Bewick Sharon, Yang Ruoting, Zhang Mingjun
Department of Mechanical, Aerospace and Biomedical Engineering, The University of Tennessee, Knoxville, TN, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6026-9. doi: 10.1109/IEMBS.2009.5333520.
In this paper, we show how evolutionary game theory can be embedded into a traditional optimal control framework in order to predict strategies for time-dependent drug dosages in the context of a growing pathogen population that exhibits the capacity to evolve in direct response to the level of applied drug. To illustrate our method for integrating evolutionary games with optimal control systems, we consider a simplified model that describes a generic trade-off between viral replication rate and drug resistance. The technique that we outline, however, is readily extendable to more complicated models that account, in more detail, for the specific biology of a particular pathogen of interest.
在本文中,我们展示了如何将进化博弈论嵌入传统的最优控制框架,以便在病原体种群不断增长且能根据所施用药物水平直接进化的背景下,预测随时间变化的药物剂量策略。为了说明我们将进化博弈与最优控制系统相结合的方法,我们考虑一个简化模型,该模型描述了病毒复制率与耐药性之间的一般权衡。然而,我们概述的技术很容易扩展到更复杂的模型,这些模型更详细地考虑了特定感兴趣病原体的具体生物学特性。