The replicability of oncolytic virus: Defining conditions in tumor virotherapy

  • Received: 01 June 2010 Accepted: 29 June 2018 Published: 01 June 2011
  • MSC : Primary: 34C23, 34C10; Secondary: 92B99.

  • The replicability of an oncolytic virus is measured by its burst size. The burst size is the number of new viruses coming out from a lysis of an infected tumor cell. Some clinical evidences show that the burst size of an oncolytic virus is a defining parameter for the success of virotherapy. This article analyzes a basic mathematical model that includes burst size for oncolytic virotherapy. The analysis of the model shows that there are two threshold values of the burst size: below the first threshold, the tumor always grows to its maximum (carrying capacity) size; while passing this threshold, there is a locally stable positive equilibrium solution appearing through transcritical bifurcation; while at or above the second threshold, there exits one or three families of periodic solutions arising from Hopf bifurcations. The study suggests that the tumor load can drop to a undetectable level either during the oscillation or when the burst size is large enough.

    Citation: Jianjun Paul Tian. The replicability of oncolytic virus: Defining conditions in tumor virotherapy[J]. Mathematical Biosciences and Engineering, 2011, 8(3): 841-860. doi: 10.3934/mbe.2011.8.841

    Related Papers:

    [1] Yicang Zhou, Zhien Ma . Global stability of a class of discrete age-structured SIS models with immigration. Mathematical Biosciences and Engineering, 2009, 6(2): 409-425. doi: 10.3934/mbe.2009.6.409
    [2] Yanxia Dang, Zhipeng Qiu, Xuezhi Li . Competitive exclusion in an infection-age structured vector-host epidemic model. Mathematical Biosciences and Engineering, 2017, 14(4): 901-931. doi: 10.3934/mbe.2017048
    [3] Zhiping Liu, Zhen Jin, Junyuan Yang, Juan Zhang . The backward bifurcation of an age-structured cholera transmission model with saturation incidence. Mathematical Biosciences and Engineering, 2022, 19(12): 12427-12447. doi: 10.3934/mbe.2022580
    [4] Toshikazu Kuniya, Hisashi Inaba . Hopf bifurcation in a chronological age-structured SIR epidemic model with age-dependent infectivity. Mathematical Biosciences and Engineering, 2023, 20(7): 13036-13060. doi: 10.3934/mbe.2023581
    [5] Azmy S. Ackleh, Keng Deng, Yixiang Wu . Competitive exclusion and coexistence in a two-strain pathogen model with diffusion. Mathematical Biosciences and Engineering, 2016, 13(1): 1-18. doi: 10.3934/mbe.2016.13.1
    [6] Churni Gupta, Necibe Tuncer, Maia Martcheva . A network immuno-epidemiological model of HIV and opioid epidemics. Mathematical Biosciences and Engineering, 2023, 20(2): 4040-4068. doi: 10.3934/mbe.2023189
    [7] Tsuyoshi Kajiwara, Toru Sasaki, Yoji Otani . Global stability of an age-structured infection model in vivo with two compartments and two routes. Mathematical Biosciences and Engineering, 2022, 19(11): 11047-11070. doi: 10.3934/mbe.2022515
    [8] Xiaodan Sun, Yanni Xiao, Zhihang Peng . Modelling HIV superinfection among men who have sex with men. Mathematical Biosciences and Engineering, 2016, 13(1): 171-191. doi: 10.3934/mbe.2016.13.171
    [9] Abba B. Gumel, Baojun Song . Existence of multiple-stable equilibria for a multi-drug-resistant model of mycobacterium tuberculosis. Mathematical Biosciences and Engineering, 2008, 5(3): 437-455. doi: 10.3934/mbe.2008.5.437
    [10] Azizeh Jabbari, Carlos Castillo-Chavez, Fereshteh Nazari, Baojun Song, Hossein Kheiri . A two-strain TB model with multiplelatent stages. Mathematical Biosciences and Engineering, 2016, 13(4): 741-785. doi: 10.3934/mbe.2016017
  • The replicability of an oncolytic virus is measured by its burst size. The burst size is the number of new viruses coming out from a lysis of an infected tumor cell. Some clinical evidences show that the burst size of an oncolytic virus is a defining parameter for the success of virotherapy. This article analyzes a basic mathematical model that includes burst size for oncolytic virotherapy. The analysis of the model shows that there are two threshold values of the burst size: below the first threshold, the tumor always grows to its maximum (carrying capacity) size; while passing this threshold, there is a locally stable positive equilibrium solution appearing through transcritical bifurcation; while at or above the second threshold, there exits one or three families of periodic solutions arising from Hopf bifurcations. The study suggests that the tumor load can drop to a undetectable level either during the oscillation or when the burst size is large enough.


