A novel strategy for dynamic modeling of genome-scale interaction networks

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Omics data enables holistic biomolecular interaction maps but often results in static networks.
• Dynamic biological process modeling is typically limited to small-scale systems.
• A significant challenge exists in constructing large-scale dynamic models for quantitative prediction of cellular behaviors.

Purpose of the Study:

• To propose an automated pipeline for the construction of large-scale dynamic models.
• To bridge the gap between static, large-scale networks and small-scale dynamic models.
• To enable holistic, quantitative predictions for applications like precision medicine.

Main Methods:

• Input: List of biomolecules and their time-course trajectories.
• Pipeline: Constructs interaction networks, translates them into biochemical reactions, generates ordinary differential equations (ODEs) for kinetics, and estimates ODE parameters using a novel large-scale approximation method.
• Tool: Systematic Protein Association Dynamic ANalyzer (SPADAN).

Main Results:

• Demonstrated high performance by modeling colorectal cancer cell line response to chemotherapy.
• Successfully constructed genome-scale dynamic models.
• SPADAN fills the gap between static and small-scale dynamic modeling strategies.

Conclusions:

• The developed pipeline and SPADAN tool enable the creation of large-scale dynamic models.
• This approach facilitates quantitative predictions crucial for simulating therapeutic interventions in precision medicine.
• The simulation approach allows for holistic quantitative predictions critical for precision medicine.