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doi: 10.3934/dcdss.2020141

Nonlinear effects in the dynamics of HIV-1 infection predicted by mathematical model with multiple delays

1. 

Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Omsk Branch, Omsk, 644043, Russian Federation

2. 

Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, 119333, Russian Federation

* Corresponding author: Nikolay Pertsev

Received  January 2019 Revised  April 2019 Published  November 2019

Fund Project: The first and the last authors are supported by Russian Science Foundation grant 18-11-00171

We formulate a novel mathematical model to describe the development of acute HIV-1 infection and the antiviral immune response. The model is formulated using a system of delay differential- and integral equations. The model is applied to study the possibility of eradication of HIV infection during the primary acute phase of the disease. To this end a combination of analytical examination and computational treatment is used. The model belongs to the Wazewski differential equation systems with delay. The conditions of asymptotic stability of a trivial steady state solution are expressed in terms of the algebraic Sevastyanov–Kotelyanskii criterion. The results of the computational experiments with the model calibrated according to the available estimates of parameters suggest that a complete elimination of HIV-1 infection after acute phase of infection is feasible.

Citation: Nikolay Pertsev, Konstantin Loginov, Gennady Bocharov. Nonlinear effects in the dynamics of HIV-1 infection predicted by mathematical model with multiple delays. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020141
References:
[1]

H. T. Banks and D. M. Bortz, A parameter sensitivity methodology in the context of HIV delay equation models, J. Math. Biol., 50 (2005), 607-625.  doi: 10.1007/s00285-004-0299-x.  Google Scholar

[2]

G. BocharovV. ChereshnevI. GainovaS. BazhanB. BachmetyevJ. ArgilaguetJ. Martinez and A. Meyerhans, Human Immunodeficiency Virus Infection: From Biological Observations to Mechanistic Mathematical Modelling, Math. Model. Nat. Phenom., 7 (2012), 78-104.  doi: 10.1051/mmnp/20127507.  Google Scholar

[3]

G. A. BocharovYu. M. NechepurenkoM. Yu. Khristichenko and D. S. Grebennikov, Maximum response perturbation-based control of virus infection model with time-delays, Russ. J. Numer. Anal. Math. Modelling, 32 (2017), 275-291.  doi: 10.1515/rnam-2017-0027.  Google Scholar

[4]

V. A. Chereshnev, G. A. Bocharov, A. V. Kim, S. I. Bazhan, I. A. Gainova, A. N. Krasovskii, N. G. Shmagel', A. V. Ivanov, M. A. Safronov and R. M. Tret'yakova, Vvedenie V Zadachi Modelirovaniya I Upravleniya Dinamikoi VICH Infektsii, (Russian) [Introduction to the tasks of modeling and managing the dynamics of HIV infection], Ijevsk: Institut komp'yuternykh issledovanii, Moscow, 2016. Google Scholar

[5]

R. D. Driver, Ordinary and Delay Differential Equations, Applied Mathematical Sciences, Vol. 20, Springer-Verlag, New York-Heidelberg, 1977. doi: 10.1007/978-1-4684-9467-9.  Google Scholar

[6]

R. EftimieJ. J. Gillard and D. A. Cantrell, Mathematical Models for Immunology: Current State of the Art and Future Research Directions, Bull. Math. Biol., 78 (2016), 2091-2134.  doi: 10.1007/s11538-016-0214-9.  Google Scholar

[7]

L. E. El'sgol'ts and S. B. Norkin, Introduction to the Theory and Application of Differential Equations with Deviating Arguments, Translated from the Russian by John L. Casti. Mathematics in Science and Engineering, Vol. 105, Academic Press [A Subsidiary of Harcourt Brace Jovanovich, Publishers], New York-London, 1973.  Google Scholar

[8]

F. R. Gantmacher, The Theory of Matrices, AMS Chelsea Publishing, Providence, RI, 1998.  Google Scholar

[9]

J. K. Hale, Theory of Functional Differential Equations, Applied Mathematical Sciences, Vol. 3, Springer-Verlag, New York-Heidelberg, 1977. doi: 10.1007/978-1-4612-9892-2.  Google Scholar

[10]

