Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, USA.
Methods. 2013 Jul 15;62(1):3-12. doi: 10.1016/j.ymeth.2012.10.012. Epub 2012 Nov 9.
Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.
鉴于生物系统的复杂性和交互性，构建这些系统的信息丰富且连贯的网络模型，并随后开发有效的方法来分析组装的网络，这一点非常重要。网络分析和动态建模的结合使人们能够研究整个基础系统的行为，并对所涉及过程中了解较少的方面做出可进行实验验证的预测。在本文中，我们介绍了生物调节网络的布尔建模的基本步骤的教程。我们展示了如何从可用的实验数据中推断出布尔网络模型，如何使用图论度量来分析网络，并将其转换为可预测的动态模型。对于每个步骤，我们还讨论了可能遇到的陷阱以及可能的规避方法。我们在一个玩具网络以及果蝇体节极性基因网络的上下文中说明了这些步骤。
