Journal article
Networked collective dynamics in animal ecology and cell biology
- Abstract:
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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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 22.6MB, Terms of use)
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- Publisher copy:
- 10.1016/j.plrev.2026.02.003
Authors
- Funder identifier:
- https://ror.org/01h0zpd94
- Publisher:
- Elsevier
- Journal:
- Physics of Life Reviews More from this journal
- Volume:
- 57
- Pages:
- 4-60
- Publication date:
- 2026-03-02
- Acceptance date:
- 2026-02-26
- DOI:
- EISSN:
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1873-1457
- ISSN:
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1571-0645
- Language:
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English
- Keywords:
- Pubs id:
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2389272
- Local pid:
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pubs:2389272
- Deposit date:
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2026-03-14
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier B.V.
- Copyright date:
- 2026
- Rights statement:
- © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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