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2016, 13(1): 135-157. doi: 10.3934/mbe.2016.13.135

A delayed HIV-1 model with virus waning term

Bing Li 1, , Yuming Chen 2, , Xuejuan Lu 1, and  Shengqiang Liu 3,

1. 

Academy of Fundamental and Interdisciplinary Science, Harbin Institute of Technology, 3041#, 2 Yi-Kuang street, Harbin, 150080, China
2. 

Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, N2L 3C5, Canada
3. 

Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, 3041#, 2 Yi-Kuang Street, Harbin, 150080

Received  April 2015 Revised  July 2015 Published  October 2015

In this paper, we propose and analyze a delayed HIV-1 model with CTL immune response and virus waning. The two discrete delays stand for the time for infected cells to produce viruses after viral entry and for the time for CD$8^+$ T cell immune response to emerge to control viral replication. We obtain the positiveness and boundedness of solutions and find the basic reproduction number $R_0$. If $R_0<1$, then the infection-free steady state is globally asymptotically stable and the infection is cleared from the T-cell population; whereas if $R_0>1$, then the system is uniformly persistent and the viral concentration maintains at some constant level. The global dynamics when $R_0>1$ is complicated. We establish the local stability of the infected steady state and show that Hopf bifurcation can occur. Both analytical and numerical results indicate that if, in the initial infection stage, the effect of delays on HIV-1 infection is ignored, then the risk of HIV-1 infection (if persists) will be underestimated. Moreover, the viral load differs from that without virus waning. These results highlight the important role of delays and virus waning on HIV-1 infection.
Keywords: delay, permanence., stability, CTLs, HIV-1 infection, virus waning, immune response.
Mathematics Subject Classification: Primary: 34K20, 92D30; Secondary: 34K1.
Citation: Bing Li, Yuming Chen, Xuejuan Lu, Shengqiang Liu. A delayed HIV-1 model with virus waning term. Mathematical Biosciences & Engineering, 2016, 13 (1) : 135-157. doi: 10.3934/mbe.2016.13.135
References:
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J. M. Heffernan and L. M. Wahl, Monte carlo estimates of natural variation in HIV infection, J. Theor. Biol., 236 (2005), 137-153. doi: 10.1016/j.jtbi.2005.03.002.  Google Scholar

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V. Herz, S. Bonhoeffer, R. Anderson, R. M. May and M. A. Nowak, Viral dynamics in vivo: Limitations on estimations on intracellular delay and virus delay, Proc. Natl. Acad. Sci. USA, 93 (1996), 7247-7251. doi: 10.1073/pnas.93.14.7247.  Google Scholar

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M. Y. Li and H. Shu, Impact of intercellular delays and target-cell dynamics on in vivo viral infections, SIAM J. Appl. Math., 70 (2010), 2434-2448. doi: 10.1137/090779322.  Google Scholar

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S. Liu and L. Wang, Global stability of an HIV-1 model with distributed intercellular delays and a combination therapy, Mathematical Biosciences and Engineering, 7 (2010), 675-685. doi: 10.3934/mbe.2010.7.675.  Google Scholar

[18]

X. Lu, L. Hui, S. Liu and J. Li, A Mathematical model of HTLV-1 infection with two time delays, Mathematical Biosciences and Engineering, 12 (2015), 431-449. doi: 10.3934/mbe.2015.12.431.  Google Scholar

[19]

L. Montagnier, Historical essay. A history of HIV discovery, Science, 298 (2002), 1727-1728. doi: 10.1126/science.1079027.  Google Scholar

[20]

M. A. Nowak and C. Bangham, Population dynamics of immune responses to persistent viruses, Science, 272 (1996), 74-79. doi: 10.1126/science.272.5258.74.  Google Scholar

[21]

M. A. Nowak and R. M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, Oxford, 2000.  Google Scholar

[22]

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

[23]

J. E. Pearson, P. Krapivsky and A. S. Perelson, Stochastic theory of early viral infection: Continuous versus burst productionofvirions, PLoS Comput Biol., 7 (2011), e1001058, 17 pp. doi: 10.1371/journal.pcbi.1001058.  Google Scholar

[24]

A. S. Perelson, A. U. Neumann, M. Markowitz, J. M. Leonard and D. D. Ho, HIV-1 dynamics in vivo: Virion clearance rate, infected cell life-span, and viral generation time, Science, 271 (1996), 1582-1586. doi: 10.1126/science.271.5255.1582.  Google Scholar

[25]

R. Ribeiro, L. Qin, L. Chavez, D. Li, S. Self and A. Perelson, Estimation of the Initial Viral Growth Rate and Basic Reproductive Number during Acute HIV-1 Infection, Journal of Virology, 84 (2010), 6096-6102. doi: 10.1128/JVI.00127-10.  Google Scholar

[26]

L. Rong, Z. Feng and A. S. Perelson, Emergence of HIV-1 drug resistance during antiretroviral treatment, Bulletin of Mathematical Biology, 69 (2007), 2027-2060. doi: 10.1007/s11538-007-9203-3.  Google Scholar

