Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory.

Area of Science:

• Systems biology
• Bioinformatics
• Network analysis

Background:

• Biological networks are crucial for understanding complex diseases like rabies.
• Traditional centrality measures (degree, closeness, betweenness) focus on individual nodes and ignore cooperative interactions.
• Identifying key genes in disease networks is essential for therapeutic target discovery.

Purpose of the Study:

• To develop and apply a novel group centrality measurement using cooperative game theory to analyze the protein interaction network in rabies.
• To identify novel gene candidates by ranking gene products based on their cooperative importance.
• To overcome the limitations of classical centrality measures that ignore node synergies.

Main Methods:

• A game-theoretic approach was used to assess the power of gene coalitions in the rabies protein interaction network (1059 nodes, 8844 edges).
• Shapley value was employed as a new centrality measure, considering different scenarios based on gene neighborhood and predefined importance.
• Analysis was performed using the CINNA package in R software.

Main Results:

• Genes with high Shapley values were identified, including those ranked low by classical centralities.
• Enrichment analysis of selected genes in scenario 1 revealed significant pathways in rabies pathogenesis.
• Node weighting variations across scenarios demonstrated a significant impact on gene ranking.

Conclusions:

• A game-theoretic framework, combined with prior disease knowledge and network topology, effectively identifies biologically significant genes.
• The proposed group centrality approach offers a more comprehensive understanding of gene importance than single-node measures.
• This method enhances the discovery of novel therapeutic targets in complex diseases.