Description
Bonin, Carla Rezende Barbosa; Guilherme Cortes Fernandes; Rodrigo Weber Dos Santos; and Marcelo Lobosco. 2017. Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology. Human Vaccines and Immunotherapy. 13(2): 484–489.
See https://pubmed.ncbi.nlm.nih.gov/28027002/ . Accessed 28 March 2023.
Abtract: New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model.
As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus.
Key words: computational science, computational modeling, computational immunology, computational vaccinology, immune system, system, ordinary differential equations, yellow fever
2017-Bonin-EtAl.pdf
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