Speaker
Description
A fast and pathogen-specific supply of effector T cells is essential in the mammalian immune response to acute and chronic infections, as well as cancer. While the basic principles of T cell activation and differentiation are well established, the mechanisms that control the balance between proliferation and differentiation remain incompletely understood.
Motivated by the observation that simple T cell differentiation motifs where proliferation competes against differentiation fail to explain clonal expansion, we developed a mathematical framework to quantify desirable expansion-related properties. Specifically, we defined measures for the potential of controlled expansion, the efficiency of the expansion, and the chronicity under persistent antigen exposure. Within this framework, we systematically analyzed different regulation mechanisms of the proliferation, involving cytokine- and antigen-mediated feedback, under acute and chronic conditions.
Subsequently, we compared the best-performing motifs to a published T helper cell differentiation motif in the context of LCMV infection in mice \cite{burt_2023} and to a tumor-immune interaction model \cite{rob_2012}. In both cases, we fitted the dynamics of the derived motifs to the dynamics in the published models and then compared proliferative potential and chronicity.
Our framework provides a foundation for exploring how circuit-level mechanisms influence T cell population dynamics in acute and chronic scenarios.
Bibliography
@article{burt_2023,
title = {Distribution modeling quantifies collective {T}$_{\textrm{{H}}}$ cell decision circuits in chronic inflammation},
volume = {9},
issn = {2375-2548},
url = {https://www.science.org/doi/10.1126/sciadv.adg7668},
doi = {10.1126/sciadv.adg7668},
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.
,
Model-based integration of kinetic in vitro and ex vivo data predicts windows of opportunity for targeted perturbation.},
language = {en},
number = {37},
urldate = {2026-03-31},
journal = {Science Advances},
author = {Burt, Philipp and Thurley, Kevin},
month = sep,
year = {2023},
pages = {eadg7668},
}
@article{rob_2012,
title = {A mathematical model of tumor–immune interactions},
volume = {294},
issn = {00225193},
url = {https://linkinghub.elsevier.com/retrieve/pii/S002251931100542X},
doi = {10.1016/j.jtbi.2011.10.027},
language = {en},
urldate = {2026-03-31},
journal = {Journal of Theoretical Biology},
author = {Robertson-Tessi, Mark and El-Kareh, Ardith and Goriely, Alain},
month = feb,
year = {2012},
pages = {56--73},
}
