Skip to main content
Top
Published in: BMC Immunology 1/2018

Open Access 01-12-2018 | Research article

A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine

Authors: Carla R. B. Bonin, Guilherme C. Fernandes, Rodrigo W. dos Santos, Marcelo Lobosco

Published in: BMC Immunology | Issue 1/2018

Login to get access

Abstract

Background

Although a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever.

Methods

This work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies.

Results

In order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine.

Conclusions

This work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.
Literature
1.
go back to reference Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G. Computational modeling of the dynamics of the map kinase cascade activated by surface and internalized egf receptors. Nat Biotechnol. 2002; 20(4):370–5.CrossRefPubMed Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G. Computational modeling of the dynamics of the map kinase cascade activated by surface and internalized egf receptors. Nat Biotechnol. 2002; 20(4):370–5.CrossRefPubMed
2.
go back to reference Wiley HS, Shvartsman SY, Lauffenburger DA. Computational modeling of the egf-receptor system: a paradigm for systems biology. Trends Cell Biol. 2003; 13(1):43–50.CrossRefPubMed Wiley HS, Shvartsman SY, Lauffenburger DA. Computational modeling of the egf-receptor system: a paradigm for systems biology. Trends Cell Biol. 2003; 13(1):43–50.CrossRefPubMed
3.
go back to reference Doddi SK, Bagchi P. Three-dimensional computational modeling of multiple deformable cells flowing in microvessels. Phys Rev E. 2009; 79(4):046318.CrossRef Doddi SK, Bagchi P. Three-dimensional computational modeling of multiple deformable cells flowing in microvessels. Phys Rev E. 2009; 79(4):046318.CrossRef
4.
go back to reference Beard DA, Schlick T. Computational modeling predicts the structure and dynamics of chromatin fiber. Structure. 2001; 9(2):105–14.CrossRefPubMed Beard DA, Schlick T. Computational modeling predicts the structure and dynamics of chromatin fiber. Structure. 2001; 9(2):105–14.CrossRefPubMed
5.
go back to reference Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, Pirani A, Gernert K, Deng J, Marzolf B, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009; 10(1):116–25.CrossRefPubMed Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, Pirani A, Gernert K, Deng J, Marzolf B, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009; 10(1):116–25.CrossRefPubMed
6.
go back to reference Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW. A whole-cell computational model predicts phenotype from genotype. Cell. 2012; 150(2):389–401.CrossRefPubMedPubMedCentral Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW. A whole-cell computational model predicts phenotype from genotype. Cell. 2012; 150(2):389–401.CrossRefPubMedPubMedCentral
7.
go back to reference Clarke S, Vvedensky DD. Origin of reflection high-energy electron-diffraction intensity oscillations during molecular-beam epitaxy: A computational modeling approach. Phys Rev Lett. 1987; 58(21):2235.CrossRefPubMed Clarke S, Vvedensky DD. Origin of reflection high-energy electron-diffraction intensity oscillations during molecular-beam epitaxy: A computational modeling approach. Phys Rev Lett. 1987; 58(21):2235.CrossRefPubMed
8.
go back to reference Sakurai T. Computational modeling of magnetic fields in solar active regions. Space Sci Rev. 1989; 51(1-2):11–48. Sakurai T. Computational modeling of magnetic fields in solar active regions. Space Sci Rev. 1989; 51(1-2):11–48.
