Pairing computation with experimentation: a powerful coupling for understanding T cell signalling

被引:45
作者
Chakraborty, Arup K. [1 ,2 ,3 ]
Das, Jayajit [4 ,5 ]
机构
[1] MIT, Dept Chem Engn, Cambridge, MA 02139 USA
[2] MIT, Dept Chem, Cambridge, MA 02139 USA
[3] MIT, Dept Biol Engn, Cambridge, MA 02139 USA
[4] Ohio State Univ, Battelle Ctr Math Med, Res Inst, Nationwide Childrens Hosp,Dept Pediat, Columbus, OH 43205 USA
[5] Ohio State Univ, Dept Phys, Columbus, OH 43205 USA
基金
美国国家卫生研究院;
关键词
RAS ACTIVATOR SON; IMMUNOLOGICAL SYNAPSE; STOCHASTIC SIMULATION; NEGATIVE FEEDBACK; SERIAL ENGAGEMENT; SEGREGATION MODEL; CENTRAL TOLERANCE; TYROSINE KINASE; RECEPTOR ZETA; NETWORK MODEL;
D O I
10.1038/nri2688
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
T cells are activated when extracellular stimuli, such as a ligand binding to the T cell receptor, are converted into functional outputs by the T cell signalling network. T cell receptor signalling is a highly complex, stochastic and dynamic process involving many interacting proteins. This complexity often confounds intuition, making it difficult to develop mechanistic principles that underly experimental observations. In this Review, we describe how computational approaches can partner successfully with biological experimentation to help address this challenge, and we illustrate this paradigm by summarizing recent work that shows new aspects of the T cell signalling network.
引用
收藏
页码:59 / 71
页数:13
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