2024 Volume 14 Issue 4
Article Contents

Yuhao Shou, Jie Lou. STUDIES ON THE INTERACTION MECHANISM BETWEEN THE MRNA VACCINE AGAINST SARS-COV-2 AND THE IMMUNE SYSTEM[J]. Journal of Applied Analysis & Computation, 2024, 14(4): 2283-2316. doi: 10.11948/20230365
Citation: Yuhao Shou, Jie Lou. STUDIES ON THE INTERACTION MECHANISM BETWEEN THE MRNA VACCINE AGAINST SARS-COV-2 AND THE IMMUNE SYSTEM[J]. Journal of Applied Analysis & Computation, 2024, 14(4): 2283-2316. doi: 10.11948/20230365

STUDIES ON THE INTERACTION MECHANISM BETWEEN THE MRNA VACCINE AGAINST SARS-COV-2 AND THE IMMUNE SYSTEM

  • Vaccines are an effective tool in the fight against infectious diseases. However, mathematical models of SARS-CoV-2 focus on the macroscopic situation, while articles on vaccines focus on effectiveness and safety. We develop four mathematical models to investigate the immune system and the microdynamics of antigens and viruses in individuals injected with mRNA vaccines. We first theoretically analyze the optimal model, calculate all equilibria, and prove that the disease-free equilibrium is globally asymptotically stable while the others are unstable. This suggests that after a certain period after vaccination, the infected cells and antigens will no longer exist in vivo and will be eliminated by the immune system over time or will die naturally. This theoretically proves the safety of the mRNA vaccines. Then, we use the differential algebra to analyze the structural identifiability of the models. We find that two of them are globally identifiable while the other two are unidentifiable, but once a certain parameter is fixed, then they are identifiable as well. To select the optimal model among four models, we use the Affine Invariant Ensemble Markov Chain Monte Carlo algorithm for data fitting and parameter estimation. We find that the roles of memory cells in killing infected cells and promoting immune cells and neutralizing antibodies in the process of mRNA vaccination are not significant and can be ignored in the modeling. On the other hand, the innate immunity of the human body plays an important role in this process. In addition, we also analyze the practical identifiability of the parameters of the optimal model. The results show that even if the structure of the system is globally identifiable, it does not ensure that all the parameters are practically identifiable. After random sampling and simulating the four unidentifiable parameters, we find that only two variables, infected cells Ⅱ and antibodies, are sensitive to these unidentifiable parameters, but the results are still within acceptable ranges. This suggests that our fitting results are generally reliable. Finally, we simulate multiple booster injections and find that booster injections are indeed effective in maintaining antibody levels in vivo, which could otherwise gradually die off over time. Therefore, booster injections are beneficial to help the human body increase and maintain immunity.

    MSC: 92D30, 92D40
  • 加载中
  • [1] H. Akaike, Statistical predictor identification, Annals of the Institute of Statistical Mathematics, 1970, 22(1), 203–217. doi: 10.1007/BF02506337

    CrossRef Google Scholar

    [2] H. Akaike, Information theory and an extension of the maximum likelihood principle, Proceedings of the Second International Symposium on Information Theory, 1973, 267–281.

    Google Scholar

    [3] H. Akaike, A new look at the statistical model identification, Automatic Control IEEE Transactions on Automatic Control, 1974, 19(6), 716–723. doi: 10.1109/TAC.1974.1100705

    CrossRef Google Scholar

    [4] L. R. Baden, H. M. El Sahly, B. Essink, K. Kotloff, S. Frey, R. Novak, D. Diemert, S. A. Spector, N. Rouphael and C. B. Creech, Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine, New England Journal of Medicine, 2021, 384(5), 403–416. doi: 10.1056/NEJMoa2035389

    CrossRef Google Scholar

    [5] G. Bellu, M. P. Saccomani, S. Audoly and L. D'Angiò, Daisy: A new software tool to test global identifiability of biological and physiological systems, Computer Methods and Programs in Biomedicine, 2007, 88(1), 52–61. doi: 10.1016/j.cmpb.2007.07.002

    CrossRef Google Scholar

    [6] S. P. Brooks and A. Gelman, General methods for monitoring convergence of iterative simulations, Journal of Computational and Graphical Statistics, 1998, 7(4), 434–455.

    Google Scholar

    [7] M. Chen, Q. Shao and J. G. Ibrahim, Monte Carlo Methods in Bayesian Computation, Springer, 2000. DOI: 10.1007/978-1-4612-1276-8.

    Google Scholar

    [8] M. S. Diamond and T. D. Kanneganti, Innate immunity: The first line of defense against SARS-CoV-2, Nature Immunology, 2022, 23(2), 165–176. doi: 10.1038/s41590-021-01091-0

    CrossRef Google Scholar

    [9] N. D. Evans, L. J. White and M. J. Chapman, The structural identifiability of the susceptible infected recovered model with seasonal forcing, Mathematical Biosciences, 2005, 194(2), 175–197. doi: 10.1016/j.mbs.2004.10.011

    CrossRef Google Scholar

    [10] A. Gelman and D. B. Rubin, Inference from iterative simulation using multiple sequences, Statistical Science, 1992, 7(4), 457–472.