[27]

H. Shu, L. Wang and J. Watmough, Global stability of a nonlinear viral infection model with infinitely distributed intracellular delays and CTL immune responses, SIAM J. Appl. Math., 73 (2013), 1280-1302. doi: 10.1137/120896463.  Google Scholar

[28]

H. L. Smith and X. Zhao, Robust persistence for semidynamical systems, Nonlinear Anal., 47 (2001), 6169-6179. doi: 10.1016/S0362-546X(01)00678-2.  Google Scholar

[29]

H. Song, W. Jiang and S. Liu, Virus dynamics model with intracellular delays and immune response, Mathematical biosciences and engineering, 12 (2015), 185-208. doi: 10.3934/mbe.2015.12.xx.  Google Scholar

[30]

M. A. Stafford, L. Corey, Y. Cao, E. S. Daar, D. D. Ho and A. S. Perelson, Modeling plasma virus concentration during primary HIV infection, J. Theor. Biol., 203 (2000), 285-301. doi: 10.1006/jtbi.2000.1076.  Google Scholar

[31]

X. Wang and S. Liu, A class of delayed viral models with saturation infection rate and immune response, Mathematical Methods in the Applied Science, 36 (2013), 125-142. doi: 10.1002/mma.2576.  Google Scholar

[32]

Y. Wang, Y. Zhou, F. Brauer and J. M. Heffernan, Viral dynamics model with CTL immune response incorporating antiretroviral therapy, J. Math. Biol., 67 (2013), 901-934. doi: 10.1007/s00285-012-0580-3.  Google Scholar

[33]

J. Wei and S. Ruan, Stability and bifurcation in a neural network model with two delays, Physica D, 130 (1999), 255-272. doi: 10.1016/S0167-2789(99)00009-3.  Google Scholar

[34]

X. Zhao, Dynamical Systems in Population Biology, Springer, Berlin, 2003. doi: 10.1007/978-0-387-21761-1.  Google Scholar

show all references

References:
[1]

, Possible transfusion-associated acquired immune deficiency syndrome (AIDS)- California,, MMWR Morb Mortal Wkly Rep, 31 (1982), 652.   Google Scholar

[2]

, Unexplained immunodeficiency and opportunistic infections infants-New York,, New Jersey, 31 (1982), 665.   Google Scholar

[3]

, Immunodeficiency among female sexual partners males with acquired immune deficiency syndrome(AIDS)-New York,, MMWR Morb Mortal Wkly Rep, 31 (1983), 697.   Google Scholar

[4]

D. Burg, L. Rong, A. Neumann and H. Dahari, Mathematical modeling of viral kinetics under immune control during primary HIV-1 infection, J. Theor. Biol., 259 (2009), 751-759. doi: 10.1016/j.jtbi.2009.04.010.  Google Scholar

[5]

M. Ciupe, B. Bivort, D. Bortz and P. Nelson, Estimating kinetic parameters from HIV primary infection data through the eyes of three different mathematical models, Math. Biosci., 200 (2006), 1-27. doi: 10.1016/j.mbs.2005.12.006.  Google Scholar

[6]

V. R. Culshaw, S. Ruan and R. J. Spiteri, Optimal HIV treatment by maximising immune response, J. Math. Biol., 48 (2004), 545-562. doi: 10.1007/s00285-003-0245-3.  Google Scholar

[7]

P. de Leenheer and H. L. Smith, Virus dynamics: A global analysis, SIAM J. Appl. Math., 63 (2003), 1313-1327. doi: 10.1137/S0036139902406905.  Google Scholar

[8]

A. S. Fauci, HIV and AIDS: 20 years of science, Nature Medicine, 9 (2003), 839-843. doi: 10.1038/nm0703-839.  Google Scholar

[9]

R. C. Gallo, Historical essay. The early years of HIV/AIDS, Science, 298 (2002), 1728-1730. doi: 10.1126/science.1078050.  Google Scholar

[10]

J. K. Hale and S. V. Lunel, Introduction to Functional Differential Equations, Springer-Verlag, New York, 1993. doi: 10.1007/978-1-4612-4342-7.  Google Scholar

[11]

J. M. Heffernan and L. M. Wahl, Monte carlo estimates of natural variation in HIV infection, J. Theor. Biol., 236 (2005), 137-153. doi: 10.1016/j.jtbi.2005.03.002.  Google Scholar

[12]

V. Herz, S. Bonhoeffer, R. Anderson, R. M. May and M. A. Nowak, Viral dynamics in vivo: Limitations on estimations on intracellular delay and virus delay, Proc. Natl. Acad. Sci. USA, 93 (1996), 7247-7251. doi: 10.1073/pnas.93.14.7247.  Google Scholar

[13]

G. Huang, Y. Takeuchi and W. Ma, Lyapunov functionals of delay differential equations model of viral infections, SIAM J. Appl. Math., 70 (2010), 2693-2708. doi: 10.1137/090780821.  Google Scholar

