Modelling Protein-Protein Interactions using Graph Theory: A Computational Biology Perspective

Abstract

This study explores the application of graph theory to model protein–protein interactions, highlighting its significance in advancing computational biology and systems-level understanding of molecular processes. By representing proteins as nodes and their interactions as edges, graph-theoretic approaches enable the structural and functional characterisation of complex interaction networks. The analysis demonstrates how centrality measures, clustering coefficients, and modularity reveal essential proteins, functional communities, and organisational principles underlying cellular behaviour. Predictive applications, including interaction inference and robustness assessment, further illustrate the value of graph-based models in addressing data incompleteness and identifying potential therapeutic targets. Although limitations persist due to data variability and the static nature of most interaction maps, the findings affirm that graph theory provides a rigorous and insightful framework for interpreting protein networks. The study underscores the growing importance of computational methods in understanding biological systems.