MaBoSS

Section outline

• Select section MaBoSS

  MaBoSS

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  • Select activity Welcome to MaBoSS course! Here you can find many u...

    

    Welcome to MaBoSS course! Here you can find many useful links that will help you master the use of MaBoSS.

    
    
  • Select activity MaBoSS is a software for simulating signaling and ...

    MaBoSS is a software for simulating signaling and regulatory networks, which produces trajectories describing the evolution of the states' probabilities of a Boolean model.
    

    
    

    More technically, it is a software for simulating continuous/discrete time Markov processes, applied on a Boolean network. It is implemented in C++. MaBoSS uses a specific language for associating transition rates to each node. Given some initial conditions, MaBoSS applies Monte-Carlo kinetic algorithm (or Gillespie algorithm) to the network to produce time trajectories. Time evolution of probabilities are estimated. In addition, global and semi-global characterizations of the whole system are computed."
    
• Select section Tool repositories

  Tool repositories

  A link to tool repository / repositories

  • Select activity MaBoSS repositories

    

    MaBoSS repositories URL
  • Select activity MaBoSS on bio.tools

    

    MaBoSS on bio.tools URL
• Select section Tools platforms/specifics

  Tools platforms/specifics

  • Select activity MaBoSS (command-line tool): UNIX/MacOSpyMaBoSS (py...

    • MaBoSS (command-line tool): UNIX/MacOS
    • pyMaBoSS (python bindings): UNIX/MacOS
    • WebMaBoSS (web tool): Any modern browser
      
• Select section Prior Knowledge needed for this tool:

  Prior Knowledge needed for this tool:

  Dear participant!

  If this is your firs time working with MaBoSS we suggest you start here. We have collected some links to tutorials that will help you gain basic knowledge needed for this tool. You will also need some basic knowledge of Python.

  • Select activity Command line (mac, linux, windows) 1

    

    Command line (mac, linux, windows) 1 URL
  • Select activity Command line (mac, linux, windows) 2

    

    Command line (mac, linux, windows) 2 URL
  • Select activity Basic knowledge of Python

    

    Basic knowledge of Python URL
• Select section Tutorials & resources:

  Tutorials & resources:

  Here you can find our recommended tutorials and resources for MaBoSS.

  • Select activity MaBoSS documentation and tutorials

    

    MaBoSS documentation and tutorials URL
  • Select activity MaBoSS tutorials

    

    MaBoSS tutorials URL
  • Select activity pyMaBoSS documentation

    

    pyMaBoSS documentation URL
  • Select activity WebMaBoSS tutorials

    

    WebMaBoSS tutorials URL
  • Select activity Introduction to qualitative modelling with MaBoSS ...

    Introduction to qualitative modelling with MaBoSS
    
    

  • Select activity Modelling biological systems can be very useful ...

    Modelling biological systems can be very useful to identify which cellular mechanisms are deregulated in diseases and why, to anticipate the effect of drugs, and to optimise the efficacy of treatments, among other purposes. This section includes training materials and resources to use MaBoSS (Markovian Boolean Stochastic Simulator), a tool for continuous time boolean modelling. You will find tutorials and demonstrations on how to use the MaBoSS extensions webMaBoSS and pyMaBoSS in different biomedical use cases.

    Introduction to modelling of biological systems and to MaBoSS

    If you are new to the topic, take a look at this Introduction video:

    
    
  • Select activity Tutorials to start using WebMaBoSS for studying ...

    Tutorials to start using WebMaBoSS for studying tumor cell invasion

    WebMaBoSS is the web tool version of MaBoSS for simulating Boolean models.

    • WebMaBoSS allows simulations, and multiple outputs for results. It also allows sensitivity analysis by performing single and double mutations.
    • WebMaBoSS is able to import models in MaBoSS format (bnd, cfg files), BoolNet format, SBML-qual format, or in GINsim format. It also allows to export models in any of these three formats.
    • WebMaBoSS allows to browse models from CellCollective and BioModels, and import them.

    WebMaBoSS website includes a self-learning tutorial to start using the platform and to use the Cohen’s model of tumor invasion. In combination with this material, you can watch the demonstration below that takes you through the tutorial.

    
    
  • Select activity Tutorials to start using pyMaBoSS pyMaboSS&nbsp...

    Tutorials to start using pyMaBoSS

    pyMaboSS is the Python interface for the MaBoSS software. The pyMaBoSS website includes a tutorial and an example workflow.

    Additionally, you can carry out the following pyMaBoSS tutorial using Montagud’s model of prostate. Start familiarising yourself with the topic by watching this demonstration below.

    • To carry out the tutorial you will need to run a dedicated Jupyter notebook. Access the tutorial github repository and take a look at the Readme file.
    • Then, access the tutorial notebook to carry out the exercise
    • Finally, you also have access to the solved tutorial

    
    

    You can find all the videos in this 

    .

    Material developed by:

    • Vincent Noël (Institut Curie)
    • Marco Ruscone (Institut Curie)
    • Daniel Thomas Lopez (EMBL-EBI)
    • Marta Lloret Llinares (EMBL-EBI)

    
    
• Select section Publications

  Publications

  • Select activity Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm

    

    Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm URL
  • Select activity MaBoSS 2.0: an environment for stochastic Boolean modeling.

    

    MaBoSS 2.0: an environment for stochastic Boolean modeling. URL
  • Select activity Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells

    

    Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells URL
• Select section Related publications

  Related publications

  • Select activity Logic Modeling in Quantitative Systems Pharmacology

    

    Logic Modeling in Quantitative Systems Pharmacology URL
  • Select activity Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

    

    Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients URL
  • Select activity Patient-specific Boolean models of signalling networks guide personalised treatments

    

    Patient-specific Boolean models of signalling networks guide personalised treatments URL
  • Select activity Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

    

    Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers URL
  • Select activity Conceptual and computational framework for logical modelling of biological networks deregulated in diseases

    

    Conceptual and computational framework for logical modelling of biological networks deregulated in diseases URL
  • Select activity Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration

    

    Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration URL
  • Select activity Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors

    

    Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors URL
  • Select activity A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle

    

    A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle URL
  • Select activity A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle

    

    A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle URL
  • Select activity Oznaka