Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Edgewise Networks, Burlington, MA, USA.
Bioinformatics. 2018 Jul 1;34(13):i583-i592. doi: 10.1093/bioinformatics/bty272.
We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction.
We discuss details about the computation and rendering of static, dynamic, and causal views of a model, which include the contact map (CM), snaphots at different resolutions, the dynamic influence network (DIN) and causal compression. We provide use cases illustrating how these concepts generate insight. Specifically, we show how the CM and snapshots provide information about systems capable of polymerization, such as Wnt signaling. A well-understood model of the KaiABC oscillator, translated into Kappa from the literature, is deployed to demonstrate the DIN and its use in understanding systems dynamics. Finally, we discuss how pathways might be discovered or recovered from a rule-based model by means of causal compression, as exemplified for early events in EGF signaling.
The Kappa platform is available via the project website at kappalanguage.org. All components of the platform are open source and freely available through the authors' code repositories.
我们介绍了 Kappa 平台的概述,这是一个集成的分析和可视化技术套件,用于构建和交互式探索基于规则的模型。该平台的主要组件包括 Kappa 模拟器、Kappa 静态分析器和 Kappa 故事提取器。除了这些组件,我们还描述了 Kappa 用户界面,其中包括一系列用于基于规则模型的交互式可视化工具,这些工具对于理解生物系统的复杂性至关重要。我们认为,在这种方法中,建模类似于编程,同样可以从集成开发环境中受益。我们的平台就是朝着这个方向迈出的一步。
我们讨论了模型的静态、动态和因果视图的计算和渲染细节,包括接触图 (CM)、不同分辨率的快照、动态影响网络 (DIN) 和因果压缩。我们提供了用例来说明这些概念如何产生洞察力。具体来说,我们展示了 CM 和快照如何提供有关聚合能力系统的信息,例如 Wnt 信号传导。将文献中从文献中翻译为 Kappa 的 KaiABC 振荡器的一个很好理解的模型部署来演示 DIN 及其在理解系统动力学方面的用途。最后,我们讨论了如何通过因果压缩从基于规则的模型中发现或恢复途径,例如 EGF 信号传导的早期事件。
Kappa 平台可通过项目网站 kappalanguage.org 获得。该平台的所有组件都是开源的,并通过作者的代码存储库免费提供。