The interplay of assortativity, centrality, and robustness in human signaling networks: Implications for drug discovery

Abstract

This study investigated the relationship between assortativity, a fundamental structural property of complex networks, and other key network characteristics in the context of a human signaling network. Despite the importance of assortativity in understanding network structures, there is a lack of comprehensive research exploring its connections to centrality measures and robustness, especially at the node level. We address this gap by examining the interplay between assortativity, various centrality measures, and network robustness while also exploring its potential as an indicator for predicting drug targets. Our findings revealed significant correlations between these network properties. First, we observed a strong negative relationship between assortativity and centrality measures at the node level, indicating that highly assortative nodes tended to have lower centrality scores. Second, we demonstrate that network robustness, defined as the ability to maintain dynamic behavior under perturbations, is negatively correlated with assortativity. Networks that exhibit higher assortativity are less robust. Finally, we identified assortativity as a promising indicator for predicting drug targets within the human signaling network, suggesting its potential for identifying key nodes that can modulate network dynamics. This study contributes to a deeper understanding of the structural and dynamic properties of complex networks, particularly in biological signaling systems. Our findings not only advance theoretical knowledge but also offer practical insights for applications such as identifying influential nodes and designing interventions to control network dynamics. This study paves the way for further exploration of the intricate relationships between structural and dynamical properties in complex networks.