Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
J Cell Sci. 2013 May 1;126(Pt 9):1913-21. doi: 10.1242/jcs.112045.
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.
许多生物学家认为细胞信号的计算模型过于复杂。既然可以直接做另一个实验，为什么还要用数学呢？在这里，我们解释了数学公式化的概念模型如何提供了直接推动实验细胞生物学发展的见解。在过去的几年中，模型已经影响了我们讨论信号网络的方式、我们监测它们的方式以及在对其进行干扰时得出的结论。这些见解需要湿实验室实验，但如果没有明确的计算建模和定量分析，就不会出现这些见解。如今，最好的建模者是具有交叉训练背景的实验生物学调查人员，他们与合作者密切合作，但也在自己的实验室中开展实验工作。为了确定计算模型何时可以成为实验的有用补充，生物学家将从核心建模原理中受益。尽管模型的数学基础对于了解其优缺点很有用，但在计算上测试或产生有价值的生物学假设并不需要它们。
