Computational Verification of Large Logical Models—Application to the Prediction of T Cell Response to Checkpoint Inhibitors - Hernandez, Céline; Thomas-Chollier, Morgane; Naldi, Aurélien; Thieffry, Denis (Frontiers in Physiology, 2020)
At the crossroad between biology and mathematical modelling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as our knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we take advantage of generic computational techniques to enable the dynamical study of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalisation and automated verification of validation criteria for whole models or selected sub-parts, thereby greatly facilitating model development. A second approach, called "value propagation", enables efficient analytical computation of the impact of specific environmental or genetic conditions on model dynamics. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While the use of model verification greatly eased the delineation of logical rules complying with a set of dynamical specifications, the use of value propagation provided interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.