Computational Biology and Bioinformatics

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The Effect of the Infection Rate on Oncolytic Virotherapy

Received: 10 June 2020    Accepted: 23 June 2020    Published: 04 July 2020
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Abstract

Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy.

DOI 10.11648/j.cbb.20200801.14
Published in Computational Biology and Bioinformatics (Volume 8, Issue 1, June 2020)
Page(s) 20-28
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Computational Biology, Oncolytic Virotherapy, Bifurcation, Dynamical System

References
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[2] Diaconu, I., Cerullo, V., Hirvinen, M. L., Escutenaire, S., Ugolini, M., Pesonen, S. K., Bramante, S., Parviainen, S., Kanerva, A., Loskog, A. S. et al., Immune response is an important aspect of the antitumor effect produced by a CD40L-encoding oncolytic adenovirus. Cancer Res. 2012, 72, 2327–2338.
[3] Ito, H., Aoki, H., Kuhnel, F., Kondo, Y., Kubicka, S., Wirth, T., Iwado, E., Iwamaru, A., Fujiwara, K., Hess, K. R. et al., Autophagic cell death of malignant glioma cells induced by a conditionally replicating adenovirus. J. Natl. Cancer Inst. 2006, 98, 625–636.
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Author Information
  • Department of Mathematics, Texas A&M University-Kingsville, Kingsville, United States of America

  • Department of Biological and Health Sciences, Texas A&M University-Kingsville, Kingsville, United States of America

  • Department of Mathematics, Clark Atlanta University, Atlanta, United States of America

  • Department of Global Finance and Banking, Inha University, Incheon, South Korea

Cite This Article
  • APA Style

    Dongwook Kim, Haeyoung Kim, Hui Wu, Dong-Hoon Shin. (2020). The Effect of the Infection Rate on Oncolytic Virotherapy. Computational Biology and Bioinformatics, 8(1), 20-28. https://doi.org/10.11648/j.cbb.20200801.14

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    ACS Style

    Dongwook Kim; Haeyoung Kim; Hui Wu; Dong-Hoon Shin. The Effect of the Infection Rate on Oncolytic Virotherapy. Comput. Biol. Bioinform. 2020, 8(1), 20-28. doi: 10.11648/j.cbb.20200801.14

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    AMA Style

    Dongwook Kim, Haeyoung Kim, Hui Wu, Dong-Hoon Shin. The Effect of the Infection Rate on Oncolytic Virotherapy. Comput Biol Bioinform. 2020;8(1):20-28. doi: 10.11648/j.cbb.20200801.14

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  • @article{10.11648/j.cbb.20200801.14,
      author = {Dongwook Kim and Haeyoung Kim and Hui Wu and Dong-Hoon Shin},
      title = {The Effect of the Infection Rate on Oncolytic Virotherapy},
      journal = {Computational Biology and Bioinformatics},
      volume = {8},
      number = {1},
      pages = {20-28},
      doi = {10.11648/j.cbb.20200801.14},
      url = {https://doi.org/10.11648/j.cbb.20200801.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.cbb.20200801.14},
      abstract = {Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy.},
     year = {2020}
    }
    

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    T1  - The Effect of the Infection Rate on Oncolytic Virotherapy
    AU  - Dongwook Kim
    AU  - Haeyoung Kim
    AU  - Hui Wu
    AU  - Dong-Hoon Shin
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    T2  - Computational Biology and Bioinformatics
    JF  - Computational Biology and Bioinformatics
    JO  - Computational Biology and Bioinformatics
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    PB  - Science Publishing Group
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    AB  - Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy.
    VL  - 8
    IS  - 1
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