9.
go back to reference Cuitino AM, Ortiz M. Computational modelling of single crystals. Model Simul Mater Sci Eng. 1993; 1(3):225.CrossRef Cuitino AM, Ortiz M. Computational modelling of single crystals. Model Simul Mater Sci Eng. 1993; 1(3):225.CrossRef
10.
go back to reference Yanez J, Kuznetsov M. An analysis of flame instabilities for hydrogen–air mixtures based on sivashinsky equation. Phys Lett A. 2016; 380(33):2549–2560.CrossRef Yanez J, Kuznetsov M. An analysis of flame instabilities for hydrogen–air mixtures based on sivashinsky equation. Phys Lett A. 2016; 380(33):2549–2560.CrossRef
11.
go back to reference Feldgus S, Landis CR. Large-scale computational modeling of [rh (duphos)]+-catalyzed hydrogenation of prochiral enamides: reaction pathways and the origin of enantioselection. J Am Chem Soc. 2000; 122(51):12714–27.CrossRef Feldgus S, Landis CR. Large-scale computational modeling of [rh (duphos)]+-catalyzed hydrogenation of prochiral enamides: reaction pathways and the origin of enantioselection. J Am Chem Soc. 2000; 122(51):12714–27.CrossRef
12.
go back to reference Bicerano J. Computational Modeling of Polymers. New York: CRC press; 1992. Bicerano J. Computational Modeling of Polymers. New York: CRC press; 1992.
13.
go back to reference Rots JG. Computational modeling of concrete fracture. PhD thesis, Technische Hogeschool Delft. 1988. Rots JG. Computational modeling of concrete fracture. PhD thesis, Technische Hogeschool Delft. 1988.
14.
go back to reference Schafer B, Peköz T. Computational modeling of cold-formed steel: characterizing geometric imperfections and residual stresses. J Constr Steel Res. 1998; 47(3):193–210.CrossRef Schafer B, Peköz T. Computational modeling of cold-formed steel: characterizing geometric imperfections and residual stresses. J Constr Steel Res. 1998; 47(3):193–210.CrossRef
15.
go back to reference Roussel N, Geiker MR, Dufour F, Thrane LN, Szabo P. Computational modeling of concrete flow: general overview. Cem Concr Res. 2007; 37(9):1298–307.CrossRef Roussel N, Geiker MR, Dufour F, Thrane LN, Szabo P. Computational modeling of concrete flow: general overview. Cem Concr Res. 2007; 37(9):1298–307.CrossRef
16.
go back to reference McHugh P, Asaro R, Shih C. Computational modeling of metal matrix composite materials. i. isothermal deformation patterns in ideal microstructures. Acta Metallurgica et Materialia. 1993; 41(5):1461–76.CrossRef McHugh P, Asaro R, Shih C. Computational modeling of metal matrix composite materials. i. isothermal deformation patterns in ideal microstructures. Acta Metallurgica et Materialia. 1993; 41(5):1461–76.CrossRef
17.
go back to reference Porter B, Zauel R, Stockman H, Guldberg R, Fyhrie D. 3-d computational modeling of media flow through scaffolds in a perfusion bioreactor. J Biomech. 2005; 38(3):543–9.CrossRefPubMed Porter B, Zauel R, Stockman H, Guldberg R, Fyhrie D. 3-d computational modeling of media flow through scaffolds in a perfusion bioreactor. J Biomech. 2005; 38(3):543–9.CrossRefPubMed
18.
go back to reference Kuhl E, Maas R, Himpel G, Menzel A. Computational modeling of arterial wall growth. Biomech Model Mechanobiol. 2007; 6(5):321–31.CrossRefPubMed Kuhl E, Maas R, Himpel G, Menzel A. Computational modeling of arterial wall growth. Biomech Model Mechanobiol. 2007; 6(5):321–31.CrossRefPubMed
19.
go back to reference Randall DA, Ringler TD, Heikes RP, Jones P, Baumgardner J, et al. Climate modeling with spherical geodesic grids. Comput Sci Eng. 2002; 4(5):32–41.CrossRef Randall DA, Ringler TD, Heikes RP, Jones P, Baumgardner J, et al. Climate modeling with spherical geodesic grids. Comput Sci Eng. 2002; 4(5):32–41.CrossRef
