Literature DB >> 1943135

Mathematical model of antiviral immune response. I. Data analysis, generalized picture construction and parameters evaluation for hepatitis B.

G I Marchuk1, R V Petrov, A A Romanyukha, G A Bocharov.   

Abstract

The present approach to the mathematical modelling of infectious diseases is based upon the idea that specific immune mechanisms play a leading role in development, course, and outcome of infectious disease. The model describing the reaction of the immune system to infectious agent invasion is constructed on the bases of Burnet's clonal selection theory and the co-recognition principle. The mathematical model of antiviral immune response is formulated by a system of ten non-linear delay-differential equations. The delayed argument terms in the right-hand part are used for the description of lymphocyte division, multiplication and differentiation processes into effector cells. The analysis of clinical and experimental data allows one to construct the generalized picture of the acute form of viral hepatitis B. The concept of the generalized picture includes a quantitative description of dynamics of the principal immunological, virological and clinical characteristics of the disease. Data of immunological experiments in vitro and experiments on animals are used to obtain estimates of permissible values of model parameters. This analysis forms the bases for the solution of the parameter identification problem for the mathematical model of antiviral immune response which will be the topic of the following paper (Marchuk et al., 1991, J. theor. Biol. 15).

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Year:  1991        PMID: 1943135     DOI: 10.1016/s0022-5193(05)80142-0

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  6 in total

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5.  Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection.

Authors:  Ada W C Yan; Sophie G Zaloumis; Julie A Simpson; James M McCaw
Journal:  PLoS Comput Biol       Date:  2019-01-17       Impact factor: 4.475

6.  Modeling within-Host SARS-CoV-2 Infection Dynamics and Potential Treatments.

Authors:  Mehrshad Sadria; Anita T Layton
Journal:  Viruses       Date:  2021-06-14       Impact factor: 5.048

  6 in total

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