Metzcar John, Wang Yafei, Heiland Randy, Macklin Paul
Indiana University, Bloomington, IN.
JCO Clin Cancer Inform. 2019 Feb;3:1-13. doi: 10.1200/CCI.18.00069.
Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer's ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.
癌症生物学涉及癌细胞与其组织微环境之间复杂、动态的相互作用。单细胞效应是临床进展的关键驱动因素。肿瘤细胞与基质细胞之间的化学和机械通讯可利用正常生理过程来促进生长和侵袭。癌细胞的异质性增强了癌症测试适应微环境应激策略的能力。缺氧和治疗可筛选出癌症干细胞并驱动侵袭和耐药性。基于细胞的计算模型(也称为离散模型、基于主体的模型或基于个体的模型)模拟单个细胞在虚拟组织中的相互作用,这使我们能够探索单细胞行为如何导致我们在癌症系统中观察到并试图控制的动态变化。在本综述中,我们介绍了可用于基于细胞的计算建模的广泛技术。这些方法的范围可以从仅几个细胞及其形态的高度详细模型到三维组织中的数百万个较简单细胞。对单个细胞进行建模使我们能够将生物学观察结果直接转化为模拟规则。在许多情况下,单个细胞主体包括分子尺度模型。大多数模型还模拟氧气、药物和生长因子的运输,这使我们能够将癌症发展与微环境条件联系起来。我们通过从癌症缺氧、血管生成、侵袭、干细胞和免疫监视中选取的例子来说明这些方法。在云计算资源使软件更易于访问且超级计算资源使大规模模拟研究成为可能的时代,一个可互操作的基于细胞的模拟工具生态系统正在兴起。随着该领域的发展,我们预计高通量模拟研究将使我们能够快速探索生物学可能性的空间,预先筛选新的治疗策略,甚至对肿瘤细胞和基质细胞进行重新设计以控制癌症系统。