June  2017, 14(3): 805-820. doi: 10.3934/mbe.2017044

A note on the global properties of an age-structured viral dynamic model with multiple target cell populations

1. 

School of Mathematics and Statistics, Henan University, Kaifeng 475001, Henan, China

2. 

Centre for Disease Modelling, York University, Toronto, Ontario, M3J 1P3, Canada

3. 

Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309, USA

Received  January 25, 2016 Accepted  October 30, 2016 Published  December 2016

Fund Project: This work was finished when S. Wang visited York University. Wang is supported by NSFC (No.11326200), Foundation of He’nan Educational Committee (No.15A110015, No.15A110018), and The Grant of China Scholarship Council (No.201408410018). L. Rong is partially supported by the NSF grant DMS-1349939

Some viruses can infect different classes of cells. The age of infection can affect the dynamics of infected cells and viral production. Here we develop a viral dynamic model with the age of infection and multiple target cell populations. Using the methods of semigroup and Lyapunov function, we study the global asymptotic property of the steady states of the model. The results show that when the basic reproductive number falls below 1, the infection is predicted to die out. When the basic reproductive number exceeds 1, there exists a unique infected steady state which is globally asymptotically stable. The model can be extended to study virus dynamics with multiple compartments or coinfection by multiple types of viruses. We also show that under some scenarios the age-structured model can be reduced to an ordinary differential equation system with or without time delays.

Citation: Shaoli Wang, Jianhong Wu, Libin Rong. A note on the global properties of an age-structured viral dynamic model with multiple target cell populations. Mathematical Biosciences & Engineering, 2017, 14 (3) : 805-820. doi: 10.3934/mbe.2017044
References:
[1]

A. AlshormanC. SamarasingheW. Lu and L. Rong, An HIV model with age-structured latently infected cells, J. Biol. Dyn., (2015), 1-24. doi: 10.1080/17513758.2016.1198835. Google Scholar

[2]

R. P. Beasley, Hepatocellular carcinoma and hepatitis B virus, Lancet, 2 (1981), 1129-1133. Google Scholar

[3]

F. BrauerZ. Shuai and P. van den Driessche, Dynamics of an age-of-infection choleramodel, Math. Biosci. Eng., 10 (2013), 1335-1349. doi: 10.3934/mbe.2013.10.1335. Google Scholar

[4]

C. J. Browne, A multi-strain virus model with infected cell age structure: Application to HIV, Nonlinear Anal. Real World Appl., 22 (2015), 354-372. doi: 10.1016/j.nonrwa.2014.10.004. Google Scholar

[5]

C. J. Browne and S. S. Pilyugin, Global analysis of age-structured within-host virus model, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 1999-2017. doi: 10.3934/dcdsb.2013.18.1999. Google Scholar

[6]

C. A. Carter and L. S. Ehrlich, Cell biology of HIV-1 infection of macrophages, Annu. Rev. Microbiol., 62 (2008), 425-443. doi: 10.1146/annurev.micro.62.081307.162758. Google Scholar

[7]

I. Castillo, Hepatitis C virus replicates in peripheral blood mononuclear cells of patients with occult hepatitis C virus infection, Gut., 54 (2005), 682-685. doi: 10.1136/gut.2004.057281. Google Scholar

[8]

C. Ferrari, Cellular immune response to hepatitis B virus encoded antigens in a cute and chronic hepatitis B virus infection, J. Immunol., 145 (1990), 3442-3449. Google Scholar

[9]

M. A. GilchristD. Coombs and A. S. Perelson, Optimizing within-host viral fitness: Infected cell lifespan and virion production rate, J. Theoret. Biol., 229 (2004), 281-288. doi: 10.1016/j.jtbi.2004.04.015. Google Scholar

[10]

J. K. Hale, Asymptotic Behavior of Dissipative Systems, Mathematical Surveys and Mono-graphs, American Mathematical Society, Providence, RI, 1988. Google Scholar

[11]

E. A. Hernandez-Vargas and R. H. Middleton, Modeling the three stages in HIV infection, J. Theor. Biol., 320 (2013), 33-40. doi: 10.1016/j.jtbi.2012.11.028. Google Scholar

[12]

G. HuangX. Liu and Y. Takeuchi, Lyapunov functions and global stability for age-structured HIV infection model, SIAM J. Appl. Math., 72 (2012), 25-38. doi: 10.1137/110826588. Google Scholar

[13]

S. Koenig, Detection of AIDS virus in macrophages in brain tissue from AIDS patients with encephalopathy, Science, 233 (1986), 1089-1093. doi: 10.1126/science.3016903. Google Scholar

