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基于规则模型的自动化可视化

Automated visualization of rule-based models.

作者信息

Sekar John Arul Prakash, Tapia Jose-Juan, Faeder James R

机构信息

Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.

出版信息

PLoS Comput Biol. 2017 Nov 13;13(11):e1005857. doi: 10.1371/journal.pcbi.1005857. eCollection 2017 Nov.

Abstract

Frameworks such as BioNetGen, Kappa and Simmune use "reaction rules" to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models.

摘要

诸如BioNetGen、Kappa和Simmune等框架使用“反应规则”来简洁地指定生化相互作用,其中每个规则指定一种机制,如结合或磷酸化及其结构要求。当前基于规则的信号通路模型有数十到数百条规则,并且随着添加更多的分子类型和通路,这些数字预计还会增加。可视化表示对于传达基于规则的模型至关重要,但当前展示规则以及规则之间相互作用的方法在随着模型规模增大时扩展性不佳。此外,推断生化相互作用中出现的设计基序是一个未解决的问题,因此当前可视化模型架构的方法依赖于对模型的人工解读。在此,我们展示了三种新的可视化工具,它们构成了一个用于基于规则模型的自动化可视化框架:(i)一种紧凑的规则可视化,可高效显示每条规则;(ii)原子 - 规则图,它将模型中的调控相互作用作为二分网络进行传达;(iii)一个可调节的压缩管道,该管道纳入专家知识,并在应用于原子 - 规则图时生成模型架构的紧凑图表。如我们通过应用于具体示例所示,压缩后的图表传达了对理解小型和大型基于规则模型都有用的网络基序和架构特征。我们的工具还比当前方法生成的图表更具可读性,如我们通过使用标准图形指标比较27个已发表模型的可视化结果所示。我们在开源且免费可用的BioNetGen框架中提供了实现,但底层方法具有通用性,也可应用于来自Kappa和Simmune框架的基于规则模型。我们期望这些工具将促进基于规则模型的交流与分析,并最终将其整合到全面的全细胞模型中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61e0/5703574/491ca38dac0f/pcbi.1005857.g001.jpg

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