Discrete Dynamics in Nature and Society

Volume 2013 (2013), Article ID 264759, 8 pages

http://dx.doi.org/10.1155/2013/264759

Research Article

Global Dynamics of HIV Infection of CD4 ⁺ T Cells and Macrophages

A. M. Elaiw1,2 and A. S. Alsheri1,3

¹Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia

²Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut 71511, Egypt

³Department of Mathematics, Science and Literature College in Namas, King Khalid University, Abha 61431, Saudi Arabia

Received 16 May 2013; Revised 7 July 2013; Accepted 8 July 2013

Academic Editor: Zhengqiu Zhang

Copyright © 2013 A. M. Elaiw and A. S. Alsheri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We study the global dynamics of an HIV infection model describing the interaction of the HIV with CD4⁺ T cells and macrophages. The incidence rate of virus infection and the growth rate of the uninfected CD4⁺ T cells and macrophages are given by general functions. We have incorporated two types of distributed delays into the model to account for the time delay between the time the uninfected cells are contacted by the virus particle and the time for the emission of infectious (matures) virus particles. We have established a set of conditions which are sufficient for the global stability of the steady states of the model. Using Lyapunov functionals and LaSalle's invariant principle, we have proven that if the basic reproduction number is less than or equal to unity, then the uninfected steady state is globally asymptotically stable (GAS), and if the infected steady state exists, then it is GAS.

1. Introduction

Recently, many mathematical models have been developed to describe the interaction of human immunodeficiency virus (HIV) with the immune system [1]. Those mathematical models can provide some insights into the dynamics of HIV viral load in vivo and may play a significant role in the development of a better understanding of HIV/AIDS and drug therapies. For example, they provided a quantitative understanding of the level of virus production during the long asymptomatic stage of HIV infection [2].

The basic mathematical model describing the dynamics of HIV infection of CD4⁺ T cells is given by [3]

where , , and represent the populations of the uninfected CD4⁺ T cells, infected cells, and free virus particles, respectively. The uninfected cells are generated from sources within the body at a rate . The parameter is the death rate constant of the uninfected cells. The incidence rate is given by the bilinear form , where is the rate constant characterizing infections of the cells. Equation (2) describes the population dynamics of the infected cells and shows that they die with rate constant . The virus particles are produced by the infected cells with a rate constant and are cleared from plasma with a rate constant .

Many researchers suggested that the bilinear incidence rate is insufficient to describe the infection process in detail [4–8]. Therefore, different forms of the incidence rate of infection have been proposed such as saturated incidence rate [4], Holling type II functional response [5], Beddington-DeAngelis infection rate [6], Crowley-Martin functional response [7, 8], where , , and a general function [9].

In model (1)–(3), it is assumed that the infection could occur and the viruses are produced from infected cells instantaneously, once the uninfected cells are contacted by the virus particles. However, this assumption is unrealistic. Therefore, more realistic HIV dynamics models incorporate the delay between the time of viral entry into the uninfected cell and the time for the production of new virus particles, modeled with discrete time delay or distributed time delay using functional differential equations (see, e.g., [2, 5, 10–18]). As pointed by Li and Shu [18], the period between the time for HIV to enter the target cell and the time for new virions to be produced from the infected cell needs the following stages: (i) the period between the viral entry of a target cell and integration of viral DNA into the host genome, (ii) the period from the integration of viral DNA to the transcription of viral RNA and translation of viral proteins such as reverse transcriptase, integrase, and protease, and (iii) the period between the transcription of viral RNA and the release and maturation of virus [18].

All of the afore mentioned delayed HIV infection models are mainly modeled with the interaction of the HIV with one class of target cells, CD4⁺ T cells. More accurate modeling was developed in 1997 when Perelson et al. [19] observed that, after the rapid first phase of decay during the initial 1-2 weeks of antiretroviral treatment, plasma virus levels declined at a considerably slower rate. This second phase of viral decay was attributed to the turnover of a longer-lived virus reservoir of infected cells. These cells are called macrophages and considered as the second target cell for the HIV. To model the second class of target cells, two additional equations describing the population dynamics of the uninfected and infected macrophages have to be added to the basic model (1)–(3) (see [20, 21]). In [20–27], the HIV models have been proposed to describe the HIV dynamics with the CD4⁺ T cells and macrophages. The global stability analysis of these models has been investigated in [24–27]. Elaiw [24] studied the global properties of HIV infection model with bilinear and nonlinear incidence rates. In [25], Beddington-DeAngelis functional response has been considered. In [24, 25], the effect of time delay is neglected. Elaiw et al. [26] studied the global stability of HIV model with Beddington-DeAngelis functional response and one kind of discrete time delay. Elaiw [27] studied the global dynamics of a delay HIV model with saturated functional response.

