Solving Immunology?

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• 作者：Yoram Vodovotz¹ ; Ashley Xia² ; ¹⁷ ; Elizabeth L. Read³ ; Josep Bassaganya-Riera⁴ ; David A. Hafler⁵ ; Eduardo Sontag⁶ ; Jin Wang⁷ ; ⁸ ; John S. Tsang⁹ ; Judy D. Day¹⁰ ; Steven H. Kleinstein¹¹ ; ¹² ; Atul J. Butte¹³ ; Matthew C. Altman¹⁴ ; Ross Hammond¹⁵ ; Stuart C. Sealfon¹⁶ ; ^{stuart.sealfon@mssm.edu}
• 关键词：mathematical modeling ; conference ; autoimmune disease ; personalized medicine ; translation
• 刊名：Trends in Immunology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：38
• 期：2
• 页码：116-127
• 全文大小：1843 K
• 卷排序：38

文摘

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.