Ozik Jonathan, Collier Nicholson, Heiland Randy, An Gary, Macklin Paul
Decision and Infrastructure Sciences , Argonne National Laboratory , 9700 S. Cass Ave , Lemont , IL 60439 , USA . Email:
Consortium for Advanced Science and Engineering , University of Chicago , 5801 S. Ellis Ave. , Chicago , IL 60637 , USA.
Mol Syst Des Eng. 2019 Aug 1;4(4):747-760. doi: 10.1039/c9me00036d. Epub 2019 Jun 7.
We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.
我们提出了一个集成框架，用于在高性能计算资源上利用详细的动态模拟模型对癌症免疫疗法的设计空间进行动态探索。我们的框架将PhysiCell（一个用于癌症和其他多细胞系统的基于代理的开源模拟平台）和EMEWS（一个用于超大规模模型探索的开源平台）结合在一起。我们构建了一个针对异质性肿瘤的免疫监视基于代理的模型，其中包括随机肿瘤 - 免疫接触相互作用的空间动态。我们使用高性能计算工作流程实现主动学习和遗传算法，以自适应地对模型参数空间进行采样，并在生物学和临床约束内迭代发现最佳癌症消退区域。
