使用rxncon开发基于规则的模型
Using rxncon to develop rule based models
摘要：
我们提出了一个协议，用于建立，验证和模拟模型的信号转导网络。由于组合的复杂性和稀疏的数据，这些网络是具有挑战性的建模目标，这使得它成为一个重大的挑战，甚至形式化当前的知识。为了解决这个问题，社区已经开发了基于站点动态的生物分子反应网络建模方法。这种方法的优点是反应和状态可以在可变分辨率下定义，这使得可以根据经验数据调整模型分辨率。这提高了可扩展性和准确性，使其有可能正式的信号转导网络的大型模型。在这里，我们提出了一种构建和验证信号传导网络大型模型的方法。工作流基于rxncon，反应-应急语言。在一个五步的过程中，我们创建一个机械网络模型，将其转换为可执行的布尔模型，使用布尔模型来评估和改进网络，最后将rxncon模型导出为基于规则的格式。我们提供了对rxncon语言的介绍和一个带注释的、逐步的工作流程协议。最后，我们创建了一个胰岛素信号通路的小模型来说明该协议，以及一些挑战-以及它们的一些解决方案-在建模信号转导。
英文摘要：
We present a protocol for building, validating and simulating models of signal transduction networks. These networks are challenging modelling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalise the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics. The strength of this approach is that reactions and states can be defined at variable resolution, which makes it possible to adapt the model resolution to the empirical data. This improves both scalability and accuracy, making it possible to formalise large models of signal transduction networks. Here, we present a method to build and validate large models of signal transduction networks. The workflow is based on rxncon, the reaction-contingency language. In a five-step process, we create a mechanistic network model, convert it into an executable Boolean model, use the Boolean model to evaluate and improve the network, and finally export the rxncon model into a rule based format. We provide an introduction to the rxncon language and an annotated, step-by-step protocol for the workflow. Finally, we create a small model of the insulin signalling pathway to illustrate the protocol, together with some of the challenges - and some of their solutions - in modelling signal transduction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
