Modeling the Effect of Memory in the Adaptive Immune Response
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Modeling the Effect of Memory in the Adaptive Immune Response Asia Wyatt1 · Doron Levy2 Received: 8 January 2020 / Accepted: 25 August 2020 © Society for Mathematical Biology 2020
Abstract It is well understood that there are key differences between a primary immune response and subsequent responses. Specifically, memory T cells that remain after a primary response drive the clearance of antigen in later encounters. While the existence of memory T cells is widely accepted, the specific mechanisms that govern their function are generally debated. In this paper, we develop a mathematical model of the immune response. This model follows the creation, activation, and regulation of memory T cells, which allows us to explore the differences between the primary and secondary immune responses. Through the incorporation of memory T cells, we demonstrate how the immune system can mount a faster and more effective secondary response. This mathematical model provides a quantitative framework for studying chronic infections and auto-immune diseases. Keywords Memory T cells · Secondary immune response · Immune memory · Chronic infections
1 Introduction When studying T cell-driven adaptive immunity, it is necessary to distinguish between a primary immune response and subsequent responses. Moreover, during a primary immune response, the immune system is primed to accelerate and amplify secondary and subsequent responses in comparison with a primary one. This difference is driven primarily by the presence of antigen-specific memory T cells. These cells, while
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Doron Levy [email protected] Asia Wyatt [email protected]
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Department of Mathematics, University of Maryland, College Park, MD 20742, USA
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Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD 20742, USA 0123456789().: V,-vol
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A. Wyatt, D. Levy
formed from T cells, require a lower amount of stimulation to be activated and perform as effector T cells faster than their non-antigen specific counterparts (Rogers et al. 2000). Given that memory T cells can have a half-life of up to 8–15 years, these cells allow for a prolonged and effective use of the immune system (Akondy et al. 2017; Macallan et al. 2017). The formation and efficacy of these memory T cells is heavily dependent upon the duration of antigen exposure and the strength of inflammatory signaling. Shortening the duration of antigen exposure by introducing therapies, such as antibiotics, can prevent the formation of memory T cells. In contrast, overexposure to antigen, such as in chronic viral infections, can also prevent the formation of memory T cells through clonal exhaustion—continual antigen stimulation, leading to terminally differentiated effector T cells (Kaech and Cui 2012). From molecular to tissue-level interactions, there are numerous mathematical models that have been developed to answer immunological questions (Eftimie et al. 2016). At the cellular level, models addressing T cell
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