生物信号网络Petri网模型转化为影响图的一种算法。

IF 2 4区生物学 Q2 BIOLOGY

Biosystems Pub Date : 2025-02-20 DOI:10.1016/j.biosystems.2025.105415

Simon Gamache-Poirier , Alexia Souvane , William Leclerc , Catherine Villeneuve , Simon V. Hardy

{"title":"生物信号网络Petri网模型转化为影响图的一种算法。","authors":"Simon Gamache-Poirier , Alexia Souvane , William Leclerc , Catherine Villeneuve , Simon V. Hardy","doi":"10.1016/j.biosystems.2025.105415","DOIUrl":null,"url":null,"abstract":"<div><div>A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism for reaction-based models is the Petri nets which has a graphical representation and a mathematical notation that enables structural analysis and quantitative simulation. In this paper, we present an algorithm based on Petri nets topological features for the transformation of the computational model of a biological signaling network into an annotated influence graph. We also show the transformation of the Petri nets model of the beta-adrenergic receptor activating the PKA-MAPK signaling network into its representation as an influence graph.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"250 ","pages":"Article 105415"},"PeriodicalIF":2.0000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An algorithm for the transformation of the Petri net models of biological signaling networks into influence graphs\",\"authors\":\"Simon Gamache-Poirier , Alexia Souvane , William Leclerc , Catherine Villeneuve , Simon V. Hardy\",\"doi\":\"10.1016/j.biosystems.2025.105415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism for reaction-based models is the Petri nets which has a graphical representation and a mathematical notation that enables structural analysis and quantitative simulation. In this paper, we present an algorithm based on Petri nets topological features for the transformation of the computational model of a biological signaling network into an annotated influence graph. We also show the transformation of the Petri nets model of the beta-adrenergic receptor activating the PKA-MAPK signaling network into its representation as an influence graph.</div></div>\",\"PeriodicalId\":50730,\"journal\":{\"name\":\"Biosystems\",\"volume\":\"250 \",\"pages\":\"Article 105415\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0303264725000255\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264725000255","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

生物信号网络的常见描述是影响图，其中分子物种之间的激活和抑制效应用顶点和圆弧连接起来。基于反应的模型的另一种形式是Petri网，它具有图形表示和数学符号，可以进行结构分析和定量模拟。本文提出了一种基于Petri网拓扑特征的算法，用于将生物信号网络的计算模型转换为带注释的影响图。我们还展示了β -肾上腺素能受体激活PKA-MAPK信号网络的Petri网模型转化为影响图。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

An algorithm for the transformation of the Petri net models of biological signaling networks into influence graphs

A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism for reaction-based models is the Petri nets which has a graphical representation and a mathematical notation that enables structural analysis and quantitative simulation. In this paper, we present an algorithm based on Petri nets topological features for the transformation of the computational model of a biological signaling network into an annotated influence graph. We also show the transformation of the Petri nets model of the beta-adrenergic receptor activating the PKA-MAPK signaling network into its representation as an influence graph.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Biosystems 生物-生物学

CiteScore

3.70

自引率

18.80%

发文量

129

审稿时长

34 days

期刊介绍： BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.