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Methods Mol Biol. 2020;2058:307-320. doi: 10.1007/978-1-4939-9794-7_21.

Mathematical Modeling of Oncolytic Virotherapy.

Author information

1
Research Group Mechanisms of Oncolytic Immunotherapy, Clinical Cooperation Unit Virotherapy, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), University Hospital Heidelberg, Heidelberg, Germany.
2
Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
3
Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
4
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA. heiko.enderling@moffitt.org.

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.

KEYWORDS:

Combination immunotherapy; Mathematical modeling; Oncology; Oncolytic virotherapy; Virus

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