MaBoSS 2.0: an environment for stochastic Boolean modeling.

Abstract:

Motivation: Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model. Results: We present a new version of MaBoSS (2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions. Availability and Implementation: MaBoSS software can be found at https://maboss.curie.fr , including tutorials on existing models and examples of models. Contact: gautier.stoll@upmc.fr or laurence.calzone@curie.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

SEEK ID: https://fairdomhub.org/publications/618

PubMed ID: 28881959

Projects: COVID-19 Disease Map

Publication type: Journal

Journal: Bioinformatics

Citation: Bioinformatics. 2017 Jul 15;33(14):2226-2228. doi: 10.1093/bioinformatics/btx123.

Date Published: 15th Jul 2017

Registered Mode: by PubMed ID

Authors: G. Stoll, B. Caron, E. Viara, A. Dugourd, A. Zinovyev, A. Naldi, G. Kroemer, E. Barillot, L. Calzone

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Created: 13th Aug 2021 at 10:51

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