tSystems immunology is an emerging paradigm that aims at a more systematic and quantitative under-standing of the immune system. Two major approaches have been utilized to date in this field: unbiaseddata-driven modeling to comprehensively identify molecular and cellular components of a system andtheir interactions; and hypothesis-based quantitative modeling to understand the operating principlesof a system by extracting a minimal set of variables and rules underlying them. In this review, wedescribe applications of the two approaches to the study of viral infections and autoimmune diseasesin humans, and discuss possible ways by which these two approaches can synergize when applied tohuman immunology.©