20.
go back to reference Nefedova V, Jacob R, Foster I, Liu Z, Liu Y, Deelman E, Mehta G, Su M-H, Vahi K. Automating climate science: Large ensemble simulations on the teragrid with the griphyn virtual data system. In: 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science’06). Washington, DC: IEEE Computer Society: 2006. p. 32–32. Nefedova V, Jacob R, Foster I, Liu Z, Liu Y, Deelman E, Mehta G, Su M-H, Vahi K. Automating climate science: Large ensemble simulations on the teragrid with the griphyn virtual data system. In: 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science’06). Washington, DC: IEEE Computer Society: 2006. p. 32–32.
21.
go back to reference Bernholdt D, Bharathi S, Brown D, Chanchio K, Chen M, Chervenak A, Cinquini L, Drach B, Foster I, Fox P, et al. The earth system grid: Supporting the next generation of climate modeling research. Proc IEEE. 2005; 93(3):485–95.CrossRef Bernholdt D, Bharathi S, Brown D, Chanchio K, Chen M, Chervenak A, Cinquini L, Drach B, Foster I, Fox P, et al. The earth system grid: Supporting the next generation of climate modeling research. Proc IEEE. 2005; 93(3):485–95.CrossRef
22.
go back to reference Das S, Aki K. Fault plane with barriers: a versatile earthquake model. J Geophys Res. 1977; 82(36):5658–70.CrossRef Das S, Aki K. Fault plane with barriers: a versatile earthquake model. J Geophys Res. 1977; 82(36):5658–70.CrossRef
23.
go back to reference Loomis HG. Tsunami prediction using the reciprocal property of green’s functions. Mar Geodesy. 1979; 2(1):27–39.CrossRef Loomis HG. Tsunami prediction using the reciprocal property of green’s functions. Mar Geodesy. 1979; 2(1):27–39.CrossRef
24.
go back to reference Pappalardo F, Flower D, Russo G, Pennisi M, Motta S. Computational modelling approaches to vaccinology. Pharmacol Res. 2015; 92:40–5.CrossRefPubMed Pappalardo F, Flower D, Russo G, Pennisi M, Motta S. Computational modelling approaches to vaccinology. Pharmacol Res. 2015; 92:40–5.CrossRefPubMed
25.
go back to reference Doytchinova IA, Flower DR. Quantitative approaches to computational vaccinology. Immunol Cell Biol. 2002; 80(3):270.CrossRefPubMed Doytchinova IA, Flower DR. Quantitative approaches to computational vaccinology. Immunol Cell Biol. 2002; 80(3):270.CrossRefPubMed
26.
go back to reference Brusic V, Petrovsky N. Bioinformatics for characterisation of allergens, allergenicity and allergic crossreactivity. Trends Immunol. 2003; 24(5):225–8.CrossRefPubMed Brusic V, Petrovsky N. Bioinformatics for characterisation of allergens, allergenicity and allergic crossreactivity. Trends Immunol. 2003; 24(5):225–8.CrossRefPubMed
27.
go back to reference Taylor PD, Flower DR. In: Flower D, Timmis J, (eds).Immunoinformatics and Computational Vaccinology: A Brief Introduction. Boston: Springer; 2007, pp. 23–46. Taylor PD, Flower DR. In: Flower D, Timmis J, (eds).Immunoinformatics and Computational Vaccinology: A Brief Introduction. Boston: Springer; 2007, pp. 23–46.
28.
go back to reference Flower DR. Bioinformatics for Vaccinology. United Kingdom: John Wiley & Sons; 2008.CrossRef Flower DR. Bioinformatics for Vaccinology. United Kingdom: John Wiley & Sons; 2008.CrossRef
29.
go back to reference Paul WE. Fundamental Immunology, 5th edn. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins; 2008. Paul WE. Fundamental Immunology, 5th edn. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins; 2008.
30.
