Sekar John A P, Faeder James R
Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Methods Mol Biol. 2012;880:139-218. doi: 10.1007/978-1-61779-833-7_9.
Biological cells accomplish their physiological functions using interconnected networks of genes, proteins, and other biomolecules. Most interactions in biological signaling networks, such as bimolecular association or covalent modification, can be modeled in a physically realistic manner using elementary reaction kinetics. However, the size and combinatorial complexity of such reaction networks have hindered such a mechanistic approach, leading many to conclude that it is premature and to adopt alternative statistical or phenomenological approaches. The recent development of rule-based modeling languages, such as BioNetGen (BNG) and Kappa, enables the precise and succinct encoding of large reaction networks. Coupled with complementary advances in simulation methods, these languages circumvent the combinatorial barrier and allow mechanistic modeling on a much larger scale than previously possible. These languages are also intuitive to the biologist and accessible to the novice modeler. In this chapter, we provide a self-contained tutorial on modeling signal transduction networks using the BNG Language and related software tools. We review the basic syntax of the language and show how biochemical knowledge can be articulated using reaction rules, which can be used to capture a broad range of biochemical and biophysical phenomena in a concise and modular way. A model of ligand-activated receptor dimerization is examined, with a detailed treatment of each step of the modeling process. Sections discussing modeling theory, implicit and explicit model assumptions, and model parameterization are included, with special focus on retaining biophysical realism and avoiding common pitfalls. We also discuss the more advanced case of compartmental modeling using the compartmental extension to BioNetGen. In addition, we provide a comprehensive set of example reaction rules that cover the various aspects of signal transduction, from signaling at the membrane to gene regulation. The reader can modify these reaction rules to model their own systems of interest.
生物细胞利用基因、蛋白质和其他生物分子相互连接的网络来完成其生理功能。生物信号网络中的大多数相互作用，如双分子缔合或共价修饰，都可以使用基本反应动力学以物理上现实的方式进行建模。然而，这种反应网络的规模和组合复杂性阻碍了这种机械方法的应用，导致许多人认为现在采用这种方法还为时过早，转而采用替代的统计或现象学方法。基于规则的建模语言，如BioNetGen（BNG）和Kappa的最新发展，使得能够精确而简洁地编码大型反应网络。再加上模拟方法的互补进展，这些语言规避了组合障碍，并允许在比以前更大的规模上进行机械建模。这些语言对生物学家来说也很直观，新手建模者也可以使用。在本章中，我们提供了一个关于使用BNG语言和相关软件工具对信号转导网络进行建模的独立教程。我们回顾了该语言的基本语法，并展示了如何使用反应规则来阐述生化知识，这些反应规则可用于以简洁和模块化的方式捕捉广泛的生化和生物物理现象。我们研究了配体激活受体二聚化的模型，并对建模过程的每个步骤进行了详细处理。还包括讨论建模理论、隐式和显式模型假设以及模型参数化的章节，特别关注保持生物物理真实性并避免常见陷阱。我们还讨论了使用BioNetGen的隔室扩展进行隔室建模的更高级情况。此外，我们提供了一套全面的示例反应规则，涵盖了信号转导的各个方面，从膜信号传导到基因调控。读者可以修改这些反应规则来对自己感兴趣的系统进行建模。
