbox for the Fast Contextualisation of Logical Networks.
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/31637
FALCON: A Toolbox for the Fast Contextualisation of Logical Networks.
English
De Landtsheer, Sébastien mailto [> >]
Trairatphisan, Panuwat mailto [> >]
Lucarelli, Philippe mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit]
Jul-2017
Bioinformatics (Oxford, England)
Bioinformatics
Yes (verified by ORBilu)
International
1367-4803
1367-4811
England
[en] Motivation: Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. Results: We have developed a computational approach to contextualise logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualise networks, which includes a pipeline for conducting parameter analysis, knockouts, and easy and fast model investigation. The contextualised models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. Availability and implementation: FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON . FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. Contact: thomas.sauter@uni.lu. Supplementary information: Supplementary data are available at Bioinformatics online.
University of Luxembourg: The Faculty of Science, Technology and Communication (FSTC)
Fonds National de la Recherche - FnR ; Horizon 2020
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/31637
10.1093/bioinformatics/btx380
H2020 ; 642295 - MEL-PLEX - Exploiting MELanoma disease comPLEXity to address European research training needs in translational cancer systems biology and cancer systems medicine
FnR ; FNR7643621 > Thomas Sauter > Melanoma sensitivity > Predicting Individual Sensitivity Of Malignant Melanoma To Combination Therapies By Statistical And Network Modeling On Innovative 3D Organotypic Screening Models > 01/05/2015 > 30/04/2018 > 2013

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