MaBoSS 2.0: an environment for stochastic Boolean modeling | Zendy

Gautier Stoll | Zendy; Barthélémy Caron | Zendy; Eric Viara | Zendy; Aurélien Dugourd | Zendy; Andreï Zinovyev | Zendy; Aurélien Naldi | Zendy; Guido Kroemer | Zendy; Emmanuel Barillot | Zendy; Laurence Calzone | Zendy

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MaBoSS 2.0: an environment for stochastic Boolean modeling

Author(s) -

Gautier Stoll,

Barthélémy Caron,

Eric Viara,

Aurélien Dugourd,

Andreï Zinovyev,

Aurélien Naldi,

Guido Kroemer,

Emmanuel Barillot,

Laurence Calzone

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx123

Subject(s) - computer science , software , visualization , sensitivity (control systems) , computational biology , theoretical computer science , software engineering , data mining , biology , programming language , electronic engineering , engineering

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.

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