S. I. KabanikhinO. I. KrivorotkoD. V. ErmolenkoV. N. Kashtanova and V. A. Latyshenko, Inverse problems of immunology and epidemiology, Eurasian Journal of Mathematical and Computer Applications, 5 (2017), 14-35.   Google Scholar

[11]

S. I. KabanikhinO. I. KrivorotkoD. A. Voronov and D. V. Ermolenko, Optimization approach for solving the inverse problem of the simplest model of an infectious disease, Siberian Electronic Mathematical Reports, 12 (2015), 154-162.   Google Scholar

[12]

V. B. Kolmanovskii and V. R. Nosov, Ustoichivost' i Periodicheskie Rejimy Sistem Upravleniya s Posledeistviem, (Russian) [Stability and periodic regimes of control systems with aftereffect], [Theoretical Foundations of Engineering Cybernetics] Nauka, Moscow, 1981.  Google Scholar

[13]

A. A. Lackner, M. M. Lederman and B. Rodriguez, HIV Pathogenesis: The Host, Cold Spring Harb Perspect Med, 2 (2012), a007005. doi: 10.1101/cshperspect.a007005.  Google Scholar

[14]

G. I. Marchuk, Matematicheskie Modeli v Immunologii, (Russian) [Mathematical models in immunology], 2nd edition, With a preface by R. V. Petrov., Nauka, Moscow, 1985.  Google Scholar

[15]

G. I. Marchuk, Matematicheskie Modeli V Immunologii. Vychislitel'nye Metody I Eksperimenty, (Russian) [Mathematical models in immunology. Computational methods and experiments], 3rd edition, Nauka, Moscow, 1991.  Google Scholar

[16]

S. NakaokaI. Shingo and K. Sato, Dynamics of HIV infection in lymphoid tissue network, J. Math. Biol., 72 (2016), 909-938.  doi: 10.1007/s00285-015-0940-x.  Google Scholar

[17]

P. W. Nelson and A. S. Perelson, Mathematical Analysis of delay differential equation models of HIV-1 infection, Math. Biosci., 179 (2002), 73-94.  doi: 10.1016/S0025-5564(02)00099-8.  Google Scholar

[18]

A. Yu. Obolenskii, Stability of solutions of autonomous Wazewski systems with delayed action, Ukr. Math. J., 35 (1983), 486-492.  doi: 10.1007/BF01061640.  Google Scholar

[19]

S. Pankavich, The effects of latent infection on the dynamics of HIV, Differ. Equ. Dyn. Syst., 24 (2016), 281-303.  doi: 10.1007/s12591-014-0234-6.  Google Scholar

[20]

K. A. PawelekS. LiuF. Pahlevani and L. Rong, A model of HIV-1 infection with two time delays: Mathematical analysis and comparison with patient data, Math. Biosci., 235 (2012), 98-109.  doi: 10.1016/j.mbs.2011.11.002.  Google Scholar

[21]

A. S. Perelson and R. M. Ribeiro, Modeling The Within-Host Dynamics of HIV Infection, BMC Biology, 2013. doi: 10.1186/1741-7007-11-96.  Google Scholar

[22]

N. V. Pertsev, Global Solvability and Estimates of Solutions to the Cauchy Problem for the Retarded Functional Differential Equations That Are Used to Model Living Systems, Sib. Math. J., 59 (2018), 113-125.  doi: 10.1134/S0037446618010135.  Google Scholar

[23]

N. V. PertsevB. Yu. Pichugin and K. K. Loginov, Stochastic Analog of the Dynamic Model of HIV-1 Infection Described by Delay Differential Equations, J. Appl. Ind. Math., 13 (2019), 103-117.  doi: 10.1134/S1990478919010125.  Google Scholar

[24]

N. V. Pertsev, B. Yu. Pichugin and A. N. Pichugina, Application of M-Matrices in studies of mathematical models of living systems, Mathematical Biology and Bioinformatics, 13 (2018), t104–t131. doi: 10.17537/2018.13.t104.  Google Scholar

[25]

M. Pitchaimani and C. Monica, Global stability analysis of HIV-1 infection model with three time delays, J. Appl. Math. Comput., 48 (2015), 293-319.  doi: 10.1007/s12190-014-0803-4.  Google Scholar

