Heidbuechel Johannes P W, Abate-Daga Daniel, Engeland Christine E, Enderling Heiko
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.
Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
Methods Mol Biol. 2020;2058:307-320. doi: 10.1007/978-1-4939-9794-7_21.
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.
生物学中的数学建模有着悠久的历史，因为它能够以低成本对复杂的动态生物系统进行分析和模拟。基于实验或临床数据训练的数学模型可用于生成和评估假设、提出“如果……会怎样”的问题，以及进行计算机模拟实验以指导未来的实验和验证。此类模型可能有助于识别驱动动态系统变化的机制并深入了解这些机制。虽然数学模型永远无法取代实际实验，但它可以与实验协同作用，通过识别不太可能产生有利结果的实验条件，并利用优化原则识别最有可能成功的实验，从而节省时间和资源。在过去十年中，也已经开发出了许多用于溶瘤病毒疗法的模型，从仅仅是理论框架到利用实验数据生成可操作假设的完全整合研究。在这里，我们描述如何针对特定的溶瘤病毒疗法实验设置开发此类模型，以及使用整合的数学肿瘤学可以回答和无法回答哪些问题。
