A Mathematical Model Linking CD8 T Cell Population and Transcriptional Dynamics Following Acute Viral Infection

CD8 T cells are a critical component of the adaptive immune response. During acute viral infection, these cells progress through distinct phases of expansion, activation, and contraction. Undifferentiated naive T cells transition to heterogeneous populations of effector and memory cells, which function to clear the virus and provide long-term immunity, respectively. It remains difficult to connect gene-level regulatory mechanisms to population-level immune dynamics. Here, we propose an ordinary differential equation-based mathematical modeling framework that integrates CD8 T cell population dynamics with key underlying gene regulatory network dynamics. The model is constructed to preserve the major phases of the acute viral response while linking transcriptional regulation to changes in T cell abundance and state over time. Longitudinal single-cell RNA sequencing data provide snapshot measurements of gene expression at the level of individual cells, offering transcriptional and cell dynamics at discrete time points, which guides the gene regulatory and cell population dynamics in this model. This framework aims to provide a mechanistic foundation for studying how gene regulation influences CD8 T cell differentiation and the overall immune response to acute viral infection.