Ding, S.S. et al. (2017) International Worm Meeting "Automated animal tracking and quantitative analysis of C. elegans social behavior."

Schumacher, L.J., Brown, A.E.X., Endres, R.G, Javer, A.E., Ding, S.S.

[International Worm Meeting, 2017]

Social behavior is common in the animal kingdom, but studies are often limited to the observational level, as few systems allow for perturbation in a controlled environment. To this end, we use C. elegans to dissect the behavioral mechanisms of aggregation, a simple social behavior. C. elegans natural isolates aggregate into tight groups, whereas the laboratory strain forms looser groups or feeds alone; the difference is due in part to a mutation in the neuropeptide receptor gene npr-1 which arose during laboratory domestication. We quantified the behavior of N2, an npr-1 mutant and a natural isolate using fluorescence imaging and automated animal tracking, and built a chain-like agent-based mathematical model to identify the key differences.

Ding, S.S. et al. (2019) International Worm Meeting "Shared behavioral mechanisms underlie C. elegans aggregation and swarming."

Endres, R.G., Javer, A.E., Schumacher, L.J., Ding, S.S., Brown, A.E.X., Sarkisyan, K.S.

[International Worm Meeting, 2019]

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. We currently extend this work by studying swarming at very high worm numbers, as well as by visualising and quantifying feeding using a bioluminescent bacterial system.