  • This article has been cited by:

    1. E. Numfor, S. Bhattacharya, S. Lenhart, M. Martcheva, S. Anita, N. Hritonenko, G. Marinoschi, A. Swierniak, Optimal Control in Coupled Within-host and Between-host Models, 2014, 9, 0973-5348, 171, 10.1051/mmnp/20149411
    2. Lin Zhao, Zhi-Cheng Wang, Shigui Ruan, Traveling wave solutions in a two-group epidemic model with latent period, 2017, 30, 0951-7715, 1287, 10.1088/1361-6544/aa59ae
    3. Rony Izhar, Jarkko Routtu, Frida Ben-Ami, Host age modulates within-host parasite competition, 2015, 11, 1744-9561, 20150131, 10.1098/rsbl.2015.0131
    4. Tufail Malik, Abba Gumel, Elamin H. Elbasha, Qualitative analysis of an age- and sex-structured vaccination model for human papillomavirus, 2013, 18, 1553-524X, 2151, 10.3934/dcdsb.2013.18.2151
    5. Robert Rowthorn, Selma Walther, The optimal treatment of an infectious disease with two strains, 2017, 74, 0303-6812, 1753, 10.1007/s00285-016-1074-5
    6. Jemal Mohammed-Awel, Eric Numfor, Ruijun Zhao, Suzanne Lenhart, A new mathematical model studying imperfect vaccination: Optimal control analysis, 2021, 500, 0022247X, 125132, 10.1016/j.jmaa.2021.125132
    7. Mohammad A. Safi, Abba B. Gumel, Elamin H. Elbasha, Qualitative analysis of an age-structured SEIR epidemic model with treatment, 2013, 219, 00963003, 10627, 10.1016/j.amc.2013.03.126
    8. S.M. Garba, M.A. Safi, A.B. Gumel, Cross-immunity-induced backward bifurcation for a model of transmission dynamics of two strains of influenza, 2013, 14, 14681218, 1384, 10.1016/j.nonrwa.2012.10.003
    9. Toshikazu Kuniya, Jinliang Wang, Hisashi Inaba, A multi-group SIR epidemic model with age structure, 2016, 21, 1531-3492, 3515, 10.3934/dcdsb.2016109
    10. Roberto Cavoretto, Simona Collino, Bianca Giardino, Ezio Venturino, A two-strain ecoepidemic competition model, 2015, 8, 1874-1738, 37, 10.1007/s12080-014-0232-x
    11. Eminugroho Ratna Sari, Fajar Adi-Kusumo, Lina Aryati, Mathematical analysis of a SIPC age-structured model of cervical cancer, 2022, 19, 1551-0018, 6013, 10.3934/mbe.2022281
    12. Chin-Lung Li, Chang-Yuan Cheng, Chun-Hsien Li, Global dynamics of two-strain epidemic model with single-strain vaccination in complex networks, 2023, 69, 14681218, 103738, 10.1016/j.nonrwa.2022.103738
    13. S.Y. Tchoumi, H. Rwezaura, J.M. Tchuenche, Dynamic of a two-strain COVID-19 model with vaccination, 2022, 39, 22113797, 105777, 10.1016/j.rinp.2022.105777
    14. Ting Cui, Peijiang Liu, Fractional transmission analysis of two strains of influenza dynamics, 2022, 40, 22113797, 105843, 10.1016/j.rinp.2022.105843
    15. Shasha Gao, Mingwang Shen, Xueying Wang, Jin Wang, Maia Martcheva, Libin Rong, A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US, 2023, 565, 00225193, 111468, 10.1016/j.jtbi.2023.111468
    16. Md. Mamun-Ur-Rashid Khan, Md. Rajib Arefin, Jun Tanimoto, Time delay of the appearance of a new strain can affect vaccination behavior and disease dynamics: An evolutionary explanation, 2023, 24680427, 10.1016/j.idm.2023.06.001
    17. Yucui Wu, Zhipeng Zhang, Limei Song, Chengyi Xia, Global stability analysis of two strains epidemic model with imperfect vaccination and immunity waning in a complex network, 2024, 179, 09600779, 114414, 10.1016/j.chaos.2023.114414
    18. 彦锦 吉, Studies with Vaccination and Asymptomatic Transmission Models, 2024, 14, 2160-7583, 424, 10.12677/pm.2024.145197
    19. Mohammadi Begum Jeelani, Rahim Ud Din, Ghaliah Alhamzi, Manel Hleili, Hussam Alrabaiah, Deterministic and Stochastic Nonlinear Model for Transmission Dynamics of COVID-19 with Vaccinations Following Bayesian-Type Procedure, 2024, 12, 2227-7390, 1662, 10.3390/math12111662
    20. Taqi A.M. Shatnawi, Stephane Y. Tchoumi, Herieth Rwezaura, Khalid Dib, Jean M. Tchuenche, Mo’tassem Al-arydah, A two-strain COVID-19 co-infection model with strain 1 vaccination, 2024, 26668181, 100945, 10.1016/j.padiff.2024.100945
    21. Riya Das, Dhiraj Kumar Das, T K Kar, Analysis of a chronological age-structured epidemic model with a pair of optimal treatment controls, 2024, 99, 0031-8949, 125240, 10.1088/1402-4896/ad8e0b
    22. Xi-Chao Duan, Chenyu Zhu, Xue-Zhi Li, Eric Numfor, Maia Martcheva, Dynamics and optimal control of an SIVR immuno-epidemiological model with standard incidence, 2025, 0022247X, 129449, 10.1016/j.jmaa.2025.129449
  • Reader Comments
  • © 2011 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3803) PDF downloads(715) Cited by(51)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog