Human systems immunology: Hypothesis-based modeling and unbiased data-driven approaches

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.

► Human systems immunology is an emerging approach for the study of immune disorders. ► Two major approaches used are unbiased data-driven and hypothesis-based modeling. ► Unbiased data-driven approaches are useful in identifying components and their interactions. ► Hypothesis-based modeling derives rules that govern the behavior of a system. ► Synergistic interplay of both approaches will be essential for the development of systems immunology.