A Differential Equation Based Model of Cell and Cytokine Activation Influenced by Glucose Dynamics and Elevated Cortisol Levels Due to Aging
Resumo
Immunosenescence refers to the alterations in the immune system that occur due to the aging process, which increases susceptibility to diseases and reduces vaccine efficacy. Consequently, understanding the impact of aging on the immune system is crucial for simulating the different ways in which it can be challenged, thereby increasing life expectancy and quality of life. This study combines two mathematical models to understand how cortisol affects and is affected by the glucose uptake and the proand anti-inflammatory cytokines under infection. Cortisol concentration follows a diurnal rhythm and increases with glucose intake. The model simulates the influence of cortisol on the immune response, specifically through cytokine regulation.Referências
Agondi, R. C., Rizzo, L. V., Kalil, J., and Barros, M. (2012). Imunossenescência / immunosenescence.
Bosch, J. A., Phillips, A. C., and Lord, J. M., editors (2013). Immunosenescence. Springer New York, New York, NY.
Marchuk, G. I. and Romanyukha, A. A. (2010). Mathematical modelling and the homeostatic function of the immune system. Russian Journal of Numerical Analysis and Mathematical Modelling, 25(6).
Miller, R., Stalder, T., Jarczok, M., Almeida, D. M., Badrick, E., Bartels, M., Boomsma, D. I., Coe, C. L., Dekker, M. C., Donzella, B., Fischer, J. E., Gunnar, M. R., Kumari, M., Lederbogen, F., Power, C., Ryff, C. D., Subramanian, S., Tiemeier, H., Watamura, S. E., and Kirschbaum, C. (2016). The CIRCORT database: Reference ranges and seasonal changes in diurnal salivary cortisol derived from a meta-dataset comprised of 15 field studies. Psychoneuroendocrinology, 73:16–23.
Perelson, A. (2002). Modelling viral and immune system dynamics. Nat Rev Immunol, 2:28–36.
Pritchard-Bell, A. (2016). Mathematical modeling in systems medicine: New paradigms for glucose control in critical care. page 142.
Quintela, B. d. M., dos Santos, R. W., and Lobosco, M. (2014). On the Coupling of Two Models of the Human Immune Response to an Antigen. BioMed Research International, 2014:1–19.
Romanyukha, A. A., Rudnev, S. G., Sannikova, T. E., and Yashin, A. I. (2018). Mathematical Modeling of Immunosenescence: Scenarios, Processes, and Limitations. In Fulop, T., Franceschi, C., Hirokawa, K., and Pawelec, G., editors, Handbook of Immunosenescence, pages 1–21. Springer International Publishing, Cham.
Talaei, K., Garan, S. A., Quintela, B. d. M., Olufsen, M. S., Cho, J., Jahansooz, J. R., Bhullar, P. K., Suen, E. K., Piszker, W. J., Martins, N. R. B., Moreira de Paula, M. A., dos Santos, R. W., and Lobosco, M. (2021). A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus. Frontiers in Cellular and Infection Microbiology, 11:711153.
Uluseker, C., Simoni, G., Marchetti, L., Dauriz, M., Matone, A., and Priami, C. (2018). A closed-loop multi-level model of glucose homeostasis. PLOS ONE, 13(2):e0190627.
WHO (2020). Decade of Healthy Ageing (2021-2030) — who.int. [link]. [Accessed 12-Dec-2022].
Zavala, E et. al. (2019). Mathematical modelling of endocrine systems. trends in endocrinology and metabolism: TEM, 30:244–257.
Bosch, J. A., Phillips, A. C., and Lord, J. M., editors (2013). Immunosenescence. Springer New York, New York, NY.
Marchuk, G. I. and Romanyukha, A. A. (2010). Mathematical modelling and the homeostatic function of the immune system. Russian Journal of Numerical Analysis and Mathematical Modelling, 25(6).
Miller, R., Stalder, T., Jarczok, M., Almeida, D. M., Badrick, E., Bartels, M., Boomsma, D. I., Coe, C. L., Dekker, M. C., Donzella, B., Fischer, J. E., Gunnar, M. R., Kumari, M., Lederbogen, F., Power, C., Ryff, C. D., Subramanian, S., Tiemeier, H., Watamura, S. E., and Kirschbaum, C. (2016). The CIRCORT database: Reference ranges and seasonal changes in diurnal salivary cortisol derived from a meta-dataset comprised of 15 field studies. Psychoneuroendocrinology, 73:16–23.
Perelson, A. (2002). Modelling viral and immune system dynamics. Nat Rev Immunol, 2:28–36.
Pritchard-Bell, A. (2016). Mathematical modeling in systems medicine: New paradigms for glucose control in critical care. page 142.
Quintela, B. d. M., dos Santos, R. W., and Lobosco, M. (2014). On the Coupling of Two Models of the Human Immune Response to an Antigen. BioMed Research International, 2014:1–19.
Romanyukha, A. A., Rudnev, S. G., Sannikova, T. E., and Yashin, A. I. (2018). Mathematical Modeling of Immunosenescence: Scenarios, Processes, and Limitations. In Fulop, T., Franceschi, C., Hirokawa, K., and Pawelec, G., editors, Handbook of Immunosenescence, pages 1–21. Springer International Publishing, Cham.
Talaei, K., Garan, S. A., Quintela, B. d. M., Olufsen, M. S., Cho, J., Jahansooz, J. R., Bhullar, P. K., Suen, E. K., Piszker, W. J., Martins, N. R. B., Moreira de Paula, M. A., dos Santos, R. W., and Lobosco, M. (2021). A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus. Frontiers in Cellular and Infection Microbiology, 11:711153.
Uluseker, C., Simoni, G., Marchetti, L., Dauriz, M., Matone, A., and Priami, C. (2018). A closed-loop multi-level model of glucose homeostasis. PLOS ONE, 13(2):e0190627.
WHO (2020). Decade of Healthy Ageing (2021-2030) — who.int. [link]. [Accessed 12-Dec-2022].
Zavala, E et. al. (2019). Mathematical modelling of endocrine systems. trends in endocrinology and metabolism: TEM, 30:244–257.
Publicado
27/06/2023
Como Citar
QUINTELA, Bárbara M.; MARINS, Thaís S.; GARAN, Steven A.; SUEN, Elliott K.; TALAEI, Kian; MARTINS, Nuno R. B.; JAHANSOOZ, Julia R.; PISZKER, Walter.
A Differential Equation Based Model of Cell and Cytokine Activation Influenced by Glucose Dynamics and Elevated Cortisol Levels Due to Aging. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 95-103.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.229512.