In this paper, we study the global dynamics of HIV infection of CD4⁺ T cells and macrophages. We assume that the incidence rate and the growth rate of the uninfected cells are given by general functions. We incorporate two types of distributed delays into the model to account for the time delay between the time the uninfected cells are contacted by the virus particle and the time for the emission of infectious (mature) virus particles. We established a set of conditions which are sufficient for the global stability of the steady states of the model. Using Lyapunov functionals and LaSalle’s invariant principle, we prove that if the basic reproduction number is less than or equal to unity, then the uninfected steady state is globally asymptotically stable (GAS), and if the infected steady state exists, then it is GAS.

2. The Model

In this sections we propose an HIV dynamics model which describes the interaction of the HIV with the CD4⁺ T cells and macrophages. Two types of distributed time delays are incorporated into the model

where and represent the populations of the uninfected and infected cells, respectively, where and correspond to CD4⁺ T cells and macrophages. Parameters and satisfy and , . All the variables and other parameters of the model have the same meanings as given in model (1)–(3). To take into account the delay between viral infection of an uninfected target cell and the production of an actively infected target cell, we let be the random variable that describes the time between viral entry and the transcription of viral RNA (stages (i) and (ii)) with a probability distribution over the interval and is limit superior of this delay. On the other hand, to consider the delay between viral RNA transcription and viral release and maturation, we let be the random variable that is the time between these two events with a probability distribution over the interval and is limit superior of this delay [18]. The probability distribution functions and are integral functions with

Function represents the rate for the uninfected target cell of class to be infected by the mature viruses. Special forms of function have been presented in the literature as follows:

(i)bilinear incidence rate [3]: ,(ii)saturated incidence rate [4, 28, 29]: ,(iii)Holling type II functional response [5]: ,(iv)Beddington-DeAngelis infection rate [6, 25]: ,(v)Crowley-Martin functional response [7, 8, 30]: .

The growth rate of the uninfected cells is given by general function . The following particular forms of function have widely been used in the literature of HIV dynamics:

where is the maximum proliferation rate of the target cells of class and is the maximum level of uninfected cells population in the body [13, 16, 31].

Initial Conditions. The initial conditions for system (4)–(6) take the form

where and is the Banach space of continuous functions mapping the interval into . By the fundamental theory of functional differential equations [32], system (4)–(6) has a unique solution satisfying initial conditions (9).

We assume that functions and satisfy the following assumptions.

Assumption 1. Function satisfies the following:

(i) is continuous and differentiable and ,(ii)there exits an such that

Assumption 2. (i) is positive, continuous, and differentiable for all , and ,

(ii) , for any , , ,

(iii) , for all , , .

2.1. Nonnegativity and Boundedness of Solutions

In the following, we establish the nonnegativity and boundedness of solutions of (4)–(6) with initial conditions (9).

Proposition 3. Assume that Assumptions 1 and 2 are satisfied. Let be any solution of (4)–(6) satisfying the initial conditions (9); then is nonnegative for and ultimately bounded.

Proof. First, we prove that , , for all . Assume that loses its nonnegativity on some local existence interval for some constant and let be such that , . From (4), we have

Hence, for some , where is sufficiently small. This leads to a contradiction and hence for all . Further, from (5) and (6), we have

confirming that , , and for all . By a recursive argument, we obtain , , and for all .

Now, we show the boundedness of the solutions of (4)–(6). Assumption 1 and (4) imply that . It follows that

Let , and , ; then

Hence, , where . Since , we get . On the other hand,

Then , where . Therefore, is ultimately bounded.

2.2. Steady States

Let Assumptions 1 and 2 be satisfied; then system (4)–(6) has an uninfected steady state , where is defined in Assumption 1, , , and . The system can also have another steady state which is the infected steady state; the coordinates of the infected steady state, if they exist, satisfy the equalities

We define the basic reproduction number for system (4)–(6) as

The term denotes the maximal average number of target cells that each virus infects, and is the basic reproduction number for the dynamics of the virus and the uninfected cell of class .

2.3. Global Stability Analysis

In this section, we establish a set of conditions which are sufficient for the global stability of the uninfected and infected steady states of system (4)–(6). The strategy of the proofs is to use suitable Lyapunov functionals which are similar in nature to those used in [33, 34]. We will use the following notation: for any . We also define a function as

It is clear that for any and has the global minimum .

