Swarming Behavior in Plant Roots

Year: 2012

Authors: Ciszak M., Comparini D., Mazzolai B., Baluska F., Arecchi F.T., Vicsek T., Mancuso S.

Autors Affiliation: CNR-Istituto Nazionale di Ottica, Florence, Italy; LINV-Department of Plant Soil and Environmental Science, University of Florence, Florence, Italy; Center for Micro-BioRobotics, Istituto Italiano di Tecnologia, Pontedera (PI), Italy; Institute of Cellular and Molecular Botany, University of Bonn, Bonn, Germany; Department of Physics, University of Florence, Florence, Italy; Department of Biological Physics, Eötvös Loránd University, Budapest, Hungary

Abstract: Interactions between individuals that are guided by simple rules can generate swarming behavior. Swarming behavior has been observed in many groups of organisms, including humans, and recent research has revealed that plants also demonstrate social behavior based on mutual interaction with other individuals. However, this behavior has not previously been analyzed in the context of swarming. Here, we show that roots can be influenced by their neighbors to induce a tendency to align the directions of their growth. In the apparently noisy patterns formed by growing roots, episodic alignments are observed as the roots grow close to each other. These events are incompatible with the statistics of purely random growth. We present experimental results and a theoretical model that describes the growth of maize roots in terms of swarming.

Journal/Review: PLOS ONE

Volume: 7 (1)      Pages from: e29759  to: e29759

More Information: Financial sources that have supported the work: Marie Curie European Reintegration Grant (N. 239324) within the 7th European Community Framework Program. URL: http://cordis.europa.eu/fp7/dc/index.cfm. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
KeyWords: article; controlled study; experimental design; maize; nonhuman; plant root; root growth; social behavior; social interaction; theoretical model; algorithm; biological model; growth, development and aging; maize; meristem, Zea mays, Algorithms; Meristem; Models, Biological; Plant Roots; Zea mays
DOI: 10.1371/journal.pone.0029759

ImpactFactor: 3.730
Citations: 27
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