Guerriero Maria Luisa, Heath John K
Centre for Systems Biology at Edinburgh, University of Edinburgh, Edinburgh, United Kingdom.
Methods Enzymol. 2011;487:217-51. doi: 10.1016/B978-0-12-381270-4.00008-1.
"In silico" experiments (i.e., computer simulation) constitute an aid to traditional biological research, by allowing biologists to execute efficient simulations taking into consideration the data obtained in wet experiments and to generate new hypotheses, which can be later verified in additional wet experiments. In addition to being much cheaper and faster than wet experiments, computer simulation has other advantages: it allows us to run experiments in which several species can be monitored at the same time, to explore quickly various conditions by varying species and parameters in different runs, and in some cases to observe the behavior of the system at a greater level of detail than the one permitted by experimental techniques. In the past few years there has been a considerable effort in the computer science community to develop computational languages and software tools for modeling and analysing biochemical systems. Among the challenges which must be addressed in this context, there are: the definition of languages powerful enough to express all the relevant features of biochemical systems, the development of efficient algorithms to analyze models and interpret the results, and the implementation of modeling platforms which are usable by nonprogrammers. In this chapter, we focus on the use of computational modeling to the analysis of biochemical systems. Computational modeling, in conjunction with the use of formal intuitive modeling languages, enables biologists to define models using a notation very similar to the informal descriptions they commonly use, but formal and, hence, automatically executable. We describe the main features of the existing textual computational languages and the tool support available for model development and analysis.
“计算机模拟”实验（即计算机仿真）通过让生物学家在考虑湿实验所获数据的情况下执行高效模拟并生成新假设，从而辅助传统生物学研究，这些新假设随后可在额外的湿实验中得到验证。除了比湿实验成本低得多且速度快之外，计算机模拟还有其他优势：它使我们能够进行可同时监测多个物种的实验，通过在不同运行中改变物种和参数来快速探索各种条件，并且在某些情况下能够比实验技术所允许的更详细地观察系统行为。在过去几年中，计算机科学界付出了相当大的努力来开发用于生化系统建模和分析的计算语言及软件工具。在此背景下必须解决的挑战包括：定义足够强大以表达生化系统所有相关特征的语言、开发用于分析模型和解释结果的高效算法，以及实现非程序员也能使用的建模平台。在本章中，我们重点关注计算建模在生化系统分析中的应用。计算建模与形式直观的建模语言相结合，使生物学家能够使用一种与他们常用的非正式描述非常相似的符号来定义模型，但这种符号是形式化的，因此可以自动执行。我们描述了现有文本计算语言的主要特征以及可用于模型开发和分析的工具支持。
