A Review on Computational Systems Biology Approach to UnderstandDuchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is a severe, hereditary, and progressively worsening neuromuscular disorder caused by the lack of dystrophin. This deficiency leads to progressive muscle degeneration, loss of ambulation, and multisystem complications, including cardiac and respiratory failure. Despite advances in supportive care, effective disease-modifying therapies remain limited, highlighting the need for a better understanding of disease mechanisms. Recent advances in computational network biology offer promising strategies for identifying novel therapeutic targets by integrating omics data, biomolecular modeling, and network analysis to better understand disease mechanisms and associated pathways. This narrative review explores the pivotal role of network biology in DMD research, with a focus on various biological networks (including protein-protein interaction and gene regulatory networks) and their topological properties that define network structure and behavior. We discuss key approaches such as Weighted Gene Co-expression Network Analysis (WGCNA), Principal Component Analysis (PCA), differential gene expression analysis, and network-based personalized medicine strategies to identify regulatory hubs and potential therapeutic targets. This review provides a comprehensive overview of how network-based approaches enhance our insights into DMD pathophysiology, highlight translational opportunities, and contribute to the development of more effective therapies for DMD and related neuromuscular disorders.