[14]

A. Kumar and G. Herbein, The macrophage: A therapeutic target in HIV-1 infection Mol. Cell. Therapies, 2 (2014), 10pp. doi: 10.1186/2052-8426-2-10. Google Scholar

[15]

M. J. Kuroda, Macrophages: Do they impact AIDS progression more than CD4 T cells?, J. Leukoc. Biol., 87 (2010), 569-573. doi: 10.1189/jlb.0909626. Google Scholar

[16]

X. Lai and X. Zou, Dynamics of evolutionary competition between budding and lytic viral release strategies, Math. Biosci. Eng., 11 (2014), 1091-1113. doi: 10.3934/mbe.2014.11.1091. Google Scholar

[17]

X. Lai and X. Zou, Modeling HIV-1 virus dynamics with both virus-to-cell Infection and cell-to-cell transmission, Math. Biosci. Eng., 11 (2014), 1091-1113. doi: 10.1137/130930145. Google Scholar

[18]

M. Y. Li and H. Shu, Global dynamics of an in-host viral model with intracellular delay, Bull. Math. Biol., 72 (2010), 1492-1505. doi: 10.1007/s11538-010-9503-x. Google Scholar

[19]

P. Magal, Compact attractors for time periodic age-structured population models, Electron J. Differ. Equ., 65 (2001), 1-35. Google Scholar

[20]

P. Magal and C. C. McCluskey, Two-group infection age model including an application to nosocomial infection, SIAM J. Appl. Math., 73 (2013), 1058-1095. doi: 10.1137/120882056. Google Scholar

[21]

P. Magal and H. R. Thieme, Eventual compactness for semi ows generated by nonlinear age-structured models, Commun. Pure Appl. Anal., 3 (2004), 695-727. doi: 10.3934/cpaa.2004.3.695. Google Scholar

[22]

P. W. NelsonM. A. GilchristD. CoombsJ. M. Hyman and A. S. Perelson, An agestructured model of HIV infection that allows for variations in the production rate of viral particles and the death rate of productively infected cells, Math. Biosci. Eng., 1 (2004), 267-288. doi: 10.3934/mbe.2004.1.267. Google Scholar

[23]

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

[24]

M. A. Nowak, Viral dynamics in hepatitis B virus infection, Proc. Natl. Acad. Sci. USA, 93 (1996), 4398-4402. doi: 10.1073/pnas.93.9.4398. Google Scholar

[25]

M. Pope, Conjugates of dendritic cells and memory T lymphocytes from skin facilitate productive infection with HIV-1, Cell, 78 (1994), 389-398. doi: 10.1016/0092-8674(94)90418-9. Google Scholar

[26]

F. Regenstein, New approaches to the treatment of chronic viral-hepatitis-B and viral-hepatitis C, Am. J. Med., 96 (1994), 47-51. Google Scholar

[27]

L. Rong, H. Dahari, R. M. Ribeiro and A. S. Perelson, Rapid emergence of protease inhibitor resistance in hepatitis C virus Sci. Transl. Med., 2 (2010), 30ra32. doi: 10.1126/scitranslmed.3000544. Google Scholar

[28]

L. RongZ. Feng and A. S. Perelson, Mathematical analysis of age-structured HIV-1 dynamics with combination antiviral theraphy, SIAM J. Appl. Math., 67 (2007), 731-756. doi: 10.1137/060663945. Google Scholar

[29]

L. Rong, J. Guedj, H. Dahari, D. Coffield, M. Levi, P. Smith and A. S. Perelson, Analysis of hepatitis C virus decline during treatment with the protease inhibitor danoprevir using a multiscale model PLoS Comput. Biol., 9 (2013), e1002959, 12pp. doi: 10.1371/journal.pcbi.1002959. Google Scholar

[30]

M. ShenY. Xiao and L. Rong, Global stability of an infection-age structured HIV-1 model linking within-host and between-host dynamics, Math. Biosci., 263 (2015), 37-50. doi: 10.1016/j.mbs.2015.02.003. Google Scholar

[31]

X. Song and A. U. Neumann, Global stability and periodic solution of the viral dynamics, J. Math. Anal. Appl., 329 (2007), 281-297. doi: 10.1016/j.jmaa.2006.06.064. Google Scholar

[32]

H. R. Thieme, Semiflows generated by Lipschitz perturbations of non-densely defined operators, Differ. Integral Equ., 3 (1990), 1035-1066. Google Scholar

[33]

