IDEAS home Printed from https://ideas.repec.org/a/plo/ppat00/0030137.html
   My bibliography  Save this article

A Virtual Look at Epstein–Barr Virus Infection: Biological Interpretations

Author

Listed:
  • Karen A Duca
  • Michael Shapiro
  • Edgar Delgado-Eckert
  • Vey Hadinoto
  • Abdul S Jarrah
  • Reinhard Laubenbacher
  • Kichol Lee
  • Katherine Luzuriaga
  • Nicholas F Polys
  • David A Thorley-Lawson

Abstract

The possibility of using computer simulation and mathematical modeling to gain insight into biological and other complex systems is receiving increased attention. However, it is as yet unclear to what extent these techniques will provide useful biological insights or even what the best approach is. Epstein–Barr virus (EBV) provides a good candidate to address these issues. It persistently infects most humans and is associated with several important diseases. In addition, a detailed biological model has been developed that provides an intricate understanding of EBV infection in the naturally infected human host and accounts for most of the virus' diverse and peculiar properties. We have developed an agent-based computer model/simulation (PathSim, Pathogen Simulation) of this biological model. The simulation is performed on a virtual grid that represents the anatomy of the tonsils of the nasopharyngeal cavity (Waldeyer ring) and the peripheral circulation—the sites of EBV infection and persistence. The simulation is presented via a user friendly visual interface and reproduces quantitative and qualitative aspects of acute and persistent EBV infection. The simulation also had predictive power in validation experiments involving certain aspects of viral infection dynamics. Moreover, it allows us to identify switch points in the infection process that direct the disease course towards the end points of persistence, clearance, or death. Lastly, we were able to identify parameter sets that reproduced aspects of EBV-associated diseases. These investigations indicate that such simulations, combined with laboratory and clinical studies and animal models, will provide a powerful approach to investigating and controlling EBV infection, including the design of targeted anti-viral therapies.: The possibility of using computer simulation and mathematical modeling to gain insight into biological systems is receiving increased attention. However, it is as yet unclear to what extent these techniques will provide useful biological insights or even what the best approach is. Epstein–Barr virus (EBV) provides a good candidate to address these issues. It persistently infects most humans and is associated with several important diseases, including cancer. We have developed an agent-based computer model/simulation (PathSim, Pathogen Simulation) of EBV infection. The simulation is performed on a virtual grid that represents the anatomy where EBV infects and persists. The simulation is presented on a computer screen in a form that resembles a computer game. This makes it readily accessible to investigators who are not well versed in computer technology. The simulation allows us to identify switch points in the infection process that direct the disease course towards the end points of persistence, clearance, or death, and identify conditions that reproduce aspects of EBV-associated diseases. Such simulations, combined with laboratory and clinical studies and animal models, provide a powerful approach to investigating and controlling EBV infection, including the design of targeted anti-viral therapies.

Suggested Citation

  • Karen A Duca & Michael Shapiro & Edgar Delgado-Eckert & Vey Hadinoto & Abdul S Jarrah & Reinhard Laubenbacher & Kichol Lee & Katherine Luzuriaga & Nicholas F Polys & David A Thorley-Lawson, 2007. "A Virtual Look at Epstein–Barr Virus Infection: Biological Interpretations," PLOS Pathogens, Public Library of Science, vol. 3(10), pages 1-13, October.
  • Handle: RePEc:plo:ppat00:0030137
    DOI: 10.1371/journal.ppat.0030137
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.0030137
    Download Restriction: no

