Networked collective dynamics in animal ecology and cell biology

Collective behavior, emerging from interactions among individuals, is a ubiquitous phenomenon observed across a wide range of biological systems-from cellular dynamics to animal ecology. Network science offers powerful tools for understanding the structure and functional properties underlying such systems. Despite significant progress in modeling, data-driven analysis, and interdisciplinary approaches, several critical challenges persist. How do complex social interactions among individuals influence the emergence of collective behavior? Moreover, in what ways do individual information and social interactions jointly shape collective decision-making? To address these challenges, this review synthesizes recent advances in network science, statistical physics, and artificial intelligence as applied to the study of collective phenomena. We focus on collective patterns in animals and cells, highlighting the interplay between structure and dynamics. The review begins with an overview of basic forms of collective behavior, followed by discussions of microscopic and macroscopic modeling approaches, structural and functional features of social interaction networks, mechanisms of information transmission, decision-making processes, and emergent collective intelligence. We also explore cutting-edge applications, including bio-inspired robotic swarms and coordination systems for unmanned aerial vehicles. We conclude by outlining open questions and potential research directions, aiming to provide insights into the design, control, and understanding of complex collective systems across biology, physics, and artificial intelligence.