SBDyNetVis:用于系统生物学模型的动态网络的可视化工具
1Faculty of Engineering, University of Miyazaki, Miyazaki, Japan.
Bio Systems
|November 7, 2025
概括
SBDyNetVis是一个新的Python库,可视化生物网络动态. 它通过将模拟数据与交互式网络可视化集成,帮助研究人员了解复杂的分子相互作用.
科学领域:
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 分析复杂的分子相互作用需要将网络结构与动态模拟数据集成在一起.
- 传统工具经常呈现静态网络或单独的模拟输出,阻碍了对时间动态和网络拓学的并发解释.
- 需要集成的平台来有效地可视化动态的生物过程.
研究的目的:
- 介绍SBDyNetVis,一个新的Python库,用于创建生物网络和时间过程数据的交互式JavaScript可视化.
- 为了使反应动力学,流量方程和生物系统内的动态变化的直观探索.
- 通过基于Web的界面,促进研究人员之间的协作和数据共享.
主要方法:
- 开发了SBDyNetVis作为一个基于Python的库.
- 集成的Cytoscape.js用于交互式JavaScript可视化.
- 包含多个网络布局算法和动画节点/边缘表示.
- 从BioModels中测试了913个模型的转换,并应用于IKK-NFκB-IκB反应案例研究.
主要成果:
- 通过SBDyNetVis.成功地将913个模型从BioModels转换成SBDyNetVis.
- 证明了该工具在可视化IKK-NFκB-IκB反应的动态方面的实用性.
- 创建了一个集成的平台,同时探索网络结构和时间动态.
- 启用了反应动力学和流量方程的直观可视化.
结论:
- SBDyNetVis有效地弥合了静态网络表示和动态模拟可视化之间的差距.
- 该图书馆增强了对细胞过程的理解,并推进了系统生物学研究.
- 它的基于网络的性质促进了所有技术水平的研究人员的可访问性和协作.
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