Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/32773
Title: A Cellular Automata Model of Oncolytic Virotherapy in Pancreatic Cancer
Authors: Chen, J.
Weihs, D.
VERMOLEN, Fred 
Issue Date: 2020
Publisher: SPRINGER
Source: BULLETIN OF MATHEMATICAL BIOLOGY, 82 (8) (Art N° 103)
Abstract: Oncolytic virotherapy is known as a new treatment to employ less virulent viruses to specifically target and damage cancer cells. This work presents a cellular automata model of oncolytic virotherapy with an application to pancreatic cancer. The fundamental biomedical processes (like cell proliferation, mutation, apoptosis) are modeled by the use of probabilistic principles. The migration of injected viruses (as therapy) is modeled by diffusion through the tissue. The resulting diffusion-reaction equation with smoothed point viral sources is discretized by the finite difference method and integrated by the IMEX approach. Furthermore, Monte Carlo simulations are done to quantitatively evaluate the correlations between various input parameters and numerical results. As we expected, our model is able to simulate the pancreatic cancer growth at early stages, which is calibrated with experimental results. In addition, the model can be used to predict and evaluate the therapeutic effect of oncolytic virotherapy.
Notes: Chen, J (corresponding author), Amsterdam UMC, Dept Biomed Engn & Phys, Amsterdam, Netherlands.; Chen, J (corresponding author), Delft Univ Technol, Delft Inst Appl Math, Delft, Netherlands.
J.chen-6@tudelft.nl
Other: Chen, J (corresponding author), Amsterdam UMC, Dept Biomed Engn & Phys, Amsterdam, Netherlands ; Delft Univ Technol, Delft Inst Appl Math, Delft, Netherlands. J.chen-6@tudelft.nl
Keywords: Cellular automata;Computational modeling;Cancer treatment;Virotherapy;Monte Carlo simulations
Document URI: http://hdl.handle.net/1942/32773
ISSN: 0092-8240
e-ISSN: 1522-9602
DOI: 10.1007/s11538-020-00780-5
ISI #: WOS:000559331700001
Rights: The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2021
Appears in Collections:Research publications

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