Oncolytic Viruses pp 307-320 | Cite as
Mathematical Modeling of Oncolytic Virotherapy
Abstract
Mathematical modeling in biology has a long history as it allows the analysis and simulation of complex dynamic biological systems at little cost. A mathematical model trained on experimental or clinical data can be used to generate and evaluate hypotheses, to ask “what if” questions, and to perform in silico experiments to guide future experimentation and validation. Such models may help identify and provide insights into the mechanisms that drive changes in dynamic systems. While a mathematical model may never replace actual experiments, it can synergize with experiments to save time and resources by identifying experimental conditions that are unlikely to yield favorable outcomes, and by using optimization principles to identify experiments that are most likely to be successful. Over the past decade, numerous models have also been developed for oncolytic virotherapy, ranging from merely theoretic frameworks to fully integrated studies that utilize experimental data to generate actionable hypotheses. Here we describe how to develop such models for specific oncolytic virotherapy experimental setups, and which questions can and cannot be answered using integrated mathematical oncology.
Key words
Mathematical modeling Oncology Virus Oncolytic virotherapy Combination immunotherapyNotes
Acknowledgments
J.P.W.H. receives a PhD stipend by the Helmholtz International Graduate School for Cancer Research at the German Cancer Research Center (DKFZ). C.E.E. receives funding from the Wilhelm Sander-Stiftung (Grant 2018.058.1). H.E. and D.A.D. thank Dr. Alexander R. A. Anderson and the Moffitt Physical Sciences in Oncology Center for the organization and support of the fourth Moffitt IMO workshop on Viruses and Cancer, where parts of the idea of this project were conceived.
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