Assumption 4. , for all .

Assumption 5. , .

The global stability of the uninfected steady state will be established in the next theorem.

Theorem 6. If Assumptions 1–5 hold true and , then is GAS.

Proof. Define a Lyapunov functional as follows:

where

Functions and satisfy

We note that is defined and continuous for all . Also, the global minimum occurs at the uninfected steady state . The time derivative of along the solution of (4)–(6) is given by

The first term of (22) is less than or equal to zero according to Assumption 5, so it can be seen that if , then for all , . By Theorem in [32], the solutions of system (4)–(6) limit to , the largest invariant subset of . Clearly, it follows from (22) that if and only if , , and . Noting that is invariant, for each element of , we have , and then . From (6), we derive that

Since for all , then if and only if , . Hence, if and only if , , , and . From LaSalle’s invariance principle, is GAS.

To establish the global stability of the infected steady state, we need the following conditions.

Assumption 7. .

Assumption 8. .

Theorem 9. Assume that Assumptions 1, 2, 7, and 8 hold true and exists; then is GAS.

Proof. Define Lyapunov functional as follows:

By calculating the time derivative along (4)–(6), we get

Collecting terms of (25), we obtain

Using the infected steady state conditions (16), we get

Using the following equalities:

we obtain

Clearly, if exists, then for all , , and if and only if , , and , which is the infected steady state . It follows that is GAS.

3. Conclusion

In this paper, we have proposed an HIV dynamics model describing the interaction of the HIV with CD4⁺ T cells and macrophages. The incidence rate of virus infection and the growth rate of the uninfected cells are given by general functions. Two types of distributed delays have been incorporated into the model to account for the time delay between the time the uninfected cells are contacted by the virus particle and the time for the emission of infectious (mature) virus particles. We have established a set of conditions which are sufficient for the global stability of the steady states of the model. Using Lyapunov functionals and LaSalle's invariant principle, we have proven that if , then the uninfected steady state is GAS, and if the infected steady state exists, then it is GAS.

Acknowledgments

This article was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah. The authors, therefore, acknowledge with thanks the DSR technical and financial support. The authors are grateful to the anonymous reviewers for constructive suggestions and valuable comments, which improve the quality of the paper.

References

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

Z. Yuan and X. Zou, “Global threshold dynamics in an HIV virus model with nonlinear infection rate and distributed invasion and production delays,” Mathematical Biosciences and Engineering, vol. 10, no. 2, pp. 483–498, 2013.

M. A. Nowak and C. R. M. Bangham, “Population dynamics of immune responses to persistent viruses,” Science, vol. 272, no. 5258, pp. 74–79, 1996.

X. Song and A. U. Neumann, “Global stability and periodic solution of the viral dynamics,” Journal of Mathematical Analysis and Applications, vol. 329, no. 1, pp. 281–297, 2007.

G. Huang, H. Yokoi, Y. Takeuchi, T. Kajiwara, and T. Sasaki, “Impact of intracellular delay, immune activation delay and nonlinear incidence on viral dynamics,” Japan Journal of Industrial and Applied Mathematics, vol. 28, no. 3, pp. 383–411, 2011.

G. Huang, W. Ma, and Y. Takeuchi, “Global properties for virus dynamics model with Beddington-DeAngelis functional response,” Applied Mathematics Letters, vol. 22, no. 11, pp. 1690–1693, 2009.

S. Xu, “Global stability of the virus dynamics model with Crowley-Martin functional response,” Electronic Journal of Qualitative Theory of Differential Equations, 2013.

X. Zhou and J. Cui, “Global stability of the viral dynamics with Crowley-Martin functional response,” Bulletin of the Korean Mathematical Society, vol. 48, no. 3, pp. 555–574, 2011.

A. Korobeinikov, “Global asymptotic properties of virus dynamics models with dose-dependent parasite reproduction and virulence, and non-linear incidence rate,” Mathematical Medicine and Biology, vol. 26, pp. 225–239, 2009.

R. V. Culshaw and S. Ruan, “A delay-differential equation model of HIV infection of CD4⁺ T-cells,” Mathematical Biosciences, vol. 165, no. 1, pp. 27–39, 2000.

P. W. Nelson, J. D. Murray, and A. S. Perelson, “A model of HIV-1 pathogenesis that includes an intracellular delay,” Mathematical Biosciences, vol. 163, no. 2, pp. 201–215, 2000.

N. M. Dixit and A. S. Perelson, “Complex patterns of viral load decay under antiretroviral therapy: influence of pharmacokinetics and intracellular delay,” Journal of Theoretical Biology, vol. 226, no. 1, pp. 95–109, 2004.

