Reproducible Boolean model analyses and simulations with the CoLoMoTo software suite: a tutorial.

Abstract

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.