Integrating T-Cell Contraction Phase Into a Mathematical Model for Vaccine Immune Response & Immune Memory

To prevent complex infections like HIV-AIDs, malaria, and COVID-19, the next generation of vaccines will need to stimulate all branches of the immune system, not just antibody-based ;humoral immunity. In contrast ;Cellular immunity comprises various T-cell subtypes that when activated by pathogen molecules can seek and destroy individual infected cells, refocus the immune response towards addressing the unique threats posed by the pathogen, and remain in circulation decades after infection to provide a rapid response to variants of the original invader. However, the interaction of multiple factors surrounding T-cell activation can lead to profound differences in response success and cell fate, providing significant uncertainty when researchers try to design vaccine formulas to elicit specific results. Computer models can provide clarity by simulating these factors, tracking millions of cell-cell interactions in a way that is difficult or even impossible in experiments. Our research group has created the first probabilistic simulation of the immune response to vaccine injection. My project aims to expand the model to predict the end of the immune response, where T-cell numbers drop to leave a reserve of memory cells. I will use data from literature and our collaborators to codify the genetic and metabolic timing that governs T-cell replication and lifespan, as well as relating these population-level changes to factors that surround the early activation of a handful of cells. Thus, the model will be able to predict complex T-cell dynamics from a few early interactions, providing necessary information that can determine the best dosage and scheduling of vaccines to ensure an appropriate immune response to infection with a capable reserve of memory cells for the future.