Resilience and evolutionary insights in PPI networks: comparative analysis of node resilience and centrality measures

作者：Jiarui Zhang · 发表于：Frontiers in Genetics · 年份：2026 · DOI：10.3389/fgene.2025.1613475 · 被引用次数：2 · 研究领域：Bioinformatics and Genomic Networks、Gene Regulatory Network Analysis、Complex Network Analysis Techniques

Introduction: Protein-protein interaction (PPI) networks serve as the central framework for deciphering the modular structure of cellular functions and signal transduction mechanisms. While established network topological Measures (such as degree centrality, betweenness centrality, and closeness centrality) can statically characterize nodal connectivity density or pathway intermediation capacity, they fail to dynamically capture cascade following node failure. Method: This study employs systems biology approaches to quantitatively analyze network resilience based on bacterial PPI network data obtained from the Stanford Network Analysis Platform (SNAP). First, a progressive node removal strategy was implemented to simulate cascading failure propagation and evaluate system-level resilience degradation dynamics. Subsequently, single-node knockout experiments were systematically conducted to quantify local topological disruption effects, with network fragmentation metrics (e.g., giant component size decay rate) being integrated to establish the Node Resilience (NR) index. To validate the biological relevance of NR, we developed a multidimensional analytical framework that performs cross-correlation analysis between NR and classical centrality measures [Degree centrality (DC), Betweenness centrality (BC), Closeness centrality (CC), Eigenvector centrality (EC)], enabling systematic revelation of consensus vital nodes identified by both approaches, and unique sensitive nodes detecta...