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Dynamical Boolean Modeling of Immunogenic Cell Death.

Andrea Checcoli¹, Jonathan G Pol^2,3, Aurelien Naldi¹

¹Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France.

Frontiers in Physiology

|December 7, 2020

Summary

This summary is machine-generated.

Immunogenic cell death (ICD) triggers adaptive immunity against cancer. This study models ICD dynamics to identify targets for enhancing anti-tumor responses, improving cancer therapies.

Keywords:

antitumor immune response cytotoxic CD8+ T lymphocytes dendritic cells immunogenic cell death logical modeling

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Area of Science:

Immunology
Computational Biology
Cancer Research

Background:

Immunogenic cell death (ICD) is a form of cell death that elicits an adaptive immune response, distinct from standard apoptosis.
ICD in cancer cells, induced by chemotherapy or radiotherapy, releases signals that activate dendritic cells (DCs) to prime anti-tumor immunity.
Not all current chemotherapeutic drugs effectively induce all features of ICD, necessitating further characterization.

Purpose of the Study:

To develop a mathematical model of the cell populations involved in ICD to understand its dynamics.
To identify potential pharmacological targets and drug combinations that can enhance ICD-induced anti-tumor immunity.
To predict challenging-to-measure time scales within the ICD process.

Main Methods:

A discrete Boolean mathematical model was constructed, integrating intracellular mechanisms and intercellular communications among cancer cells, DCs, CD8+, and CD4+ T cells.
The model was simulated using UPMaBoSS software, performing stochastic simulations with continuous time to analyze cell population dynamics.
Model analysis included network structure visualization, simulations, and parameter sensitivity analyses, with results compiled in interactive notebooks.

Main Results:

The cell population model successfully recapitulated key features of ICD dynamics, including time-dependent cell type sizes.
The model predicted the time scales of several processes involved in ICD, which are difficult to measure experimentally.
Actionable targets for boosting ICD-induced anti-tumor responses were identified through model analysis.

Conclusions:

Mathematical modeling provides a powerful tool for dissecting the complex dynamics of immunogenic cell death.
The developed model offers insights into optimizing cancer therapies by enhancing ICD and leveraging the immune system.
Identification of actionable targets holds promise for developing novel strategies to improve therapeutic efficacy against cancer.