[26]

D. Sanchez-TaltavullA. Vieiro and T. Alarcon, Stochastic modelling of the eradication of the HIV-1 infection by stimulation of latently infected cells in patients under highly active anti-retroviral therapy, J. Math. Biol., 73 (2016), 919-946.  doi: 10.1007/s00285-016-0977-5.  Google Scholar

[27]

A. ShetP. Nagaraja and N. M. Dixit, Viral Decay Dynamics and Mathematical Modeling of Treatment Response: Evidence of Lower in vivo Fitness of HIV-1 Subtype C, J. Acquir. Immune Defic. Syndr., 73 (2016), 245-251.  doi: 10.1097/QAI.0000000000001101.  Google Scholar

[28]

R. F. Siliciano and W. C. Greene, HIV Latency, Cold Spring Harb Perspect Med, 1 (2011), a007096. doi: 10.1101/cshperspect.a007096.  Google Scholar

[29]

W. I. Sundquist and H.-G. Kraüsslich, HIV-1 Assembly, Budding, and Maturation, Cold Spring Harb Perspect Med, 2 (2012), a006924. doi: 10.1101/cshperspect.a006924.  Google Scholar

[30]

J. WangJ. Lang and X. Zou, Analysis of an age structured HIV infection model with virus-to-cell infection and cell-to-cell transmission, Nonlinear Anal. Real World Appl., 34 (2017), 75-96.  doi: 10.1016/j.nonrwa.2016.08.001.  Google Scholar

show all references

References:
[1]

H. T. Banks and D. M. Bortz, A parameter sensitivity methodology in the context of HIV delay equation models, J. Math. Biol., 50 (2005), 607-625.  doi: 10.1007/s00285-004-0299-x.  Google Scholar

[2]

G. BocharovV. ChereshnevI. GainovaS. BazhanB. BachmetyevJ. ArgilaguetJ. Martinez and A. Meyerhans, Human Immunodeficiency Virus Infection: From Biological Observations to Mechanistic Mathematical Modelling, Math. Model. Nat. Phenom., 7 (2012), 78-104.  doi: 10.1051/mmnp/20127507.  Google Scholar

[3]

G. A. BocharovYu. M. NechepurenkoM. Yu. Khristichenko and D. S. Grebennikov, Maximum response perturbation-based control of virus infection model with time-delays, Russ. J. Numer. Anal. Math. Modelling, 32 (2017), 275-291.  doi: 10.1515/rnam-2017-0027.  Google Scholar

[4]

V. A. Chereshnev, G. A. Bocharov, A. V. Kim, S. I. Bazhan, I. A. Gainova, A. N. Krasovskii, N. G. Shmagel', A. V. Ivanov, M. A. Safronov and R. M. Tret'yakova, Vvedenie V Zadachi Modelirovaniya I Upravleniya Dinamikoi VICH Infektsii, (Russian) [Introduction to the tasks of modeling and managing the dynamics of HIV infection], Ijevsk: Institut komp'yuternykh issledovanii, Moscow, 2016. Google Scholar

[5]

R. D. Driver, Ordinary and Delay Differential Equations, Applied Mathematical Sciences, Vol. 20, Springer-Verlag, New York-Heidelberg, 1977. doi: 10.1007/978-1-4684-9467-9.  Google Scholar

[6]

R. EftimieJ. J. Gillard and D. A. Cantrell, Mathematical Models for Immunology: Current State of the Art and Future Research Directions, Bull. Math. Biol., 78 (2016), 2091-2134.  doi: 10.1007/s11538-016-0214-9.  Google Scholar

[7]

L. E. El'sgol'ts and S. B. Norkin, Introduction to the Theory and Application of Differential Equations with Deviating Arguments, Translated from the Russian by John L. Casti. Mathematics in Science and Engineering, Vol. 105, Academic Press [A Subsidiary of Harcourt Brace Jovanovich, Publishers], New York-London, 1973.  Google Scholar

[8]

F. R. Gantmacher, The Theory of Matrices, AMS Chelsea Publishing, Providence, RI, 1998.  Google Scholar

[9]

