Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.
Semin Immunol. 2013 Oct 31;25(3):193-200. doi: 10.1016/j.smim.2012.11.003. Epub 2013 Jan 29.
Systems immunology is an emerging paradigm that aims at a more systematic and quantitative understanding of the immune system. Two major approaches have been utilized to date in this field: unbiased data-driven modeling to comprehensively identify molecular and cellular components of a system and their interactions; and hypothesis-based quantitative modeling to understand the operating principles of a system by extracting a minimal set of variables and rules underlying them. In this review, we describe applications of the two approaches to the study of viral infections and autoimmune diseases in humans, and discuss possible ways by which these two approaches can synergize when applied to human immunology.
系统免疫学是一种新兴的范式，旨在更系统和定量地理解免疫系统。迄今为止，该领域采用了两种主要方法：无偏数据驱动建模，以全面识别系统的分子和细胞成分及其相互作用；基于假设的定量建模，通过提取系统背后的最小变量集和规则来理解系统的工作原理。在这篇综述中，我们描述了这两种方法在人类病毒感染和自身免疫性疾病研究中的应用，并讨论了当应用于人类免疫学时，这两种方法可以协同作用的可能途径。
