Demystifying the cytokine network: Mathematical models point the way.
Demystifying the cytokine network: Mathematical models point the way.
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DOI：
10.1016/j.cyto.2016.11.013
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发表时间：
2017-10
期刊：
影响因子：
3.8
通讯作者：
Faeder JR
中科院分区：
文献类型：
--
作者：
Morel PA;Lee REC;Faeder JR
细胞因子提供了免疫细胞相互之间以及与实质细胞之间交流的途径。有超过一百种细胞因子，其中许多存在于共享受体成分和信号转导途径的家族中，形成了复杂的网络。通过使用基因靶向小鼠，理解特定细胞因子作用的还原主义方法揭示了细胞因子功能中冗余和多效性的进一步复杂性。在实验中，对细胞因子与其靶细胞之间复杂的相互作用的理解是具有挑战性的。数学和计算建模提供了一套强大的工具，通过这些工具可以研究和分析细胞因子之间的复杂相互作用，在此过程中产生新的见解，可以通过实验进一步测试。这篇综述将讨论并提供不同的建模方法的例子，这些方法已被用来增加我们对细胞因子网络的理解。这包括讨论基于知识和数据驱动的建模方法和单细胞分析的最新进展。使用模型来优化基于细胞因子的疗法也将被讨论。
Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.