MaBoSS: a tool for modelling biological systems

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:

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.

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.

You can find all the videos in this MaBoSS training playlist.

Material developed by:

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