Shared behavioral mechanisms underlie C. elegans aggregation and swarming
Abstract
In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming-a dynamic phenotype only observed at longer timescales. Using fluorescence multi-worm tracking, we quantify aggregation in terms of individual dynamics and population-level statistics. Then we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules for aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation.
Data availability
All data generated and analysed during this study is deposited on the Open Worm Movement Database community page (https://zenodo.org/communities/open-worm-movement-database/). As the full dataset is over 1TB, it is not possible to provide a single DOI for the full dataset. Instead, each recording has a separate DOI, which can be found in Supplementary Table 2. The code for model simulations is available at github.com/ljschumacher/sworm-model.
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Open Worm Movement DatabaseZenodo, Open Worm Movement Database.
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Open Worm Movement DatabaseZenodo, Open Worm Movement Database.
Article and author information
Author details
Funding
Biotechnology and Biological Sciences Research Council (BB/N00065X/1)
- Robert G Endres
- André EX Brown
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2019, Ding et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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