Open AccessContinuous and discrete modeling of HIV-1 decline on therapy
Author(s) -
Elvan Akın,
Gülşah Yeni,
Alan S. Perelson
Publication year - 2020
Publication title -
journal of mathematical biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.928
H-Index - 97
eISSN - 1432-1416
pISSN - 0303-6812
DOI - 10.1007/s00285-020-01492-z
Subject(s) - protease inhibitor (pharmacology) , mathematics , reverse transcriptase inhibitor , human immunodeficiency virus (hiv) , stability (learning theory) , discrete time and continuous time , mathematical model , statistical physics , biology , virology , statistics , viral load , antiretroviral therapy , computer science , physics , machine learning
Mathematical models have shed light on the dynamics of HIV- 1 infection in vivo. In this paper, we generalize continuous mathematical models of drug therapy for HIV-1 by Perelson et al. (Science 271:1582-1586, 1996) and Perelson and Nelson (SIAM Rev 41:3-44, 1999) on time scales, i.e., a nonempty closed subset of real numbers in order to derive new discrete models that predict the total concentration of plasma virus as a function of time. One of our main goals is to compare discrete mathematical models with the continuous model in Perelson et al. (1996) where HIV infected patients were given protease inhibitors and sampled frequently thereafter. For the comparison, we use experimental data collected in Perelson et al. (1996) and estimate the parameters such as the virion clearance rate and the rate of loss of infected cells by fitting the total concentration of plasma virus to this data set. Our results show that discrete systems describe the best fit. In the previous models of this study, the efficacy of protease inhibitor is assumed to be perfect. Motivated by Perelson and Nelson (1999), we end the paper with a mathematical model of imperfect protease inhibitor and reverse transcriptase (RT) inhibitor combination therapy of HIV-1 infection on time scales with its stability analysis.