S. WangX. Feng and Y. He, Global asymptotical properties for a diffused HBV infection model with CTL immune response and nonlinear incidence, Acta Math. Sci. Ser. B Engl. Ed., 31 (2011), 1959-1967. doi: 10.1016/S0252-9602(11)60374-3. Google Scholar

[34]

K. Wang and W. Wang, Propagation of HBV with spatial dependence, Math. Biosci., 210 (2007), 78-95. doi: 10.1016/j.mbs.2007.05.004. Google Scholar

[35]

K. WangW. Wang and S. Song, Dynamics of an HBV model with diffusion and delay, J. Theor. Biol., 253 (2008), 36-44. doi: 10.1016/j.jtbi.2007.11.007. Google Scholar

[36]

J. WangR. Zhang and T. Kuniya, Mathematical analysis for an age-structured HIV infection model with saturation infection rate, Electron J. Differ. Equ., 33 (2015), 1-19. Google Scholar

[37]

J. WangR. Zhang and T. Kuniya, The dynamics of an SVIR epidemiological model with infection age, IMA J. Appl. Math., 81 (2016), 321-343. doi: 10.1093/imamat/hxv039. Google Scholar

[38]

J. WangJ. Lang and F. Li, Constructing Lyapunov functionals for a delayed viral infection model with multitarget cells, nonlinear incidence rate, state-dependent removal rate, J. Nonlinear Sci. Appl., 9 (2016), 524-536. Google Scholar

[39]

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

[40]

J. WangR. Zhang and T. Kuniya, Global dynamics for a class of age-infection HIV models with nonlinear infection rate, J. Math. Anal. Appl., 432 (2015), 289-313. doi: 10.1016/j.jmaa.2015.06.040. Google Scholar

[41]

J. Wang and R. Zhang, A note on dynamics of an age-of-infection cholera model, Math. Biosci. Eng., 13 (2016), 227-247. doi: 10.3934/mbe.2016.13.227. Google Scholar

[42]

J. I. Weissberg, Survival in chronic hepatitis B: An analysis of 379 patients, Ann. Intern. Med., 101 (1984), 613-616. doi: 10.7326/0003-4819-101-5-613. Google Scholar

[43]

R. Xu and Z. Ma, An HBV model with diffusion and time delay, J. Theor. Biol., 257 (2009), 499-509. doi: 10.1016/j.jtbi.2009.01.001. Google Scholar

[44]

R. Xu, Global stability of an HIV-1 infection model with saturation infection and intracellular delay, J. Math. Anal. Appl., 375 (2011), 75-81. doi: 10.1016/j.jmaa.2010.08.055. Google Scholar

[45]

Y. YangS. Ruan and D. Xiao, Global stability of an age-structured virus dynamics model with Beddington-Deangelis infection function, Math. Biosci. Eng., 12 (2015), 859-877. doi: 10.3934/mbe.2015.12.859. Google Scholar

[46]

H. Zhu and X. Zou, Impact of delays in cell infection and virus production on HIV-1 dynamics, Math. Med. Biol., 25 (2008), 99-112. doi: 10.1093/imammb/dqm010. Google Scholar

show all references

References:
[1]

A. AlshormanC. SamarasingheW. Lu and L. Rong, An HIV model with age-structured latently infected cells, J. Biol. Dyn., (2015), 1-24. doi: 10.1080/17513758.2016.1198835. Google Scholar

[2]

R. P. Beasley, Hepatocellular carcinoma and hepatitis B virus, Lancet, 2 (1981), 1129-1133. Google Scholar

[3]

F. BrauerZ. Shuai and P. van den Driessche, Dynamics of an age-of-infection choleramodel, Math. Biosci. Eng., 10 (2013), 1335-1349. doi: 10.3934/mbe.2013.10.1335. Google Scholar

[4]

C. J. Browne, A multi-strain virus model with infected cell age structure: Application to HIV, Nonlinear Anal. Real World Appl., 22 (2015), 354-372. doi: 10.1016/j.nonrwa.2014.10.004. Google Scholar

[5]

C. J. Browne and S. S. Pilyugin, Global analysis of age-structured within-host virus model, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 1999-2017. doi: 10.3934/dcdsb.2013.18.1999. Google Scholar

[6]

C. A. Carter and L. S. Ehrlich, Cell biology of HIV-1 infection of macrophages, Annu. Rev. Microbiol., 62 (2008), 425-443. doi: 10.1146/annurev.micro.62.081307.162758. Google Scholar

[7]