    File URL: https://journals.plos.org/plospathogens/article/file?id=10.1371/journal.ppat.0030137&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.ppat.0030137?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alan S. Perelson & Avidan U. Neumann & Martin Markowitz & John M. Leonard & David D. Ho, 1996. "HIV-1 Dynamics In Vivo: Virion Clearance Rate, Infected Cell Lifespan, and Viral Generation Time," Working Papers 96-02-004, Santa Fe Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jared B Hawkins & Edgar Delgado-Eckert & David A Thorley-Lawson & Michael Shapiro, 2013. "The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation," PLOS Pathogens, Public Library of Science, vol. 9(10), pages 1-16, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sutimin, & Wijaya, Karunia Putra & Páez Chávez, Joseph & Tian, Tianhai, 2021. "An in-host HIV-1 infection model incorporating quiescent and activated CD4+ T cells as well as CTL response," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    2. Iraj Hosseini & Feilim Mac Gabhann, 2012. "Multi-Scale Modeling of HIV Infection in vitro and APOBEC3G-Based Anti-Retroviral Therapy," PLOS Computational Biology, Public Library of Science, vol. 8(2), pages 1-17, February.
    3. E Fabian Cardozo & Adriana Andrade & John W Mellors & Daniel R Kuritzkes & Alan S Perelson & Ruy M Ribeiro, 2017. "Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration," PLOS Pathogens, Public Library of Science, vol. 13(7), pages 1-18, July.
    4. A. M. Elaiw & N. H. AlShamrani & E. Dahy & A. A. Abdellatif & Aeshah A. Raezah, 2023. "Effect of Macrophages and Latent Reservoirs on the Dynamics of HTLV-I and HIV-1 Coinfection," Mathematics, MDPI, vol. 11(3), pages 1-26, January.
    5. Yu Shi & Zizhao Zhang & Weng Kee Wong, 2019. "Particle swarm based algorithms for finding locally and Bayesian D-optimal designs," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-17, December.
    6. Wang, Jinliang & Guo, Min & Liu, Xianning & Zhao, Zhitao, 2016. "Threshold dynamics of HIV-1 virus model with cell-to-cell transmission, cell-mediated immune responses and distributed delay," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 149-161.
    7. Heffernan, J.M. & Keeling, M.J., 2008. "An in-host model of acute infection: Measles as a case study," Theoretical Population Biology, Elsevier, vol. 73(1), pages 134-147.
    8. Singh, Harendra, 2021. "Analysis of drug treatment of the fractional HIV infection model of CD4+ T-cells," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    9. Xu, Jinhu & Geng, Yan & Zhou, Yicang, 2017. "Global dynamics for an age-structured HIV virus infection model with cellular infection and antiretroviral therapy," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 62-83.
    10. Jianwei Chen, 2010. "Modelling long‐term human immunodeficiency virus dynamic models with application to acquired immune deficiency syndrome clinical study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 805-820, November.
    11. Musharif Ahmed & Muhammad Aamer Saleem & Muhammad Zubair & Ijaz Mansoor Qureshi & Saad Zafar, 2022. "Stability analysis and memetic computation using differential evolution for in-host HIV model," Indian Journal of Pure and Applied Mathematics, Springer, vol. 53(1), pages 76-91, March.
    12. Dubey, Preeti & Dubey, Uma S. & Dubey, Balram, 2018. "Modeling the role of acquired immune response and antiretroviral therapy in the dynamics of HIV infection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 144(C), pages 120-137.
    13. Yang, Junyuan & Wang, Xiaoyan, 2019. "Dynamics and asymptotical profiles of an age-structured viral infection model with spatial diffusion," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 236-254.
    14. Jessica M Conway & Alan S Perelson & Jonathan Z Li, 2019. "Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-26, July.
    15. Chen, Wei & Zhang, Long & Wang, Ning & Teng, Zhidong, 2024. "Bifurcation analysis and chaos for a double-strains HIV coinfection model with intracellular delays, saturated incidence and Logistic growth," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 617-641.
    16. Guo, Wenjuan & Zhang, Qimin, 2021. "Explicit numerical approximation for an impulsive stochastic age-structured HIV infection model with Markovian switching," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 86-115.
    17. González, Ramón E.R. & Coutinho, Sérgio & Zorzenon dos Santos, Rita Maria & de Figueirêdo, Pedro Hugo, 2013. "Dynamics of the HIV infection under antiretroviral therapy: A cellular automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4701-4716.
    18. Bernhard Konrad & Naveen Vaidya & Robert Smith?, 2011. "Modelling Mutation to a Cytotoxic T-lymphocyte HIV Vaccine," Mathematical Population Studies, Taylor & Francis Journals, vol. 18(2), pages 122-149.
    19. Liu, Baisen & Wang, Liangliang & Nie, Yunlong & Cao, Jiguo, 2019. "Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 233-246.
    20. Ulrich D Kadolsky & Becca Asquith, 2010. "Quantifying the Impact of Human Immunodeficiency Virus-1 Escape From Cytotoxic T-Lymphocytes," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-11, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:ppat00:0030137. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plospathogens (email available below). General contact details of provider: https://journals.plos.org/plospathogens .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.