Measuring and modeling the thermotactic learning behavior of C. elegans.
ORAL
Abstract
The roundworm C. elegans learns from its experiences. Worms on a thermal gradient perform
thermotaxis to or away from the conditioned temperature, depending on its thermal history and the
starvation duration during the conditioning phase. To quantify this behavior, we developed a novel assay
that tracks single worms—each experiencing a spatiotemporal thermal gradient with thermal precision
±0.01C—in a small (2.8ul) droplets of buffer, arrayed on hydrophobic-printed microscope slides. CCD
cameras track individual worms simultaneously in many droplets for many hours and at high temporal resolution, each droplet set at 20C midpoint with a thermal gradient of 0.5C/cm. A worm’s thermal preference is
summarized as a thermotaxis index. Initially, worms reared at 25C and 15C exhibit thermophilic or
cryophilic tendencies, respectively, and starvation during conditioning or in the droplet reverses these
tendencies. This reversal indicates multiple dynamic processes for learning that operate on different time
scales. We build a predictive model with multiple time scales and utilize mutants to detangle the various
learning processes. The model predicts the behavior under various training conditions and genetic perturbations.
thermotaxis to or away from the conditioned temperature, depending on its thermal history and the
starvation duration during the conditioning phase. To quantify this behavior, we developed a novel assay
that tracks single worms—each experiencing a spatiotemporal thermal gradient with thermal precision
±0.01C—in a small (2.8ul) droplets of buffer, arrayed on hydrophobic-printed microscope slides. CCD
cameras track individual worms simultaneously in many droplets for many hours and at high temporal resolution, each droplet set at 20C midpoint with a thermal gradient of 0.5C/cm. A worm’s thermal preference is
summarized as a thermotaxis index. Initially, worms reared at 25C and 15C exhibit thermophilic or
cryophilic tendencies, respectively, and starvation during conditioning or in the droplet reverses these
tendencies. This reversal indicates multiple dynamic processes for learning that operate on different time
scales. We build a predictive model with multiple time scales and utilize mutants to detangle the various
learning processes. The model predicts the behavior under various training conditions and genetic perturbations.
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Presenters
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Ahmed Roman
Physics, Emory University
Authors
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Ahmed Roman
Physics, Emory University
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Konstantine Palanski
ANTIBODY Healthcare Communications
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Ilya M Nemenman
Emory University, Physics Department, Emory University, Physics, Emory University
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William Ryu
Physics, University of Toronto