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Mathematical Biosciences and Engineering

2013, Volume 10, Issue 3: 939-957. doi: 10.3934/mbe.2013.10.939
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Computational modeling approaches to studying the dynamics of oncolytic viruses

  • 1.
    Department of Ecology and Evolutionary Biology, University of California, 321 Steinhaus Hall, Irvine, CA 92617
  • Received: 01 July 2012 Accepted: 29 June 2018 Published: 01 April 2013

  • MSC : 92D25.

  • Oncolytic viruses specifically infect cancer cells, replicate in them, killthem, and spread to further tumor cells. They represent a targeted treatmentapproach that is promising in principle, but consistent success has yet tobe observed. Mathematical models can play an important role in analyzing thedynamics between oncolytic viruses and a growing tumor cell population,providing insights that can be useful for the further development of thistherapy approach. This article reviews different mathematical modelingapproaches ranging from ordinary differential equations to spatiallyexplicit agent-based models. Problems of model robustness are discussed andso are some clinically important insight derived from the models.

    Citation: Dominik Wodarz. Computational modeling approaches to studying the dynamics of oncolytic viruses[J]. Mathematical Biosciences and Engineering, 2013, 10(3): 939-957. doi: 10.3934/mbe.2013.10.939

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  • Oncolytic viruses specifically infect cancer cells, replicate in them, killthem, and spread to further tumor cells. They represent a targeted treatmentapproach that is promising in principle, but consistent success has yet tobe observed. Mathematical models can play an important role in analyzing thedynamics between oncolytic viruses and a growing tumor cell population,providing insights that can be useful for the further development of thistherapy approach. This article reviews different mathematical modelingapproaches ranging from ordinary differential equations to spatiallyexplicit agent-based models. Problems of model robustness are discussed andso are some clinically important insight derived from the models.


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  • © 2013 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)
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