go back to reference Bonin CRB, Fernandes GC, dos Santos RW, Lobosco M. Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology. Hum Vaccines Immunotherapeutics. 2017; 13(2):484–9.CrossRef Bonin CRB, Fernandes GC, dos Santos RW, Lobosco M. Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology. Hum Vaccines Immunotherapeutics. 2017; 13(2):484–9.CrossRef
31.
32.
go back to reference Martins RM, Maia MdLS, Farias RHG, Camacho LAB, Freire MS, Galler R, Yamamura AMY, Almeida LFC, Lima SMB, Nogueira RMR, et al. 17dd yellow fever vaccine: a double blind, randomized clinical trial of immunogenicity and safety on a dose-response study. Hum Vaccines Immunotherapeutics. 2013; 9(4):879–88.CrossRef Martins RM, Maia MdLS, Farias RHG, Camacho LAB, Freire MS, Galler R, Yamamura AMY, Almeida LFC, Lima SMB, Nogueira RMR, et al. 17dd yellow fever vaccine: a double blind, randomized clinical trial of immunogenicity and safety on a dose-response study. Hum Vaccines Immunotherapeutics. 2013; 9(4):879–88.CrossRef
33.
36.
go back to reference Kumar N, Hendriks BS, Janes KA, de Graaf D, Lauffenburger DA. Applying computational modeling to drug discovery and development. Drug Discov Today. 2006; 11(17-18):806–11.CrossRefPubMed Kumar N, Hendriks BS, Janes KA, de Graaf D, Lauffenburger DA. Applying computational modeling to drug discovery and development. Drug Discov Today. 2006; 11(17-18):806–11.CrossRefPubMed
37.
go back to reference De Groot AS, Moise L, McMurry JA, Martin W. In: Falus A, (ed).Epitope-Based Immunome-Derived Vaccines: A Strategy for Improved Design and Safety. New York: Springer; 2009, pp. 39–69. De Groot AS, Moise L, McMurry JA, Martin W. In: Falus A, (ed).Epitope-Based Immunome-Derived Vaccines: A Strategy for Improved Design and Safety. New York: Springer; 2009, pp. 39–69.
38.
go back to reference Oliveira FM, Coelho IE, Lopes MD, Taranto AG, Junior MC, Santos LL, Villar JA, Fonseca CT, Lopes DD. The use of reverse vaccinology and molecular modeling associated with cell proliferation stimulation approach to select promiscuous epitopes from schistosoma mansoni. Appl Biochem Biotechnol. 2016; 179(6):1023–40.CrossRefPubMed Oliveira FM, Coelho IE, Lopes MD, Taranto AG, Junior MC, Santos LL, Villar JA, Fonseca CT, Lopes DD. The use of reverse vaccinology and molecular modeling associated with cell proliferation stimulation approach to select promiscuous epitopes from schistosoma mansoni. Appl Biochem Biotechnol. 2016; 179(6):1023–40.CrossRefPubMed
39.
go back to reference Rappuoli R, Bottomley MJ, D’Oro U, Finco O, De Gregorio E. Reverse vaccinology 2.0: Human immunology instructs vaccine antigen design. J Exp Med. 2016; 213(4):469–81.CrossRefPubMedPubMedCentral Rappuoli R, Bottomley MJ, D’Oro U, Finco O, De Gregorio E. Reverse vaccinology 2.0: Human immunology instructs vaccine antigen design. J Exp Med. 2016; 213(4):469–81.CrossRefPubMedPubMedCentral
40.
go back to reference Michalik M, Djahanshiri B, Leo JC, Linke D. Reverse Vaccinology: The Pathway from Genomes and Epitope Predictions to Tailored Recombinant Vaccines. Methods Mol Biol. 2016; 1403:87–106.CrossRefPubMed Michalik M, Djahanshiri B, Leo JC, Linke D. Reverse Vaccinology: The Pathway from Genomes and Epitope Predictions to Tailored Recombinant Vaccines. Methods Mol Biol. 2016; 1403:87–106.CrossRefPubMed
41.
go back to reference Andreoni F, Amagliani G, Magnani M. Selection of vaccine candidates for fish pasteurellosis using reverse vaccinology and an in vitro screening approach. Methods Mol Biol. 2016; 1404:181–92.CrossRefPubMed Andreoni F, Amagliani G, Magnani M. Selection of vaccine candidates for fish pasteurellosis using reverse vaccinology and an in vitro screening approach. Methods Mol Biol. 2016; 1404:181–92.CrossRefPubMed
