Learning a model of T cell self/nonself discrimination using high-dimensional cytokine dynamics”
Monday, March 28, 2022 - 4:15pm
Icahn 101
Quantitative & Computational Biology
LSI - Genomics

Available via Livestream at:


Be sure to log in with your Princeton credentials to access!


Immunology lacks a framework to derive theoretical understanding from high-dimensional datasets. We combined a robotic platform with machine learning to experimentally measure and theoretically model CD8+ T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called “antigen encoding”). We used antigen encoding to model and reconstruct patterns of T cell immune activation and study its dynamic characteristics from information-theoretic and biological standpoints. Our model delineated 6 classes of antigens eliciting distinct T cell responses. We generalized antigen encoding to multiple immune settings, including drug perturbations and activation of chimeric antigen receptor T cells. In addition, applying our model to genetically-altered T cells with limited signaling capabilities allowed us to better understand the theoretical underpinnings of antigen discrimination in immune cells. Such universal antigen encoding for T cell activation may enable further modeling of immune responses and their rational manipulation to optimize immunotherapies.

Gregoire Altan-Bonnet

About Dr. Altan-Bonnet:

Grégoire Altan-Bonnet was trained in Statistical Physics and nonlinear dynamics (PhD) and in Immunology (post-doctoral studies). His field of expertise is Systems Immunology: the ImmunoDynamics group he heads has been developing experimentally validated quantitative models of different aspects of the immune system. In particular, they have addressed the interplay between the robustness and variability of self/non-self discrimination in the immune system. They are also focused on developing quantitative models of lymphocyte-lymphocyte communications via cytokine. Current projects focus on the multicellular coordination of immune responses against tumors and pathogenic infections. Altan-Bonnet and his lab are particularly interested in developing quantitative models of the integration of signal transduction, gene regulation, cytokine communications, cell differentiation, and proliferation/death across multiple spatio-temporal scales with the long-term goal to help in the development of tailored immunotherapies (e.g. against tumors).