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FALCON: a toolbox for the fast contextualization of logical networks

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. RESULT...

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Detalles Bibliográficos
Autores principales: De Landtsheer, Sébastien, Trairatphisan, Panuwat, Lucarelli, Philippe, Sauter, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860161/
https://www.ncbi.nlm.nih.gov/pubmed/28673016
http://dx.doi.org/10.1093/bioinformatics/btx380
Descripción
Sumario: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 contextualize 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 contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.