J. K. Hale, Theory of Functional Differential Equations, Applied Mathematical Sciences, Vol. 3, Springer-Verlag, New York-Heidelberg, 1977. doi: 10.1007/978-1-4612-9892-2.  Google Scholar

[10]

S. I. KabanikhinO. I. KrivorotkoD. V. ErmolenkoV. N. Kashtanova and V. A. Latyshenko, Inverse problems of immunology and epidemiology, Eurasian Journal of Mathematical and Computer Applications, 5 (2017), 14-35.   Google Scholar

[11]

S. I. KabanikhinO. I. KrivorotkoD. A. Voronov and D. V. Ermolenko, Optimization approach for solving the inverse problem of the simplest model of an infectious disease, Siberian Electronic Mathematical Reports, 12 (2015), 154-162.   Google Scholar

[12]

V. B. Kolmanovskii and V. R. Nosov, Ustoichivost' i Periodicheskie Rejimy Sistem Upravleniya s Posledeistviem, (Russian) [Stability and periodic regimes of control systems with aftereffect], [Theoretical Foundations of Engineering Cybernetics] Nauka, Moscow, 1981.  Google Scholar

[13]

A. A. Lackner, M. M. Lederman and B. Rodriguez, HIV Pathogenesis: The Host, Cold Spring Harb Perspect Med, 2 (2012), a007005. doi: 10.1101/cshperspect.a007005.  Google Scholar

[14]

G. I. Marchuk, Matematicheskie Modeli v Immunologii, (Russian) [Mathematical models in immunology], 2nd edition, With a preface by R. V. Petrov., Nauka, Moscow, 1985.  Google Scholar

[15]

G. I. Marchuk, Matematicheskie Modeli V Immunologii. Vychislitel'nye Metody I Eksperimenty, (Russian) [Mathematical models in immunology. Computational methods and experiments], 3rd edition, Nauka, Moscow, 1991.  Google Scholar

[16]

S. NakaokaI. Shingo and K. Sato, Dynamics of HIV infection in lymphoid tissue network, J. Math. Biol., 72 (2016), 909-938.  doi: 10.1007/s00285-015-0940-x.  Google Scholar

[17]

P. W. Nelson and A. S. Perelson, Mathematical Analysis of delay differential equation models of HIV-1 infection, Math. Biosci., 179 (2002), 73-94.  doi: 10.1016/S0025-5564(02)00099-8.  Google Scholar

[18]

A. Yu. Obolenskii, Stability of solutions of autonomous Wazewski systems with delayed action, Ukr. Math. J., 35 (1983), 486-492.  doi: 10.1007/BF01061640.  Google Scholar

[19]

S. Pankavich, The effects of latent infection on the dynamics of HIV, Differ. Equ. Dyn. Syst., 24 (2016), 281-303.  doi: 10.1007/s12591-014-0234-6.  Google Scholar

[20]

K. A. PawelekS. LiuF. Pahlevani and L. Rong, A model of HIV-1 infection with two time delays: Mathematical analysis and comparison with patient data, Math. Biosci., 235 (2012), 98-109.  doi: 10.1016/j.mbs.2011.11.002.  Google Scholar

[21]

A. S. Perelson and R. M. Ribeiro, Modeling The Within-Host Dynamics of HIV Infection, BMC Biology, 2013. doi: 10.1186/1741-7007-11-96.  Google Scholar

[22]

N. V. Pertsev, Global Solvability and Estimates of Solutions to the Cauchy Problem for the Retarded Functional Differential Equations That Are Used to Model Living Systems, Sib. Math. J., 59 (2018), 113-125.  doi: 10.1134/S0037446618010135.  Google Scholar

[23]

N. V. PertsevB. Yu. Pichugin and K. K. Loginov, Stochastic Analog of the Dynamic Model of HIV-1 Infection Described by Delay Differential Equations, J. Appl. Ind. Math., 13 (2019), 103-117.  doi: 10.1134/S1990478919010125.  Google Scholar

[24]

N. V. Pertsev, B. Yu. Pichugin and A. N. Pichugina, Application of M-Matrices in studies of mathematical models of living systems, Mathematical Biology and Bioinformatics, 13 (2018), t104–t131. doi: 10.17537/2018.13.t104.  Google Scholar

[25]