42.
go back to reference Yang YT, Chow YH, Hsiao KN, Hu KC, Chiang JR, Wu SC, Chong P, Liu CC. Development of a full-length cDNA-derived enterovirus A71 vaccine candidate using reverse genetics technology. Antivir Res. 2016; 132:225–32.CrossRefPubMed Yang YT, Chow YH, Hsiao KN, Hu KC, Chiang JR, Wu SC, Chong P, Liu CC. Development of a full-length cDNA-derived enterovirus A71 vaccine candidate using reverse genetics technology. Antivir Res. 2016; 132:225–32.CrossRefPubMed
43.
go back to reference Meunier M, Guyard-Nicodeme M, Hirchaud E, Parra A, Chemaly M, Dory D. Identification of novel vaccine candidates against campylobacter through reverse vaccinology. J Immunol Res. 2016; 2016:5715790.CrossRefPubMedPubMedCentral Meunier M, Guyard-Nicodeme M, Hirchaud E, Parra A, Chemaly M, Dory D. Identification of novel vaccine candidates against campylobacter through reverse vaccinology. J Immunol Res. 2016; 2016:5715790.CrossRefPubMedPubMedCentral
44.
go back to reference De Groot AS, Bosma A, Chinai N, Frost J, Jesdale BM, Gonzalez MA, Martin W, Saint-Aubin C. From genome to vaccine: in silico predictions, ex vivo verification. Vaccine. 2001; 19(31):4385–95.CrossRefPubMed De Groot AS, Bosma A, Chinai N, Frost J, Jesdale BM, Gonzalez MA, Martin W, Saint-Aubin C. From genome to vaccine: in silico predictions, ex vivo verification. Vaccine. 2001; 19(31):4385–95.CrossRefPubMed
45.
go back to reference Lafuente EM, Reche PA. Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curr Pharm Des. 2009; 15(28):3209–20.CrossRefPubMed Lafuente EM, Reche PA. Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curr Pharm Des. 2009; 15(28):3209–20.CrossRefPubMed
46.
go back to reference Gomez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D, Blood-Forsythe MA, Chae HS, Einzinger M, Ha DG, Wu T, Markopoulos G, Jeon S, Kang H, Miyazaki H, Numata M, Kim S, Huang W, Hong SI, Baldo M, Adams RP, Aspuru-Guzik A. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater. 2016; 15(10):1120–1127.CrossRefPubMed Gomez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D, Blood-Forsythe MA, Chae HS, Einzinger M, Ha DG, Wu T, Markopoulos G, Jeon S, Kang H, Miyazaki H, Numata M, Kim S, Huang W, Hong SI, Baldo M, Adams RP, Aspuru-Guzik A. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater. 2016; 15(10):1120–1127.CrossRefPubMed
47.
go back to reference Geanes AR, Cho HP, Nance KD, McGowan KM, Conn PJ, Jones CK, Meiler J, Lindsley CW. Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes. Bioorg Med Chem Lett. 2016; 26(18):4487–4491.CrossRefPubMedPubMedCentral Geanes AR, Cho HP, Nance KD, McGowan KM, Conn PJ, Jones CK, Meiler J, Lindsley CW. Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes. Bioorg Med Chem Lett. 2016; 26(18):4487–4491.CrossRefPubMedPubMedCentral
48.
go back to reference Xu Y, Yue L, Wang Y, Xing J, Chen Z, Shi Z, Liu R, Liu YC, Luo X, Jiang H, Chen K, Luo C, Zheng M. Discovery of Novel Inhibitors Targeting Menin-Mixed Lineage Leukemia (MLL) Interface Using Pharmacophore- and Docking-Based Virtual Screening. J Chem Inf Model. 2016; 56(9):1847–1855.CrossRefPubMed Xu Y, Yue L, Wang Y, Xing J, Chen Z, Shi Z, Liu R, Liu YC, Luo X, Jiang H, Chen K, Luo C, Zheng M. Discovery of Novel Inhibitors Targeting Menin-Mixed Lineage Leukemia (MLL) Interface Using Pharmacophore- and Docking-Based Virtual Screening. J Chem Inf Model. 2016; 56(9):1847–1855.CrossRefPubMed
49.
