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

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This tutorial describes how to install and use 20 tools in the CoLoMoTo software suite to perform reproducible dynamical analyses and simulations of logical (Boolean) models of biological molecular networks. Using a previously published mammalian cell proliferation regulatory network model, the notebook walks through steps such as network visualization in GINsim, attractor analysis in bioLQM, synchronous attractor computation with BNS, module extraction, MaBoSS simulations for the wild-type model and selected mutants, and construction of compressed probabilistic state transition graphs. The paper’s main limitation is that it is an instructional workflow anchored to one example model, rather than presenting new biological findings on a specific disease. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract This tutorial provides stepwise instructions to install the 20 tools integrated in the CoLoMoTo software suite, to develop reproducible dynamical analyses of logical models of complex biological molecular 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 extend these results with the help of a selection of tools included in the CoLoMoTo suite. The notebook covers the visualisation 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, MaBoSS simulations of the wild-type model, as well as of selected mutants, 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. This notebook can further be used as a template and enriched with other ColoMoTo tools to enable comprehensive dynamical analyses of biological network models. Competing Interest Statement The authors have declared no competing interest.

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last seen: 2026-05-20T01:45:00.602351+00:00