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

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 effecto...

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Detalles Bibliográficos
Autores principales: Baral, Subhasish, Raja, Rubesh, Sen, Pramita, Dixit, Narendra M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614031/
https://www.ncbi.nlm.nih.gov/pubmed/30811096
http://dx.doi.org/10.1002/wsbm.1446
Descripción
Sumario: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. Analytical and Computational Methods > Computational Methods. Biological Mechanisms > Cell Fates. Biological Mechanisms > Cell Signaling. Models of Systems Properties and Processes > Mechanistic Models;