Shinya Tasaki,* (email@example.com)
Masao Nagasaki* (firstname.lastname@example.org)
Masaaki Oyama (email@example.com)
Hiroko Hata (firstname.lastname@example.org)
Kazuko Ueno (email@example.com)
Ryo Yoshida (firstname.lastname@example.org)
Tomoyuki Higuchi (email@example.com)
Sumio Sugano (firstname.lastname@example.org)
Satoru Miyano (email@example.com)
Medical Proteomics Laboratory, Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
Human Genome Center, Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
Department of Medical Genome Science, Graduate School of Frontier Sciences, the University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569, Japan
*These authors contributed equally.
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.