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

  • Arazi A
  • Pendergraft W
  • Ribeiro R
 et al. 
  • 77


    Mendeley users who have this article in their library.
  • 15


    Citations of this article.


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. © 2012 Elsevier Ltd.

Author-supplied keywords

  • Autoimmunity
  • Dynamical modeling
  • Hypothesis-based modeling
  • Systems immunology
  • Unbiased data-driven approaches
  • Viral infections

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free