Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology
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 acti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-02-01
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| Series: | Human Vaccines & Immunotherapeutics |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21645515.2017.1264774 |
| _version_ | 1797674551491952640 |
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| author | Carla Rezende Barbosa Bonin Guilherme Cortes Fernandes Rodrigo Weber dos Santos Marcelo Lobosco |
| author_facet | Carla Rezende Barbosa Bonin Guilherme Cortes Fernandes Rodrigo Weber dos Santos Marcelo Lobosco |
| author_sort | Carla Rezende Barbosa Bonin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T22:00:40Z |
| format | Article |
| id | doaj.art-702bcab45f434e47aebfbc625e79414a |
| institution | Directory Open Access Journal |
| issn | 2164-5515 2164-554X |
| language | English |
| last_indexed | 2024-03-11T22:00:40Z |
| publishDate | 2017-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Human Vaccines & Immunotherapeutics |
| spelling | doaj.art-702bcab45f434e47aebfbc625e79414a2023-09-25T11:02:54ZengTaylor & Francis GroupHuman Vaccines & Immunotherapeutics2164-55152164-554X2017-02-0113248448910.1080/21645515.2017.12647741264774Mathematical modeling based on ordinary differential equations: A promising approach to vaccinologyCarla Rezende Barbosa Bonin0Guilherme Cortes Fernandes1Rodrigo Weber dos Santos2Marcelo Lobosco3Postgraduate Program in Computational Modeling, Federal University of Juiz de ForaMedical School, Presidente Antônio Carlos UniversityPostgraduate Program in Computational Modeling, Federal University of Juiz de ForaPostgraduate Program in Computational Modeling, Federal University of Juiz de ForaNew 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.http://dx.doi.org/10.1080/21645515.2017.1264774computational sciencecomputational modelingcomputational immunologycomputational vaccinologyimmune systemordinary differential equationsyellow fever |
| spellingShingle | Carla Rezende Barbosa Bonin Guilherme Cortes Fernandes Rodrigo Weber dos Santos Marcelo Lobosco Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology Human Vaccines & Immunotherapeutics computational science computational modeling computational immunology computational vaccinology immune system ordinary differential equations yellow fever |
| title | Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology |
| title_full | Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology |
| title_fullStr | Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology |
| title_full_unstemmed | Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology |
| title_short | Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology |
| title_sort | mathematical modeling based on ordinary differential equations a promising approach to vaccinology |
| topic | computational science computational modeling computational immunology computational vaccinology immune system ordinary differential equations yellow fever |
| url | http://dx.doi.org/10.1080/21645515.2017.1264774 |
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