S. Liu and L. Wang, “Global stability of an HIV-1 model with distributed intracellular delays and a combination therapy,” Mathematical Biosciences and Engineering, vol. 7, no. 3, pp. 675–685, 2010.

G. Huang, W. Ma, and Y. Takeuchi, “Global analysis for delay virus dynamics model with Beddington-DeAngelis functional response,” Applied Mathematics Letters, vol. 24, no. 7, pp. 1199–1203, 2011.

P. W. Nelson and A. S. Perelson, “Mathematical analysis of delay differential equation models of HIV-1 infection,” Mathematical Biosciences, vol. 179, no. 1, pp. 73–94, 2002.

R. Xu, “Global dynamics of an HIV-1 infection model with distributed intracellular delays,” Computers & Mathematics with Applications, vol. 61, no. 9, pp. 2799–2805, 2011.

G. Huang, Y. Takeuchi, and W. Ma, “Lyapunov functionals for delay differential equations model of viral infections,” SIAM Journal on Applied Mathematics, vol. 70, no. 7, pp. 2693–2708, 2010.

M. Y. Li and H. Shu, “Impact of intracellular delays and target-cell dynamics on in vivo viral infections,” SIAM Journal on Applied Mathematics, vol. 70, no. 7, pp. 2434–2448, 2010.

A. S. Perelson, P. Essunger, Y. Cao et al., “Decay characteristics of HIV-1-infected compartments during combination therapy,” Nature, vol. 387, no. 6629, pp. 188–191, 1997.

D. S. Callaway and A. S. Perelson, “HIV-1 infection and low steady state viral loads,” Bulletin of Mathematical Biology, vol. 64, no. 1, pp. 29–64, 2002.

A. S. Perelson and P. W. Nelson, “Mathematical analysis of HIV-1 dynamics in vivo,” SIAM Review, vol. 41, no. 1, pp. 3–44, 1999.

A. M. Elaiw and X. Xia, “HIV dynamics: analysis and robust multirate MPC-based treatment schedules,” Journal of Mathematical Analysis and Applications, vol. 359, no. 1, pp. 285–301, 2009.

A. M. Elaiw and A. M. Shehata, “Stability and feedback stabilization of HIV infection model with two classes of target cells,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 963864, 20 pages, 2012.

A. M. Elaiw, “Global properties of a class of HIV models,” Nonlinear Analysis, vol. 11, no. 4, pp. 2253–2263, 2010.

A. M. Elaiw and S. A. Azoz, “Global properties of a class of HIV infection models with Beddington-DeAngelis functional response,” Mathematical Methods in the Applied Sciences, vol. 36, no. 4, pp. 383–394, 2013.

A. Elaiw, I. Hassanien, and S. Azoz, “Global stability of HIV infection models with intracellular delays,” Journal of the Korean Mathematical Society, vol. 49, no. 4, pp. 779–794, 2012.

A. M. Elaiw, “Global dynamics of an HIV infection model with two classes of target cells and distributed delays,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 253703, 13 pages, 2012.

A. M. Elaiw, “Global properties of a class of virus infection models with multitarget cells,” Nonlinear Dynamics, vol. 69, no. 1-2, pp. 423–435, 2012.

A. M. Elaiw and M. A. Alghamdi, “Global properties of virus dynamics models with multitarget cells and discrete-time delays,” Discrete Dynamics in Nature and Society, vol. 2011, Article ID 201274, 19 pages, 2011.

A. A. Bakr, A. M. Elaiw, and Z. A. S. Raizah, “Mathematical analysis of virus dynamics model with multitarget cells in vivo,” Mathematical Sciences Letters, vol. 2, no. 3, 2013.

M. A. Obaid, “Global analysis of a virus infection model with multitarget cells and distrib- uted intracellular delays,” Life Science Journal, vol. 9, no. 4, pp. 1500–1508, 2012.

J. K. Hale and S. M. Verduyn Lunel, Introduction to Functional-Differential Equations, vol. 99 of Applied Mathematical Sciences, Springer, New York, NY, USA, 1993.

C. C. McCluskey, “Complete global stability for an SIR epidemic model with delay—distributed or discrete,” Nonlinear Analysis, vol. 11, no. 1, pp. 55–59, 2010.

T. Kajiwara, T. Sasaki, and Y. Takeuchi, “Construction of Lyapunov functionals for delay differential equations in virology and epidemiology,” Nonlinear Analysis, vol. 13, no. 4, pp. 1802–1826, 2012.