I. Castillo, Hepatitis C virus replicates in peripheral blood mononuclear cells of patients with occult hepatitis C virus infection, Gut., 54 (2005), 682-685. doi: 10.1136/gut.2004.057281. Google Scholar

[8]

C. Ferrari, Cellular immune response to hepatitis B virus encoded antigens in a cute and chronic hepatitis B virus infection, J. Immunol., 145 (1990), 3442-3449. Google Scholar

[9]

M. A. GilchristD. Coombs and A. S. Perelson, Optimizing within-host viral fitness: Infected cell lifespan and virion production rate, J. Theoret. Biol., 229 (2004), 281-288. doi: 10.1016/j.jtbi.2004.04.015. Google Scholar

[10]

J. K. Hale, Asymptotic Behavior of Dissipative Systems, Mathematical Surveys and Mono-graphs, American Mathematical Society, Providence, RI, 1988. Google Scholar

[11]

E. A. Hernandez-Vargas and R. H. Middleton, Modeling the three stages in HIV infection, J. Theor. Biol., 320 (2013), 33-40. doi: 10.1016/j.jtbi.2012.11.028. Google Scholar

[12]

G. HuangX. Liu and Y. Takeuchi, Lyapunov functions and global stability for age-structured HIV infection model, SIAM J. Appl. Math., 72 (2012), 25-38. doi: 10.1137/110826588. Google Scholar

[13]

S. Koenig, Detection of AIDS virus in macrophages in brain tissue from AIDS patients with encephalopathy, Science, 233 (1986), 1089-1093. doi: 10.1126/science.3016903. Google Scholar

[14]

A. Kumar and G. Herbein, The macrophage: A therapeutic target in HIV-1 infection Mol. Cell. Therapies, 2 (2014), 10pp. doi: 10.1186/2052-8426-2-10. Google Scholar

[15]

M. J. Kuroda, Macrophages: Do they impact AIDS progression more than CD4 T cells?, J. Leukoc. Biol., 87 (2010), 569-573. doi: 10.1189/jlb.0909626. Google Scholar

[16]

X. Lai and X. Zou, Dynamics of evolutionary competition between budding and lytic viral release strategies, Math. Biosci. Eng., 11 (2014), 1091-1113. doi: 10.3934/mbe.2014.11.1091. Google Scholar

[17]

X. Lai and X. Zou, Modeling HIV-1 virus dynamics with both virus-to-cell Infection and cell-to-cell transmission, Math. Biosci. Eng., 11 (2014), 1091-1113. doi: 10.1137/130930145. Google Scholar

[18]

M. Y. Li and H. Shu, Global dynamics of an in-host viral model with intracellular delay, Bull. Math. Biol., 72 (2010), 1492-1505. doi: 10.1007/s11538-010-9503-x. Google Scholar

[19]

P. Magal, Compact attractors for time periodic age-structured population models, Electron J. Differ. Equ., 65 (2001), 1-35. Google Scholar

[20]

P. Magal and C. C. McCluskey, Two-group infection age model including an application to nosocomial infection, SIAM J. Appl. Math., 73 (2013), 1058-1095. doi: 10.1137/120882056. Google Scholar

[21]

P. Magal and H. R. Thieme, Eventual compactness for semi ows generated by nonlinear age-structured models, Commun. Pure Appl. Anal., 3 (2004), 695-727. doi: 10.3934/cpaa.2004.3.695. Google Scholar

[22]

P. W. NelsonM. A. GilchristD. CoombsJ. M. Hyman and A. S. Perelson, An agestructured model of HIV infection that allows for variations in the production rate of viral particles and the death rate of productively infected cells, Math. Biosci. Eng., 1 (2004), 267-288. doi: 10.3934/mbe.2004.1.267. Google Scholar

[23]

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

[24]

M. A. Nowak, Viral dynamics in hepatitis B virus infection, Proc. Natl. Acad. Sci. USA, 93 (1996), 4398-4402. doi: 10.1073/pnas.93.9.4398. Google Scholar

[25]

M. Pope, Conjugates of dendritic cells and memory T lymphocytes from skin facilitate productive infection with HIV-1, Cell, 78 (1994), 389-398. doi: 10.1016/0092-8674(94)90418-9. Google Scholar

[26]

F. Regenstein, New approaches to the treatment of chronic viral-hepatitis-B and viral-hepatitis C, Am. J. Med., 96 (1994), 47-51. Google Scholar

[27]

L. Rong, H. Dahari, R. M. Ribeiro and A. S. Perelson, Rapid emergence of protease inhibitor resistance in hepatitis C virus Sci. Transl. Med., 2 (2010), 30ra32. doi: 10.1126/scitranslmed.3000544. Google Scholar

