Speaker
Description
In order to address the pervasive reproducibility crisis, we combine Docker containers (or Conda packages) with Jupyter electronic notebooks, in order to develop and document dynamical analyses of logical models of complex biological molecular networks. The resulting Common Logical Modelling Tools (CoLoMoTo) environment currently encompasses over 20 tools, written in multiple languages, making them accessible with a single popular language, python.
To illustrate how complementary tools can generate novel insights, we have revisited the analysis of a model of the regulatory network controlling mammalian cell proliferation, published by Sizek et al in PLoS Computational Biology in 2019. Using the CoLoMoTo environment, we could reproduce the main 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.
More precisely, we developped a notebook encompassing the visualisation of the network with the tool GINsim, an attractor analysis with bioLQM, the identification of synchronous attractors with BNS, the extraction of modules from the full model, stochastic simulations for the wild-type and selected mutant background with MaBoSS, and finally the computation of compressed probabilistic state transition graphs.
The integration of all these analyses in an executable Jupyter notebook greatly eases their reproducibility, as well as novel 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.
