SBMLNetwork:一个基于标准的生化模型可视化框架。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-22 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013128
Adel Heydarabadipour, Lucian Smith, Joseph L Hellerstein, Herbert M Sauro
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引用次数: 0

摘要

SBMLNetwork是一个开源软件库,它使SBML Layout和Render包可用于基于标准的生化模型可视化。当前的工具通常以定制设计的、特定于工具的格式管理模型可视化数据,并将其与模型本身分开存储,这阻碍了互操作性、再现性以及可视化与模型数据的无缝集成。SBMLNetwork通过直接构建SBML布局和渲染规范,自动生成符合标准的可视化数据,提供具有广泛集成支持的模块化实现,并提供针对系统生物学研究人员需求量身定制的健壮API,解决了这些限制。我们演示了SBMLNetwork跨关键可视化任务的功能,包括sbgn兼容的可视化、预定义样式模板的应用、反映路径逻辑的布局安排,以及将模型数据集成到网络图中。这些示例演示了SBMLNetwork如何支持高级可视化特性,并将用户意图无缝地转换为可重复的输出,从而在SBML模型中支持结构表示和动态数据可视化。SBMLNetwork在MIT许可下可在https://github.com/sys-bio/SBMLNetwork免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SBMLNetwork: A framework for standards-based visualization of biochemical models.

SBMLNetwork: A framework for standards-based visualization of biochemical models.

SBMLNetwork: A framework for standards-based visualization of biochemical models.

SBMLNetwork: A framework for standards-based visualization of biochemical models.

SBMLNetwork is an open-source software library that makes the SBML Layout and Render packages practical for standards-based visualization of biochemical models. Current tools often manage model visualization data in custom-designed, tool-specific formats and store it separately from the model itself, hindering interoperability, reproducibility, and the seamless integration of visualization with model data. SBMLNetwork addresses these limitations by building directly on the SBML Layout and Render specifications, automating the generation of standards-compliant visualization data, offering a modular implementation with broad integration support, and providing a robust API tailored to the needs of systems biology researchers. We illustrate the capabilities of SBMLNetwork across key visualization tasks, including SBGN-compliant visualization, application of predefined style templates, layout arrangement to reflect pathway logic, and integration of model data into network diagrams. These examples demonstrate how SBMLNetwork enables high-level visualization features and seamlessly translate user intent into reproducible outputs that support both structural representation and dynamic data visualization within the SBML model. SBMLNetwork is freely available at https://github.com/sys-bio/SBMLNetwork under the MIT license.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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