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Stability and Responsiveness in a Self-Organized Living Architecture

Overview of attention for article published in PLoS Computational Biology, March 2013
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Title
Stability and Responsiveness in a Self-Organized Living Architecture
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002984
Pubmed ID
Authors

Simon Garnier, Tucker Murphy, Matthew Lutz, Edward Hurme, Simon Leblanc, Iain D. Couzin

Abstract

Robustness and adaptability are central to the functioning of biological systems, from gene networks to animal societies. Yet the mechanisms by which living organisms achieve both stability to perturbations and sensitivity to input are poorly understood. Here, we present an integrated study of a living architecture in which army ants interconnect their bodies to span gaps. We demonstrate that these self-assembled bridges are a highly effective means of maintaining traffic flow over unpredictable terrain. The individual-level rules responsible depend only on locally-estimated traffic intensity and the number of neighbours to which ants are attached within the structure. We employ a parameterized computational model to reveal that bridges are tuned to be maximally stable in the face of regular, periodic fluctuations in traffic. However analysis of the model also suggests that interactions among ants give rise to feedback processes that result in bridges being highly responsive to sudden interruptions in traffic. Subsequent field experiments confirm this prediction and thus the dual nature of stability and flexibility in living bridges. Our study demonstrates the importance of robust and adaptive modular architecture to efficient traffic organisation and reveals general principles regarding the regulation of form in biological self-assemblies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 7%
Spain 1 1%
Italy 1 1%
Unknown 88 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 30%
Researcher 15 15%
Student > Bachelor 9 9%
Student > Doctoral Student 7 7%
Professor > Associate Professor 7 7%
Other 20 21%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 45%
Engineering 12 12%
Computer Science 8 8%
Physics and Astronomy 6 6%
Biochemistry, Genetics and Molecular Biology 5 5%
Other 7 7%
Unknown 15 15%