Tasaki Shinya, Nagasaki Masao, Oyama Masaaki, Hata Hiroko, Ueno Kazuko, Yoshida Ryo, Higuchi Tomoyuki, Sugano Sumio, Miyano Satoru
Medical Proteomics Laboratory, Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
Genome Inform. 2006;17(2):226-38.
Cell Illustrator is a model building tool based on the Hybrid Functional Petri net with extension (HFPNe). By using Cell Illustrator, we have succeeded in modeling biological pathways, e.g., metabolic pathways, gene regulatory networks, microRNA regulatory networks, cell signaling networks, and cell-cell interactions. The recent development of tandem mass spectrometry coupled with liquid chromatography (LC/MS/MS) technology has enabled researchers to quantify the dynamic profile of a wide range of proteins within the cell. The proteomic data obtained by using LC/MS/MS has been considerably useful for introducing dynamics to the HFPNe model. Here, we report the first introduction of the time-series proteomic data to our HFPNe model. We constructed an epidermal growth factor receptor signal transduction pathway model (EFGR model) by using the biological data available in the literature. Then, the kinetic parameters were determined in the data assimilation (DA) framework with some manual tuning so as to fit the proteomic data published by Blagoev et al. (Nat. Biotechnol., 22:1139-1145, 2004). This in silico model was further refined by adding or removing some regulation loops using biological background knowledge. The DA framework was used to select the most plausible model from among the refined models. By using the proteomic data, we semi-automatically constructed a well-tuned EGFR HFPNe model by using the Cell Illustrator coupled with the DA framework.
细胞绘图仪是一种基于扩展混合功能Petri网（HFPNe）的模型构建工具。通过使用细胞绘图仪，我们成功地对生物途径进行了建模，例如代谢途径、基因调控网络、微小RNA调控网络、细胞信号网络和细胞间相互作用。液相色谱串联质谱（LC/MS/MS）技术的最新发展使研究人员能够量化细胞内多种蛋白质的动态概况。通过使用LC/MS/MS获得的蛋白质组学数据对于将动力学引入HFPNe模型非常有用。在此，我们报告首次将时间序列蛋白质组学数据引入我们的HFPNe模型。我们利用文献中可用的生物学数据构建了表皮生长因子受体信号转导途径模型（EFGR模型）。然后，在数据同化（DA）框架中通过一些手动调整确定动力学参数，以拟合Blagoev等人发表的蛋白质组学数据（《自然生物技术》，22:1139 - 1145，2004年）。通过使用生物学背景知识添加或去除一些调控环，对这个计算机模拟模型进行了进一步优化。DA框架用于从优化后的模型中选择最合理的模型。通过使用蛋白质组学数据，我们结合细胞绘图仪和DA框架半自动构建了一个经过良好调整的表皮生长因子受体HFPNe模型。
