This tutorial provides stepwise instructions to install over 20 tools, written in multiple languages. Their integration in the
CoLoMoTo
software suite makes them accessible with a single popular language (
Python
), thereby enabling reproducible and sophisticated dynamical analyses of logical models of complex cellular networks. The tutorial specifically focuses on the analysis of a previously published model of the regulatory network controlling mammalian cell proliferation. It includes chunks of
Python
code to reproduce several of the results and figures published in the original article, and further extends these results with the help of selected tools included in the
CoLoMoTo
suite. The tutorial covers the visualization of the network with the tool
GINsim
, an attractor analysis with
bioLQM
, the computation of synchronous attractors with
BNS
, the extraction of modules from the full model, stochastic simulations of the wild-type model and of selected perturbations with
MaBoSS
and finally the delineation of compressed probabilistic state transition graphs. The integration of all these analyses in an executable
Jupyter Notebook
greatly eases their reproducibility, as well as the inclusion of further extensions. The notebook provided along with this tutorial further constitutes a template, which can be enriched with other
ColoMoTo
tools, to develop comprehensive dynamical analyses of various biological network models.