go back to reference Khalili S, Mohammadpour H, Shokrollahi Barough M, Kokhaei P. ILP-2 modeling and virtual screening of an FDA-approved library:a possible anticancer therapy. Turk J Med Sci. 2016; 46(4):1135–43.CrossRefPubMed Khalili S, Mohammadpour H, Shokrollahi Barough M, Kokhaei P. ILP-2 modeling and virtual screening of an FDA-approved library:a possible anticancer therapy. Turk J Med Sci. 2016; 46(4):1135–43.CrossRefPubMed
50.
go back to reference Bonin CRB, Fernandes GC, dos Santos RW, Lobosco M. A simplified mathematical-computational model of the immune response to the yellow fever vaccine. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. Kansas City: IEEE: 2017. p. 1–8. Bonin CRB, Fernandes GC, dos Santos RW, Lobosco M. A simplified mathematical-computational model of the immune response to the yellow fever vaccine. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. Kansas City: IEEE: 2017. p. 1–8.
51.
go back to reference Le D, Miller JD, Ganusov VV. Mathematical modeling provides kinetic details of the human immune response to vaccination. Front Cellular Infect Microbiol. 2015; 4:177.CrossRef Le D, Miller JD, Ganusov VV. Mathematical modeling provides kinetic details of the human immune response to vaccination. Front Cellular Infect Microbiol. 2015; 4:177.CrossRef
52.
go back to reference Goutelle S, Maurin M, Rougier F, Barbaut X, Bourguignon L, Ducher M, Maire P. The Hill equation: a review of its capabilities in pharmacological modelling. Fundam Clin Pharmacol. 2008; 22(6):633–48.CrossRefPubMed Goutelle S, Maurin M, Rougier F, Barbaut X, Bourguignon L, Ducher M, Maire P. The Hill equation: a review of its capabilities in pharmacological modelling. Fundam Clin Pharmacol. 2008; 22(6):633–48.CrossRefPubMed
53.
go back to reference Haefner JW. Modeling Biological Systems:Principles and Applications, 1st edn. London: Chapman & Hall, Ltd; 1996.CrossRef Haefner JW. Modeling Biological Systems:Principles and Applications, 1st edn. London: Chapman & Hall, Ltd; 1996.CrossRef
54.
go back to reference Bonin C, dos Santos RW, Fernandes G, Lobosco M. Computational modeling of the immune response to yellow fever. J Comput Appl Math. 2016; 295:127–38.CrossRef Bonin C, dos Santos RW, Fernandes G, Lobosco M. Computational modeling of the immune response to yellow fever. J Comput Appl Math. 2016; 295:127–38.CrossRef
56.
go back to reference LeVeque RJ. Finite Difference Methods for Ordinary and Partial Differential Equations - Steady-state and Time-dependent Problems. USA: SIAM; 2007, p. 1341.CrossRef LeVeque RJ. Finite Difference Methods for Ordinary and Partial Differential Equations - Steady-state and Time-dependent Problems. USA: SIAM; 2007, p. 1341.CrossRef
57.
go back to reference Reinhardt B, Jaspert R, Niedrig M, Kostner C, L’age-Stehr J. Development of viremia and humoral and cellular parameters of immune activation after vaccination with yellow fever virus strain 17d: a model of human flavivirus infection. J Med Virol. 1998; 56(2):159–67.CrossRefPubMed Reinhardt B, Jaspert R, Niedrig M, Kostner C, L’age-Stehr J. Development of viremia and humoral and cellular parameters of immune activation after vaccination with yellow fever virus strain 17d: a model of human flavivirus infection. J Med Virol. 1998; 56(2):159–67.CrossRefPubMed
58.
go back to reference Collaborative Group for Studies on Yellow Fever Vaccines. Duration of post-vaccination immunity against yellow fever in adults. Vaccine. 2014; 32(39):4977–84.CrossRef Collaborative Group for Studies on Yellow Fever Vaccines. Duration of post-vaccination immunity against yellow fever in adults. Vaccine. 2014; 32(39):4977–84.CrossRef
