Tang Yin, Wang Fei
Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China.
J Bioinform Comput Biol. 2012 Feb;10(1):1240006. doi: 10.1142/S0219720012400069.
The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.
皮氏培养皿网络形式体系已被证明在生物学建模中十分强大。它不仅拥有最为直观的图形表示，还将经典系统生物学方法与离散建模技术相结合。混合功能皮氏培养皿网络（HFPN）是专门为生物系统建模而提出的。一系列使用HFPN构建良好的生物学模型产生了非常有趣的结果。在本文中，我们提出一种表示神经系统行为的方法，其中使用皮氏培养皿网络形式体系同时纳入了生物化学和电化学。我们使用HFPN为肾上腺素能系统构建了一个模型并进行了定量分析。我们的模拟结果与生物学数据匹配良好，表明该模型非常有效。基于我们模型所做的预测进一步彰显了HFPN的建模能力，并增进了对肾上腺素能系统的理解。我们模型的文件以及更多带有分析结果的内容可在我们的补充材料中获取。
