Models of signalling networks - what cell biologists can gain from them and give to them
Models of signalling networks - what cell biologists can gain from them and give to them
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DOI：
10.1242/jcs.112045
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发表时间：
2013-05-01
影响因子：
4
通讯作者：
Lauffenburger, Douglas A.
中科院分区：
文献类型：
--
作者：
Janes, Kevin A.;Lauffenburger, Douglas A.
许多生物学家认为细胞信号传导的计算模型过于复杂。当你可以简单地做另一个实验时，为什么要做数学呢？在这里，我们解释了概念模型，这已经制定数学，提供了直接推进实验细胞生物学的见解。在过去的几年里，模型已经影响了我们谈论信号网络的方式，我们如何监控它们，以及当我们扰动它们时我们得出的结论。这些见解需要湿实验室实验，但如果没有明确的计算模型和定量分析，就不会出现。今天，最好的建模者是在实验生物学方面经过交叉训练的研究人员，他们与合作者密切合作，但也在自己的实验室进行实验工作。生物学家将受益于熟悉建模的核心原则，以确定何时计算模型可以成为他们实验的有用补充。虽然模型的数学基础对于理解其优点和缺点是有用的，但它们并不需要通过计算来测试或生成有价值的生物学假设。
Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.