59.
go back to reference Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. Global Sensitivity Analysis: the Primer. New York: Wiley; 2008. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. Global Sensitivity Analysis: the Primer. New York: Wiley; 2008.
61.
go back to reference Akondy RS, Monson ND, Miller JD, Edupuganti S, Teuwen D, Wu H, Quyyumi F, Garg S, Altman JD, Del Rio C, et al. The yellow fever virus vaccine induces a broad and polyfunctional human memory cd8+ t cell response. J Immunol. 2009; 183(12):7919–30.CrossRefPubMedPubMedCentral Akondy RS, Monson ND, Miller JD, Edupuganti S, Teuwen D, Wu H, Quyyumi F, Garg S, Altman JD, Del Rio C, et al. The yellow fever virus vaccine induces a broad and polyfunctional human memory cd8+ t cell response. J Immunol. 2009; 183(12):7919–30.CrossRefPubMedPubMedCentral
62.
go back to reference Monath TP, Gershman M, Staples JE, Barrett ADT. Vaccines (Sixth Edition), 6th. edition In: Plotkin SA, Orenstein WA, Offit PA, editors. London: W.B. Saunders: 2013. p. 870–968. Monath TP, Gershman M, Staples JE, Barrett ADT. Vaccines (Sixth Edition), 6th. edition In: Plotkin SA, Orenstein WA, Offit PA, editors. London: W.B. Saunders: 2013. p. 870–968.
64.
go back to reference OMS. Vaccines and vaccination against yellow fever: Who position paper–june 2013. Wkly Epidemiol Rec. 2013; 88:269–84. OMS. Vaccines and vaccination against yellow fever: Who position paper–june 2013. Wkly Epidemiol Rec. 2013; 88:269–84.
65.
go back to reference de Souza Lopes O, de Almeida Guimarães SSD, de Carvalho R. Studies on yellow fever vaccine iii—dose response in volunteers. J Biol Stand. 1988; 16(2):77–82.CrossRef de Souza Lopes O, de Almeida Guimarães SSD, de Carvalho R. Studies on yellow fever vaccine iii—dose response in volunteers. J Biol Stand. 1988; 16(2):77–82.CrossRef
66.
go back to reference Vieira P, Rajewsky K. The bulk of endogenously produced igg2a is eliminated from the serum of adult c57bl/6 mice with a half-life of 6–8 days. Eur J Immunol. 1986; 16(7):871–4.CrossRefPubMed Vieira P, Rajewsky K. The bulk of endogenously produced igg2a is eliminated from the serum of adult c57bl/6 mice with a half-life of 6–8 days. Eur J Immunol. 1986; 16(7):871–4.CrossRefPubMed
67.
go back to reference Vieira P, Rajewsky K. The half-lives of serum immunoglobulins in adult mice. Eur J Immunol. 1988; 18(2):313–6.CrossRefPubMed Vieira P, Rajewsky K. The half-lives of serum immunoglobulins in adult mice. Eur J Immunol. 1988; 18(2):313–6.CrossRefPubMed
68.
go back to reference Lee HY, Topham DJ, Park SY, Hollenbaugh J, Treanor J, Mosmann TR, Jin X, Ward BM, Miao H, Holden-Wiltse J, Perelson AS, Zand M, Wu H. Simulation and prediction of the adaptive immune response to influenza A virus infection. J Virol. 2009; 83(14):7151–65.CrossRefPubMedPubMedCentral Lee HY, Topham DJ, Park SY, Hollenbaugh J, Treanor J, Mosmann TR, Jin X, Ward BM, Miao H, Holden-Wiltse J, Perelson AS, Zand M, Wu H. Simulation and prediction of the adaptive immune response to influenza A virus infection. J Virol. 2009; 83(14):7151–65.CrossRefPubMedPubMedCentral
Metadata
Title
A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine
Authors
Carla R. B. Bonin
Guilherme C. Fernandes
Rodrigo W. dos Santos
Marcelo Lobosco
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Immunology / Issue 1/2018
Electronic ISSN: 1471-2172
DOI
https://doi.org/10.1186/s12865-018-0252-1

Other articles of this Issue 1/2018

BMC Immunology 1/2018 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.