Mathematics in modern immunology.
Mathematics in modern immunology.
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DOI：
10.1098/rsfs.2015.0093
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发表时间：
2016-04-06
期刊：
影响因子：
4.4
通讯作者：
Ribeiro RM
中科院分区：
文献类型：
--
作者：
Castro M;Lythe G;Molina-París C;Ribeiro RM
数学和统计方法使催化发现的多学科方法成为可能。与实验方法一起，它们确定关键假设，定义可测量的可观察值并协调不同的结果。我们收集了T细胞生物学研究的代表性样本，这些样本说明了建模-实验合作的好处，并且已被证明是有价值的，甚至是开创性的。我们的结论是，有可能找到数学建模和免疫学实验之间协同作用的优秀例子，这些例子带来了重要的见解，如果没有这些合作，就无法获得，但还有很多东西有待发现。
Mathematical and statistical methods enable multidisciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. We collect a representative sample of studies in T-cell biology that illustrate the benefits of modelling–experimental collaborations and that have proven valuable or even groundbreaking. We conclude that it is possible to find excellent examples of synergy between mathematical modelling and experiment in immunology, which have brought significant insight that would not be available without these collaborations, but that much remains to be discovered.