We present a novel platform for testing the effects of interventions on the life- and healthspan of a short-lived freshwater organism with complex behavior and physiology-the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine-learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using a long-term dose-response profile of metformin and a short-term assay of well-studied substances such as caffeine and alcohol.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etsu-works-2-1016 |
| Date | 01 March 2022 |
| Creators | Cho, Yongmin, Jonas-Closs, Rachael A., Yampolsky, Lev Y., Kirschner, Marc W., Peshkin, Leonid |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Source | ETSU Faculty Works |
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