[14]

A. Kent and M. D. Sepkowitz, AIDS-The First 20 Years, The New England Journal of Medicine, 344 (2001), 1764-1772. Google Scholar

[15]

B. Li and S. Liu, A delayed HIV-1 model with multiple target cells and general nonlinear incidence rate, Journal of Biological Systems, 21 (2013), 1340012, 20pp. doi: 10.1142/S0218339013400123.  Google Scholar

[16]

M. Y. Li and H. Shu, Impact of intercellular delays and target-cell dynamics on in vivo viral infections, SIAM J. Appl. Math., 70 (2010), 2434-2448. doi: 10.1137/090779322.  Google Scholar

[17]

S. Liu and L. Wang, Global stability of an HIV-1 model with distributed intercellular delays and a combination therapy, Mathematical Biosciences and Engineering, 7 (2010), 675-685. doi: 10.3934/mbe.2010.7.675.  Google Scholar

[18]

X. Lu, L. Hui, S. Liu and J. Li, A Mathematical model of HTLV-1 infection with two time delays, Mathematical Biosciences and Engineering, 12 (2015), 431-449. doi: 10.3934/mbe.2015.12.431.  Google Scholar

[19]

L. Montagnier, Historical essay. A history of HIV discovery, Science, 298 (2002), 1727-1728. doi: 10.1126/science.1079027.  Google Scholar

[20]

M. A. Nowak and C. Bangham, Population dynamics of immune responses to persistent viruses, Science, 272 (1996), 74-79. doi: 10.1126/science.272.5258.74.  Google Scholar

[21]

M. A. Nowak and R. M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, Oxford, 2000.  Google Scholar

[22]

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

[23]

J. E. Pearson, P. Krapivsky and A. S. Perelson, Stochastic theory of early viral infection: Continuous versus burst productionofvirions, PLoS Comput Biol., 7 (2011), e1001058, 17 pp. doi: 10.1371/journal.pcbi.1001058.  Google Scholar

[24]

A. S. Perelson, A. U. Neumann, M. Markowitz, J. M. Leonard and D. D. Ho, HIV-1 dynamics in vivo: Virion clearance rate, infected cell life-span, and viral generation time, Science, 271 (1996), 1582-1586. doi: 10.1126/science.271.5255.1582.  Google Scholar

[25]

R. Ribeiro, L. Qin, L. Chavez, D. Li, S. Self and A. Perelson, Estimation of the Initial Viral Growth Rate and Basic Reproductive Number during Acute HIV-1 Infection, Journal of Virology, 84 (2010), 6096-6102. doi: 10.1128/JVI.00127-10.  Google Scholar

[26]

L. Rong, Z. Feng and A. S. Perelson, Emergence of HIV-1 drug resistance during antiretroviral treatment, Bulletin of Mathematical Biology, 69 (2007), 2027-2060. doi: 10.1007/s11538-007-9203-3.  Google Scholar

[27]

H. Shu, L. Wang and J. Watmough, Global stability of a nonlinear viral infection model with infinitely distributed intracellular delays and CTL immune responses, SIAM J. Appl. Math., 73 (2013), 1280-1302. doi: 10.1137/120896463.  Google Scholar

[28]

H. L. Smith and X. Zhao, Robust persistence for semidynamical systems, Nonlinear Anal., 47 (2001), 6169-6179. doi: 10.1016/S0362-546X(01)00678-2.  Google Scholar

[29]

H. Song, W. Jiang and S. Liu, Virus dynamics model with intracellular delays and immune response, Mathematical biosciences and engineering, 12 (2015), 185-208. doi: 10.3934/mbe.2015.12.xx.  Google Scholar

[30]

M. A. Stafford, L. Corey, Y. Cao, E. S. Daar, D. D. Ho and A. S. Perelson, Modeling plasma virus concentration during primary HIV infection, J. Theor. Biol., 203 (2000), 285-301. doi: 10.1006/jtbi.2000.1076.  Google Scholar

[31]

X. Wang and S. Liu, A class of delayed viral models with saturation infection rate and immune response, Mathematical Methods in the Applied Science, 36 (2013), 125-142. doi: 10.1002/mma.2576.  Google Scholar

[32]

Y. Wang, Y. Zhou, F. Brauer and J. M. Heffernan, Viral dynamics model with CTL immune response incorporating antiretroviral therapy, J. Math. Biol., 67 (2013), 901-934. doi: 10.1007/s00285-012-0580-3.  Google Scholar

[33]

J. Wei and S. Ruan, Stability and bifurcation in a neural network model with two delays, Physica D, 130 (1999), 255-272. doi: 10.1016/S0167-2789(99)00009-3.  Google Scholar

[34]

X. Zhao, Dynamical Systems in Population Biology, Springer, Berlin, 2003. doi: 10.1007/978-0-387-21761-1.  Google Scholar

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