Motivation: The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. Results: P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u SMV, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco ASP is implemented. The package is function-based and uses only native Python and N etwork X data types. Availability and Implementation: https://github.com/hklarner/PyBoolNet. Contact: hannes.klarner@fu-berlin.de.
Motivation: The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. Results: P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u SMV, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco ASP is implemented. The package is function-based and uses only native Python and N etwork X data types. Availability and Implementation: https://github.com/hklarner/PyBoolNet. Contact: hannes.klarner@fu-berlin.de.
Authors: Anna Niarakis; Martin Kuiper; Marek Ostaszewski; Rahuman S Malik Sheriff; Cristina Casals-Casas; Denis Thieffry; Tom C Freeman; Paul Thomas; Vasundra Touré; Vincent Noël; Gautier Stoll; Julio Saez-Rodriguez; Aurélien Naldi; Eugenia Oshurko; Ioannis Xenarios; Sylvain Soliman; Claudine Chaouiya; Tomáš Helikar; Laurence Calzone Journal: Brief Bioinform Date: 2021-03-22 Impact factor: 11.622
Authors: Aurélien Naldi; Céline Hernandez; Nicolas Levy; Gautier Stoll; Pedro T Monteiro; Claudine Chaouiya; Tomáš Helikar; Andrei Zinovyev; Laurence Calzone; Sarah Cohen-Boulakia; Denis Thieffry; Loïc Paulevé Journal: Front Physiol Date: 2018-06-19 Impact factor: 4.566