    Google Scholar

    [11] M. Gheblawi, K. Wang, A. Viveiros, Q. Nguyen, J. C. Zhong, A. J. Turner, M. K. Raizada, M. B. Grant and G. Y. Oudit, Angiotensin-converting enzyme 2: SARS-CoV-2 receptor and regulator of the renin-angiotensin system: Celebrating the 20th anniversary of the discovery of ACE2, Circulation Research, 2020, 126(10), 1456–1474. doi: 10.1161/CIRCRESAHA.120.317015

    CrossRef Google Scholar

    [12] Ghosh, Introduction to applied Bayesian statistics and estimation for social scientists by Scott M. Lynch, International Statal Review, 2010, 76(2), 311–312.

    Google Scholar

    [13] R. R. Goel, M. M. Painter, S. A. Apostolidis, D. Mathew, W. Meng, A. M. Rosenfeld, K. A. Lundgreen, A. Reynaldi, D. S. Khoury and A. Pattekar, mRNA vaccines induce durable immune memory to SARS-CoV-2 and variants of concern, Science, 2021. DOI: 10.1126/science.abm0829.

    CrossRef Google Scholar

    [14] J. Goodman and J. Weare, Ensemble samplers with affine invariance, Communications in Applied Mathematics and Computational Science, 2010, 5(1), 65–80. doi: 10.2140/camcos.2010.5.65

    CrossRef Google Scholar

    [15] K. Hattaf and N. Yousfi, Dynamics of SARS-CoV-2 infection model with two modes of transmission and immune response, Mathematical Biosciences and Engineering, 2020, 17(5), 5326–5340. doi: 10.3934/mbe.2020288

    CrossRef Google Scholar

    [16] G. He and J. Wang, Threshold dynamics of an epidemic model with latency and vaccination in a heterogeneous habitat, Journal of Nonlinear Modeling and Analysis, 2020, 2(3), 393–410.

    Google Scholar

    [17] D. M. Hinke, T. K. Andersen, R. P. Gopalakrishnan, L. M. Skullerud, I. C. Werninghaus, G. Grodeland, E. Fossum, R. Braathen and B. Bogen, Antigen bivalency of antigen-presenting cell-targeted vaccines increases B cell responses, Cell Reports, 2022, 39(9), 110901. doi: 10.1016/j.celrep.2022.110901

    CrossRef Google Scholar

    [18] L. A. Jackson, E. J. Anderson, N. G. Rouphael, P. C. Roberts, M. Makhene, R. N. Coler, M. P. McCullough, J. D. Chappell, M. R. Denison and L. J. Stevens, An mRNA vaccine against SARS-CoV-2–preliminary report, New England Journal of Medicine, 2020, 383(20), 1920–1931. doi: 10.1056/NEJMoa2022483

    CrossRef Google Scholar

    [19] S. Karlin, A First Course in Stochastic Processes, Academic Press, 2014.

    Google Scholar

    [20] C. Li, J. Xu, J. Liu and Y. Zhou, The within-host viral kinetics of SARS-CoV-2, Mathematical Biosciences and Engineering, 2020, 17(4), 2853–2861. doi: 10.3934/mbe.2020159

    CrossRef Google Scholar

    [21] N. Louati, T. Rekik, H. Menif and J. Gargouri, Blood lymphocyte T subsets reference values in blood donors by flow cytometry, La Tunisie Medicale, 2019, 97(2), 327–334.

    Google Scholar

    [22] C. Lucas, C. B. Vogels, I. Yildirim, J. E. Rothman, P. Lu, V. Monteiro, J. R. Gehlhausen, M. Campbell, J. Silva and A. Tabachnikova, Impact of circulating SARS-CoV-2 variants on mRNA vaccine-induced immunity, Nature, 2021, 600(7889), 523–529. doi: 10.1038/s41586-021-04085-y

    CrossRef Google Scholar

    [23] S. Marino, I. B. Hogue, C. J. Ray and D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, Journal of Theoretical Biology, 2008, 254(1), 178–196. doi: 10.1016/j.jtbi.2008.04.011

    CrossRef Google Scholar

    [24] F. Martinon, S. Krishnan, G. Lenzen, R. Magne, E. Gomard, J. G. Guillet, J. P. Lévy and P. Meulien, Induction of virus-specific cytotoxic T lymphocytes in vivo by liposome-entrapped mRNA, European Journal of Immunology, 1993, 23(7), 1719–1722. doi: 10.1002/eji.1830230749

    CrossRef Google Scholar

    [25] J. Mateus, J. M. Dan, Z. Zhang, C. R. Moderbacher, M. Lammers, B. Goodwin, A. Sette, S. Crotty and D. Weiskopf, Low-Dose mRNA-1273 COVID-19 Vaccine Generates Durable Memory Enhanced by Cross-Reactive T Cells, Science, 2021. DOI: 10.1126/science.abj9853.

