Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data

Area of Science:

• Systems Biology
• Computational Biology
• Biotechnology

Background:

• Cell Illustrator is a modeling tool based on Hybrid Functional Petri nets with extension (HFPNe).
• Tandem mass spectrometry coupled with liquid chromatography (LC/MS/MS) provides quantitative proteomic data.
• Integrating dynamic proteomic data into HFPNe models can enhance biological pathway modeling.

Purpose of the Study:

• To report the first integration of time-series proteomic data into an HFPNe model.
• To construct and refine a model of the epidermal growth factor receptor (EGFR) signal transduction pathway.
• To demonstrate the semi-automatic construction of a well-tuned EGFR HFPNe model.

Main Methods:

• Utilized Cell Illustrator software for HFPNe model construction.
• Employed a data assimilation (DA) framework for kinetic parameter determination.
• Integrated time-series proteomic data from LC/MS/MS and literature-based biological knowledge for model refinement and selection.

Main Results:

• Successfully constructed an EGFR signal transduction pathway model using HFPNe.
• Determined kinetic parameters by fitting the model to published proteomic data using a DA framework with manual tuning.
• Refined the model by incorporating biological knowledge and used the DA framework to select the most plausible model structure.

Conclusions:

• Time-series proteomic data can be effectively integrated into HFPNe models using Cell Illustrator and a DA framework.
• This approach facilitates the semi-automatic construction of well-tuned biological pathway models.
• The developed methodology enhances the accuracy and reliability of in silico biological models for systems biology research.