High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
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DOI：
10.1186/s12859-018-2510-x
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发表时间：
2018-12-21
影响因子：
3
通讯作者：
Macklin, Paul
中科院分区：
文献类型：
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作者：
Ozik, Jonathan;Collier, Nicholson;Macklin, Paul
癌症是一个复杂的、多尺度的动力系统，肿瘤细胞与非癌性宿主系统之间存在相互作用。治疗作用于这个联合的癌症-宿主系统，有时会产生意想不到的结果。对机械计算模型的系统研究可以增强传统的实验室和临床研究，帮助确定驱动治疗成功或失败的因素。然而，考虑到潜在生物学的不确定性，这些多尺度计算模型除了包含高维参数空间外，还可以采取许多潜在的形式。因此，对这些模型的探索在计算上具有挑战性。我们建议整合两种现有技术，一种用于帮助构建基于多尺度智能体的模型，另一种用于增强模型探索和优化，可以为高通量假设检验和最终优化提供计算手段。结果在本文中，我们引入了一个高通量计算（HTC）框架，该框架集成了机械三维多细胞模拟器（PhysiCell）和极端尺度模型探索平台（EMEWS）来研究高维参数空间。我们展示了将PhysiCell-EMEWS应用于3-D癌症免疫治疗的早期结果，并展示了治疗失败的见解。我们描述了一个用于高通量癌症假设检验的广义PhysiCell-EMEWS工作流程，其中将数百或数千个机制模拟与数据驱动的误差指标进行比较，以执行假设优化。结论尽管仍存在关键的符号和计算挑战，但基于机械主体的模型和高通量模型探索环境可以结合起来系统、快速地探索癌症的关键问题。这些高通量的计算实验可以提高我们对潜在生物学的理解，推动未来的实验，并最终为临床实践提供信息。
BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologiesone to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimizationcan provide a computational means for high-throughput hypothesis testing, and eventually, optimization.ResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.ConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.