Generation of a stochastic, agent-based mathematical model to predict the memory CD8+ T cell response to vaccination 3677

Abstract Description Protective immunity against infection requires both pathogen-specific antibody and T cell responses. Current vaccine strategies rely on the generation of protective antibody responses, but these vaccines fail to yield the memory CD8+ T cell populations required for protection against several pathogens including the intracellular parasites that cause malaria and toxoplasmosis. We show that a single low dose immunization with an attenuated strain of Toxoplasma gondii generates a protective, pathogen-specific CD8+ T cell response. To understand the mechanisms underlying this response, a mathematical, stochastic, agent-based model was developed to track every CD8+ T cell and simulate the events from T cell priming and expansion to T cell differentiation, contraction, and memory formation across secondary lymphoid tissues and the site of immunization. Early studies with the model accurately predicted biological defects in the CD8+ T cell response in cDC1-deficient mice, demonstrating the predictive capacity of the model. Importantly, the stochasticity of the model allows for a more physiologically accurate recapitulation than previous deterministic models of the variation in CD8+ T cell responses observed in mice as well as in human studies following vaccination. Going forward, the model will be applied to mRNA-LNP vaccination to inform their biology and streamline their development toward a strategy that effectively produces a protective CD8+ T cell response. Funding Sources Supported by NIH AI-160664 Topic Categories Computational and Systems Immunology (COMP)