Modelling asthma mechanisms
Mast cell in asthma: a large-scale mechanistic model
The project employs the AsthmaMap Process Description layer to build a mechanistic model of mast cell dynamics aiming at improving our understanding of the disease and offering a tool for predicting the efficacy of medications. The objective is to develop a predictive model which integrates qualitative knowledge about the pathway topology and quantitative experimental data available in the literature.
The SBML model is derived from the Mast Cell Module 0.0.40 of the AsthmaMap Process Description and additionally refined using information from the literature. For the simulation and parameterizations of the SBML model, we use the MATLAB toolboxes AMICI (Fröhlich et al., 2017, PMID 28114351) and PESTO (Stapor et al., 2018, PMID 29069312). Quantitative experimental data are extracted from several published studies (e.g., Parravicini et al., 2002, PMID 12089510). The model refinements derived from the quantitative analysis are fed back to the Mast Cell Module development.
The model has more than 300 species and 400 parameters and includes kinetic laws for all reactions. We established a pipeline for building a data repository which is used for the model parameterization. The current model already captures several aspects of the mast cell response to allergens.
The mast cell model was produced in CellDesigner and is available in the default CellDesigner’s format, in pure SBML format and also is provided for browsing via the MINERVA platform.
Thomas Ligon, Irina Balaur, Alexander Mazein, Charles Auffray, Jan Hasenauer