Networks and games for precision medicine

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Precision medicine personalizes therapy using omics data and computational methods.
• Complex diseases involve intricate molecular interactions, necessitating network-based analysis for drug efficacy assessment.

Purpose of the Study:

• To propose a computational framework for optimal drug selection in precision medicine.
• To integrate Game Theory and Boolean networks for modeling disease-drug interactions.

Main Methods:

• Developed the Network-Action Game (NAG) computational framework.
• Utilized Game Theory for decision-making in drug selection.
• Employed Boolean networks to model molecular system dynamics under disease and drug actions.
• Focused on interactome arc alterations as drug and disease strategies.

Main Results:

• Evaluated the NAG framework's efficiency for drug prediction.
• Demonstrated applicability on a breast cancer signaling pathway model.

Conclusions:

• The Network-Action Game framework offers a novel computational approach for personalized drug selection.
• This method aids clinical decision-making in complex diseases like cancer by analyzing molecular network perturbations.