Temporal Orchestration in Biological Systems: Advancing Network Biology Beyond Static Representations

Network biology has become a cornerstone methodology for understanding complex biological systems, yet the majority of approaches employ static networks that fail to capture the inherently dynamic nature of biological processes. This literature review synthesizes recent advances in moving biological network analysis beyond static models toward dynamic, temporal frameworks that better represent spatiotemporal complexity. We critically examine emerging methodologies for constructing, analyzing, and visualizing dynamic biological networks across multiple scales, from molecular interactions to cellular systems and organismal development. The review evaluates significant progress in temporal network inference algorithms, mathematical modeling approaches, and computational tools that have expanded our ability to interpret time-varying biological data. We further explore applications in disease progression modeling, drug response prediction, and personalized medicine, highlighting how dynamic network approaches have improved our understanding of biological mechanisms. Despite notable advances, significant challenges remain in data integration, computational efficiency, and biological interpretation of temporal network patterns. By bridging disciplinary boundaries between network science, systems biology, and computational modeling, dynamic network approaches are poised to transform our understanding of living systems and accelerate biomedical research.