Distribution-modeling quantifies collective Th cell decision circuits in chronic inflammation

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. Further, we derived a specific, data-driven mathematical model for Th1 vs. Tfh cell fate-decision dynamics in acute and chronic LCMV infections in mice. The model recapitulates important dynamical properties without model fitting, and solely by employing 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.