Immunogenic Cell Death

Taxon: Mammal  Process: Cancer
Title: Dynamical Boolean Modeling of Immunogenic Cell Death
Author(s): Checcoli, Andrea and Pol, Jonathan G. and Naldi, Aurelien and Noel, Vincent and Barillot, Emmanuel and Kroemer, Guido and Thieffry, Denis and Calzone, Laurence and Stoll, Gautier
Journal: Frontiers in Physiology
Year: 2020
DOI: 10.3389/fphys.2020.590479

Model file(s)	Description(s)
ICD_phenomenological.bnd	Phenomenological model MaBoSS network description
ICD_phenomenological.cfg	Phenomenological model MaBoSS configuration
ICD_phenomenological.upp	Phenomenological model UPMaBoSS population definition
ICD_phenomenological.ipynb	Phenomenological model Analysis notebook
ICD_extend.bnd	Extended model MaBoSS network description
ICD_extend.cfg	Extended model MaBoSS configuration
ICD_extend.upp	Extended model UPMaBoSS population definition
ICD_extend.ipynb	Extended model Analysis notebook

Summary:
This Boolean model covers the major cell types that intervene in immunogenic cell death (ICD), namely cancer cells, DCs, CD8+ and CD4+ T cells. This model integrates intracellular mechanisms within each individual cell entity, and further incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell populations.

Model dynamics has been simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type.

With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response.

All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and calculations of parameter sensitivity analyses.