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

被引：26

作者：

Arazi, Arnon ^{[2
]}

Pendergraft, William F., III ^{[1
,2
,3
,4
]}

Ribeiro, Ruy M. ^{[5
]}

Perelson, Alan S. ^{[5
]}

Hacohen, Nir ^{[1
,2
,3
]}

机构：

[1] Massachusetts Gen Hosp, Ctr Immunol & Inflammatory Dis, Charlestown, MA 02129 USA

[2] Broad Inst MIT & Harvard, Cambridge Ctr 7, Cambridge, MA 02142 USA

[3] Harvard Univ, Sch Med, Dept Med, Boston, MA 02115 USA

[4] Massachusetts Gen Hosp, Dept Med, Div Nephrol, Boston, MA 02114 USA

[5] Los Alamos Natl Lab, Theoret Biol & Biophys Grp, Los Alamos, NM 87545 USA

来源：

SEMINARS IN IMMUNOLOGY | 2013年 / 25卷 / 03期

关键词：

Systems immunology; Hypothesis-based modeling; Unbiased data-driven approaches; Dynamical modeling; Viral infections; Autoimmunity; DYNAMICS IN-VIVO; GENE-EXPRESSION PROFILES; C VIRUS-REPLICATION; T-CELL VACCINATION; INSULIN SENSITIVITY; LATENT RESERVOIR; HIV-1; INFECTION; IMMUNE-SYSTEM; MINIMAL MODEL; HEPATITIS-B;

D O I：

10.1016/j.smim.2012.11.003

中图分类号：

R392 [医学免疫学]; Q939.91 [免疫学];

学科分类号：

071005 [微生物学]; 100108 [医学免疫学];

摘要：

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. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：193 / 200

页数：8

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