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Towards multiscale modeling of the CD8+ T cell response to viral infections

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The CD8+ T cell response is critical to the control of viral infections. Yet, defining the CD8+ T cell response to viral infections quantitatively has been a challenge. Following antigen recognition, which triggers an intracellular signaling cascade, CD8+ T cells can differentiate into effector cells, which proliferate rapidly and destroy infected cells. When the infection is cleared, they leave behind memory cells for quick recall following a second challenge. If the infection persists, the cells may become exhausted, retaining minimal control of the infection while preventing severe immunopathology. These activation, proliferation and differentiation processes as well as the mounting of the effector response are intrinsically multiscale and collective phenomena. Remarkable experimental advances in the recent years, especially at the single cell level, have enabled a quantitative characterization of several underlying processes. Simultaneously, sophisticated mathematical models have begun to be constructed that describe these multiscale phenomena, bringing us closer to a comprehensive description of the CD8+ T cell response to viral infections. Here, we review the advances made and summarize the challenges and opportunities ahead. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Cell Fates Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models
Schematics of proposed differentiation pathways. (a) Naïve cells (N) differentiate into memory cells (M) and then to terminally differentiated effectors (E). Memory cells proliferate. (b) Naïve cells differentiate into effectors, which can proliferate and differentiate into memory cells. (c) The pathway in (a) is expanded by separating memory cells into central (CM) and effector memory (EM) classes. Memory cells and effectors can proliferate. (d) A branched pathway with naïve cells differentiating into precursors of short‐lived effectors (pSLE) and memory (pM) cells. The former differentiate into effectors (SLE) and the latter branch into central memory and effector memory cells. (e) The pathway in (b) is expanded by separating effectors into early (EE) and late (LE) effector stages. The early effectors proliferate
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Multiscale model of collective decision making. TCR‐pMHC stimulation can either be strong (left) or weak (right). Strong signaling leads to proliferation, which triggers IL‐2 secretion and IL‐2R expression, amplifying the proliferation signal. The enhanced extracellular IL‐2 can trigger signaling via IL‐2R in the weakly stimulated cell and drive it into proliferation
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Phenotypic model of proximal TCR signaling. Schematic of the kinetic proofreading model where TCR‐pMHC contact triggers the sequence of events from C0 to CN. Unbinding of the TCR‐pMHC complex resets the system. Following C1, a negative feedback via SHP‐1 suppresses the cascade. Following CN, a positive feedback via ERK‐1 amplifies the cascade
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Schematic of the model of post‐treatment control. Target cells (T) are infected by free virions (V) yielding productively infected cells (I) or latently infected cells (L). The former stimulate effectors (E), activating them at low antigen loads and triggering exhaustion at high antigen loads. Productively infected cells produce free virions. Effectors kill productively infected cells. Latently infected cells can proliferate and get reactivated into productive infected cells. The rates of the various processes are described in the text
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The proliferation program. (a) Strong stimulation leads to high Myc levels and more proliferation events than weak stimulation. (b) the Myc level in cells rises following stimulation and declines at a constant rate. Proliferation stops when the Myc level drops below a threshold or if the time‐to‐die is reached. τd is the time for one proliferation cycle
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Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Cell Fates
Biological Mechanisms > Cell Signaling
Analytical and Computational Methods > Computational Methods

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