Hybrid modeling frameworks of tumor development and treatment
Hybrid modeling frameworks of tumor development and treatment
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DOI：
10.1002/wsbm.1461
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发表时间：
2019-07-17
影响因子：
7.9
通讯作者：
Rejniak, Katarzyna A.
中科院分区：
文献类型：
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作者：
Chamseddine, Ibrahim M.;Rejniak, Katarzyna A.
肿瘤是复杂的多细胞异质系统，由相互作用和相互修饰的组分组成。肿瘤的发展取决于多种因素：内在的，如基因突变，改变的信号通路，或可变的受体表达;和外在的，如营养供应的差异，与基质或免疫细胞的串扰，或周围细胞外基质的可变组成。肿瘤的特征还在于高细胞异质性和动态变化的肿瘤微环境。当这个多尺度、多组分系统受到抗癌治疗的干扰时，复杂性会增加。对如此复杂的系统进行建模并预测肿瘤将如何对治疗做出反应，需要能够处理各种类型信息并在多个时间和空间尺度上联合收割机结合多样化理论方法的数学模型，即混合模型。在这次更新中，我们讨论了在过去10年中在肿瘤混合建模领域取得的进展。混合模型的经典定义是指细胞的离散描述与微环境因素的连续描述的耦合。为了反映建模领域所采取的方向，我们建议扩展混合模型的定义，以包括耦合两个或多个不同的数学框架。因此，除了讨论离散/连续建模的最新进展外，我们还讨论了这两种数学描述如何与最优控制，优化，流体动力学，博弈论和机器学习的理论框架相结合。所有这些方法将说明与应用程序的肿瘤发展和各种抗癌治疗。这篇文章的特点是：分析和计算方法>计算方法转化，基因组学和系统医学>系统属性和过程的治疗方法模型>器官，组织和生理模型
Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments. This article is characterized under: Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Therapeutic Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models