Computational Biology and Bioinformatics
Volume 8, Issue 1, June 2020, Pages: 20-28
Received: Jun. 10, 2020;
Accepted: Jun. 23, 2020;
Published: Jul. 4, 2020
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Dongwook Kim, Department of Mathematics, Texas A&M University-Kingsville, Kingsville, United States of America
Haeyoung Kim, Department of Biological and Health Sciences, Texas A&M University-Kingsville, Kingsville, United States of America
Hui Wu, Department of Mathematics, Clark Atlanta University, Atlanta, United States of America
Dong-Hoon Shin, Department of Global Finance and Banking, Inha University, Incheon, South Korea
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.
The Effect of the Infection Rate on Oncolytic Virotherapy, Computational Biology and Bioinformatics.
Vol. 8, No. 1,
2020, pp. 20-28.
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