Boolean modeling techniques for protein co-expression networks in systems medicine.

Area of Science:

• Systems biology and systems medicine
• Proteomics and biomedical research
• Computational modeling

Background:

• Systems biology/medicine approaches are promising for proteomics research.
• Selecting an adequate modeling type is crucial for these approaches.
• Boolean network modeling offers advantages for processing proteomics data.

Purpose of the Study:

• Review existing Boolean network modeling approaches for proteomics data.
• Present methods for inference, reduction, and validation of protein co-expression networks.
• Discuss the application of Boolean models for deriving system-theoretic characteristics and simulating therapeutic interventions.

Main Methods:

• Review of Boolean network modeling techniques.
• Application of methods for inference, reduction, and validation of protein co-expression networks from high-throughput proteomics data.
• Utilizing control theory for simulating therapeutic interventions.

Main Results:

• Boolean models can characterize the dynamical behavior of protein co-expression networks.
• Boolean models help describe properties of different cell states (healthy vs. diseased).
• Control theory applications show promise for computer-assisted biomarker and drug target discovery.

Conclusions:

• Boolean modeling of proteomics data is an emerging field.
• Public reference models and community standards are needed for progress.
• Clinical applications of Boolean modeling analyses are discussed.