CD8+ T cells are critical to the adaptive immune response and are a target for vaccine development. However, the complex dynamics of cell proliferation can vary response success, providing uncertainty when designing vaccines. Computer models can provide clarity by simulating these dynamics, tracking millions of cell-cell interactions, a feat that is impractical experimentally. Our group created the STORE.1 model, a probabilistic simulation of the CD8+ T cell response to vaccination. While able to accurately simulate in vivo mouse T cell clonal expansion, intracellular dynamics are absent. Furthermore, there is no mechanism by which cell division ceases. This work builds upon the STORE.1 model by systematically explaining the division dynamics of CD8+ T cells and providing measures of the extracellular environment. The new STORE.2 model has demonstrated an ability to accurately simulate differences in CD8+ T cell expansion in WT mice and mice lacking type I conventional dendritic cells up to 170 hours after vaccination. It is the first model to simulate individual cell cycle regulator protein counts for millions of cells, and the resulting impact on pH for the extracellular microenvironment. Finally, it provides a partial mechanism behind division cessation, an important element for future models seeking to further simulate the end of the T-cell response. / Thesis / Master of Applied Science (MASc) / T-cells are an important component of the human immune system, but currently, there are no vaccines in clinical use that are designed to target them. This is because there are many different dynamics that underpin how T-cells activate, and to what degree they can replicate into a substantial pool of pathogen-clearing cells. Learning which candidate vaccines can properly elicit a strong T-cell response is time and resource consuming. Mathematical models can therefore speed development of candidate vaccines by virtually testing their T-cell responsiveness. This thesis works to improve on an existing mathematical model by introducing immunological mechanisms that determine how T-cells undergo cell division, change the acidity of their immediate surroundings, and respond to their own growing population. By doing so, this new model can be more representative of the immunological reality and begin to probe new dynamics of the T-cell response.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29469 |
| Date | January 2024 |
| Creators | Bhartt, Taran |
| Contributors | Adams, Thomas, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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