Solving Immunology?

Yoram Vodovotz, Ashley Xia, Elizabeth L. Read, Josep Bassaganya-Riera, David A. Hafler, Eduardo Sontag, Jin Wang, John S. Tsang, Judy D. Day, Steven H. Kleinstein, Atul J. Butte, Matthew C. Altman, Ross Hammond, Stuart C. Sealfon*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations


Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop ‘Complex Systems Science, Modeling and Immunity’ and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Original languageEnglish
Pages (from-to)116-127
Number of pages12
JournalTrends in Immunology
Issue number2
StatePublished - 1 Feb 2017
Externally publishedYes


  • autoimmune disease
  • conference
  • mathematical modeling
  • personalized medicine
  • translation


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