M. Pitchaimani and C. Monica, Global stability analysis of HIV-1 infection model with three time delays, J. Appl. Math. Comput., 48 (2015), 293-319.  doi: 10.1007/s12190-014-0803-4.  Google Scholar

[26]

D. Sanchez-TaltavullA. Vieiro and T. Alarcon, Stochastic modelling of the eradication of the HIV-1 infection by stimulation of latently infected cells in patients under highly active anti-retroviral therapy, J. Math. Biol., 73 (2016), 919-946.  doi: 10.1007/s00285-016-0977-5.  Google Scholar

[27]

A. ShetP. Nagaraja and N. M. Dixit, Viral Decay Dynamics and Mathematical Modeling of Treatment Response: Evidence of Lower in vivo Fitness of HIV-1 Subtype C, J. Acquir. Immune Defic. Syndr., 73 (2016), 245-251.  doi: 10.1097/QAI.0000000000001101.  Google Scholar

[28]

R. F. Siliciano and W. C. Greene, HIV Latency, Cold Spring Harb Perspect Med, 1 (2011), a007096. doi: 10.1101/cshperspect.a007096.  Google Scholar

[29]

W. I. Sundquist and H.-G. Kraüsslich, HIV-1 Assembly, Budding, and Maturation, Cold Spring Harb Perspect Med, 2 (2012), a006924. doi: 10.1101/cshperspect.a006924.  Google Scholar

[30]

J. WangJ. Lang and X. Zou, Analysis of an age structured HIV infection model with virus-to-cell infection and cell-to-cell transmission, Nonlinear Anal. Real World Appl., 34 (2017), 75-96.  doi: 10.1016/j.nonrwa.2016.08.001.  Google Scholar

Figure 1.  Schematic representation of the model of acute HIV-1 infection
Figure 2.  Computational Experiment 1. Dynamics of $ y_1(t) $, $ y_2(t) $, $ y_3(t) $ for $ R_0 = 0.9224 $, $ V^0 = 10^4 $
Figure 3.  Computational Experiment 2. Dynamics of $ y_1(t) $, $ y_2(t) $, $ y_3(t) $ for $ R_0 = 0.9073 $, $ V^0 = 10^4 $
Figure 4.  Computational Experiment 3. Dynamics of $ y_1(t) $, $ y_2(t) $, $ y_3(t) $ for $ R_0 = 2.2875 $. (a) $ V^0 = 10^4 $, (b) $ V^0 = 10^6 $
Figure 5.  Computational Experiment 4. Dynamics of $ y_1(t) $, $ y_2(t) $, $ y_3(t) $ for $ R_0 = 2.2875 $. (a) $ V^0 = 10^4 $, (b) $ V^0 = 10^6 $
Figure 6.  Computational Experiment 5. Dynamics of $ y_1(t) $, $ y_2(t) $, $ y_3(t) $ for $ R_0 = 13.2135 $. (a) $ V^0 = 10^2 $, (b) $ V^0 = 10^6 $
Figure 7.  Computational Experiment 6. Dynamics of $ y_1(t) $, $ y_2(t) $, $ y_3(t) $ for $ R_0 = 3.3789 $. (a) $ V^0 = 10^4 $, (b) $ V^0 = 10^6 $
Table 1.  Time post infection needed for a complete elimination of HIV-1 infection
$ V_0 $ Computational experiment number, the value $ \tau_0 $ (day)
1 2 3 4 5 6
$ 10 $ 1 1 $ - $ $ - $ $ - $ 46
$ 10^2 $ 51 46 $ - $ 51 $ - $ 44
$ 10^4 $ 60 115 $ - $ 37 $ - $ 42
$ 10^6 $ 67 132 76 78 102 53
$ 10^7 $ 72 140 96 94 $ - $ 57
$ V_0 $ Computational experiment number, the value $ \tau_0 $ (day)
1 2 3 4 5 6
$ 10 $ 1 1 $ - $ $ - $ $ - $ 46
$ 10^2 $ 51 46 $ - $ 51 $ - $ 44
$ 10^4 $ 60 115 $ - $ 37 $ - $ 42
$ 10^6 $ 67 132 76 78 102 53
$ 10^7 $ 72 140 96 94 $ - $ 57
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