    CrossRef Google Scholar

    [26] M. D. McKay, R. J. Beckman and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 2000, 42(1), 55–61. doi: 10.1080/00401706.2000.10485979

    CrossRef Google Scholar

    [27] H. Miao, X. Xia, A. S. Perelson and H. Wu, On identifiability of nonlinear ODE models and applications in viral dynamics, SIAM Review, 2011, 53(1), 3–39. doi: 10.1137/090757009

    CrossRef Google Scholar

    [28] H. Morbach, E. Eichhorn, J. Liese and H. Girschick, Reference values for B cell subpopulations from infancy to adulthood, Clinical and Experimental Immunology, 2010, 162(2), 271–279. doi: 10.1111/j.1365-2249.2010.04206.x

    CrossRef Google Scholar

    [29] M. J. Mulligan, K. E. Lyke, N. Kitchin, J. Absalon, A. Gurtman, S. Lockhart, K. Neuzil, V. Raabe, R. Bailey and K. A. Swanson, Phase I/II study of COVID-19 RNA vaccine BNT162b1 in adults, Nature, 2020, 586(7830), 589–593. doi: 10.1038/s41586-020-2639-4

    CrossRef Google Scholar

    [30] S. L. Nutt, P. D. Hodgkin, D. M. Tarlinton and L. M. Corcoran, The generation of antibody-secreting plasma cells, Nature Reviews Immunology, 2015, 15(3), 160–171. doi: 10.1038/nri3795

    CrossRef Google Scholar

    [31] F. P. Polack, S. J. Thomas, N. Kitchin, J. Absalon, A. Gurtman, S. Lockhart, J. L. Perez, G. PérezMarc, E. D. Moreira and C. Zerbini, Safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine, New England Journal of Medicine, 2020, 383(27), 2603–2615. doi: 10.1056/NEJMoa2034577

    CrossRef Google Scholar

    [32] G. Regev-Yochay, T. Gonen, M. Gilboa, M. Mandelboim, V. Indenbaum, S. Amit, L. Meltzer, K. Asraf, C. Cohen and R. Fluss, Efficacy of a fourth dose of COVID-19 mRNA vaccine against Omicron, New England Journal of Medicine, 2022, 386(14), 1377–1380. doi: 10.1056/NEJMc2202542

    CrossRef Google Scholar

    [33] R. F. Reis, A. B. Pigozzo, C. R. B. Bonin, B. D. M. Quintela, L. T. Pompei, A. C. Vieira, M. P. Xavier, R. W. Santos and M. Lobosco, A validated mathematical model of the cytokine release syndrome in severe COVID-19, Frontiers in Molecular Biosciences, 2021. DOI: 10.3389/fmolb.2021.639423.

    CrossRef Google Scholar

    [34] W. C. Roda, Bayesian inference for dynamical systems, Infectious Disease Modelling, 2020, 5(1), 221–232.

    Google Scholar

    [35] N. Sugiura, Further analysis of the data by Akaike's information criterion and the finite corrections: Further analysis of the data by Akaike's, Communications in Statistics-theory and Methods, 1978, 7(1), 13–26. doi: 10.1080/03610927808827599

    CrossRef Google Scholar

    [36] N. Tuncer, H. Gulbudak, V. L. Cannataro and M. Martcheva, Structural and practical identifiability issues of immuno-epidemiological vector–host models with application to rift valley fever, Bulletin of Mathematical Biology, 2016, 78(9), 1796–1827. doi: 10.1007/s11538-016-0200-2

    CrossRef Google Scholar

    [37] Z. Yu, R. Ellahi, A. Nutini, A. Sohail and S. M. Sait, Modeling and simulations of COVID-19 molecular mechanism induced by cytokines storm during SARS-CoV-2 infection, Journal of Molecular Liquids, 2021. DOI: 10.1016/j.molliq.2020.114863.

    CrossRef Google Scholar

    [38] R. Yuan and Z. Wang, A HIV infection model with periodic multidrug therapy, Journal of Nonlinear Modeling and Analysis, 2019, 1(4), 573–593.

    Google Scholar

    [39] Z. Zhang, J. Mateus, C. H. Coelho, J. M. Dan, C. R. Moderbacher, R. I. Gálvez, F. H. Cortes, A. Grifoni, A. Tarke and J. Chang, Humoral and cellular immune memory to four COVID-19 vaccines, Cell, 2022, 185(14), 2434–2451. doi: 10.1016/j.cell.2022.05.022

    CrossRef Google Scholar

Figures(7)  /  Tables(5)

Article Metrics

Article views(981) PDF downloads(289) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint