Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation
Philipp Burt1,2, Kevin Thurley1,3
1Systems Biology of Inflammation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany.
Insights
Scientists developed a mathematical framework to analyze immune cell dynamics, revealing insights into cell differentiation and proliferation during inflammation. This model aids in understanding infection responses and optimizing immunotherapy.
Area of Science:
- Immunology
- Systems Biology
- Computational Biology
Background:
- Immune responses involve complex interactions between diverse cell populations, including differentiation and proliferation.
- Understanding these dynamics is crucial for deciphering immune regulation during inflammation.
Purpose of the Study:
- To develop a general mathematical framework for data-driven analysis of collective immune cell dynamics.
- To model T helper 1 versus T follicular helper cell-fate decisions in viral infections.
Main Methods:
- Analysis of kinetic transcriptome data to specify differentiation dynamics.
- Development of a data-driven mathematical model using response-time distributions.
- Simulations of immune cell fate decisions in acute and chronic infections.
Main Results:
- Identified qualitative and quantitative properties of immune cell network motifs.
- Successfully modeled T helper 1 vs. T follicular helper cell-fate dynamics without model fitting.
- Model recapitulated key dynamical properties using only measured response-time distributions.
Conclusions:
- The mathematical framework provides a novel approach to analyzing immune cell dynamics.
- Model simulations predict distinct therapeutic intervention windows for acute and chronic infections.
- Findings have potential implications for optimizing targeted immunotherapy strategies.
Abstract:
Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.
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