To develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes ( e.g. antigen-processing and presentation) and immunization protocols.To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model and a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). We identify biological processes more likely to be affected by the immunization and those that generate inter-individual variability. The robustness of the model is assessed by confrontation to new experimental data.Our approach allows to investigate immune responses in various immunization contexts, when measurements are scarce or missing, and contributes to a better understanding of inter-individual variability in CD8 T cell immune responses.Author summary Developments of vaccines and therapies based on the immune response require to understand inter-individual variability, that is variations observed in responses of individuals subject to the same immunizations. These variations may originate from genetic backgrounds, intra-specific processes and immunization protocols. We propose a mathematical framework to describe and investigate inter-individual variability in CD8 T cell responses in mice. It consists in coupling a dynamical model of CD8 T cell response and an original statistical model of inter-individual variability. We characterize individual mice dynamics in response to vaccinia virus and also tumor cells inoculation. In addition we identify biological processes more likely to be affected by the immunization and those that generate inter-individual variability. Our work provides a framework to investigate immune responses in various immunization contexts, when measurements are scarce or missing as is often the case. It contributes to a better understanding of variability and its biological causes in CD8 T cell immune responses, and can be applied to various immune responses provided that appropriate data are available.

Investigation of Inter-Individual Variability in CD8 T Cell Responses with Nonlinear Mixed Effects Models