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Modeling signaling pathways in biology with MaBoSS...
Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells
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Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells
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MaBoSS 2.0: an environment for stochastic Boolean modeling.
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Logic Modeling in Quantitative Systems Pharmacology
Table of contents
Tool repositories
MaBoSS repositories
MaBoSS on bio.tools
Prior Knowledge needed for this tool:
Command line (mac, linux, windows) 1
Command line (mac, linux, windows) 2
Basic knowledge of Python
Tutorials & resources:
MaBoSS documentation and tutorials
MaBoSS tutorials
pyMaBoSS documentation
WebMaBoSS tutorials
Publications
Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm
MaBoSS 2.0: an environment for stochastic Boolean modeling.
Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells
Related publications
Logic Modeling in Quantitative Systems Pharmacology
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Patient-specific Boolean models of signalling networks guide personalised treatments
Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
Conceptual and computational framework for logical modelling of biological networks deregulated in diseases
Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration
Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors
A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle
A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle
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