[28]

L. RongZ. Feng and A. S. Perelson, Mathematical analysis of age-structured HIV-1 dynamics with combination antiviral theraphy, SIAM J. Appl. Math., 67 (2007), 731-756. doi: 10.1137/060663945. Google Scholar

[29]

L. Rong, J. Guedj, H. Dahari, D. Coffield, M. Levi, P. Smith and A. S. Perelson, Analysis of hepatitis C virus decline during treatment with the protease inhibitor danoprevir using a multiscale model PLoS Comput. Biol., 9 (2013), e1002959, 12pp. doi: 10.1371/journal.pcbi.1002959. Google Scholar

[30]

M. ShenY. Xiao and L. Rong, Global stability of an infection-age structured HIV-1 model linking within-host and between-host dynamics, Math. Biosci., 263 (2015), 37-50. doi: 10.1016/j.mbs.2015.02.003. Google Scholar

[31]

X. Song and A. U. Neumann, Global stability and periodic solution of the viral dynamics, J. Math. Anal. Appl., 329 (2007), 281-297. doi: 10.1016/j.jmaa.2006.06.064. Google Scholar

[32]

H. R. Thieme, Semiflows generated by Lipschitz perturbations of non-densely defined operators, Differ. Integral Equ., 3 (1990), 1035-1066. Google Scholar

[33]

S. WangX. Feng and Y. He, Global asymptotical properties for a diffused HBV infection model with CTL immune response and nonlinear incidence, Acta Math. Sci. Ser. B Engl. Ed., 31 (2011), 1959-1967. doi: 10.1016/S0252-9602(11)60374-3. Google Scholar

[34]

K. Wang and W. Wang, Propagation of HBV with spatial dependence, Math. Biosci., 210 (2007), 78-95. doi: 10.1016/j.mbs.2007.05.004. Google Scholar

[35]

K. WangW. Wang and S. Song, Dynamics of an HBV model with diffusion and delay, J. Theor. Biol., 253 (2008), 36-44. doi: 10.1016/j.jtbi.2007.11.007. Google Scholar

[36]

J. WangR. Zhang and T. Kuniya, Mathematical analysis for an age-structured HIV infection model with saturation infection rate, Electron J. Differ. Equ., 33 (2015), 1-19. Google Scholar

[37]

J. WangR. Zhang and T. Kuniya, The dynamics of an SVIR epidemiological model with infection age, IMA J. Appl. Math., 81 (2016), 321-343. doi: 10.1093/imamat/hxv039. Google Scholar

[38]

J. WangJ. Lang and F. Li, Constructing Lyapunov functionals for a delayed viral infection model with multitarget cells, nonlinear incidence rate, state-dependent removal rate, J. Nonlinear Sci. Appl., 9 (2016), 524-536. Google Scholar

[39]

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

[40]

J. WangR. Zhang and T. Kuniya, Global dynamics for a class of age-infection HIV models with nonlinear infection rate, J. Math. Anal. Appl., 432 (2015), 289-313. doi: 10.1016/j.jmaa.2015.06.040. Google Scholar

[41]

J. Wang and R. Zhang, A note on dynamics of an age-of-infection cholera model, Math. Biosci. Eng., 13 (2016), 227-247. doi: 10.3934/mbe.2016.13.227. Google Scholar

[42]

J. I. Weissberg, Survival in chronic hepatitis B: An analysis of 379 patients, Ann. Intern. Med., 101 (1984), 613-616. doi: 10.7326/0003-4819-101-5-613. Google Scholar

[43]

R. Xu and Z. Ma, An HBV model with diffusion and time delay, J. Theor. Biol., 257 (2009), 499-509. doi: 10.1016/j.jtbi.2009.01.001. Google Scholar

[44]

R. Xu, Global stability of an HIV-1 infection model with saturation infection and intracellular delay, J. Math. Anal. Appl., 375 (2011), 75-81. doi: 10.1016/j.jmaa.2010.08.055. Google Scholar

[45]

Y. YangS. Ruan and D. Xiao, Global stability of an age-structured virus dynamics model with Beddington-Deangelis infection function, Math. Biosci. Eng., 12 (2015), 859-877. doi: 10.3934/mbe.2015.12.859. Google Scholar

[46]

H. Zhu and X. Zou, Impact of delays in cell infection and virus production on HIV-1 dynamics, Math. Med. Biol., 25 (2008), 99-112. doi: 10.1093/imammb/dqm010. Google Scholar

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