Projects per year
Abstract
During locomotion, soft-bodied terrestrial animals solve complex control problems at substrate interfaces, but our understanding of how they achieve this without rigid components remains incomplete. Here, we develop new all-optical methods based on optical interference in a deformable substrate to measure ground reaction forces (GRFs) with micrometre and nanonewton precision in behaving Drosophila larvae. Combining this with a kinematic analysis of substrate-interfacing features, we shed new light onto the biomechanical control of larval locomotion. Crawling in larvae measuring ~1 mm in length involves an intricate pattern of cuticle sequestration and planting, producing GRFs of 1–7 µN. We show that larvae insert and expand denticulated, feet-like structures into substrates as they move, a process not previously observed in soft-bodied animals. These ‘protopodia’ form dynamic anchors to compensate counteracting forces. Our work provides a framework for future biomechanics research in soft-bodied animals and promises to inspire improved soft-robot design.
Original language | English |
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Article number | RP87746 |
Number of pages | 24 |
Journal | eLife |
Volume | 12 |
DOIs | |
Publication status | Published - 23 Jul 2024 |
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Dive into the research topics of 'Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae'. Together they form a unique fingerprint.Projects
- 2 Finished
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Resonant and shaped photonics for under: Resonant and shaped photonics for understanding the physical and biomedical world
Dholakia, K. (PI) & Gather, M. C. (CoI)
1/08/17 → 31/07/22
Project: Standard
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H2020 ERC Starting Grant ABLASE: H2020 ERC Starting Grant ABLASE (Grant Agreement No. 640012)
Gather, M. C. (PI)
1/06/15 → 31/05/21
Project: Standard
Datasets
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Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae (dataset)
Booth, J. R. H. (Creator), Pulver, S. R. (Supervisor) & Gather, M. C. (Supervisor), University of St Andrews, 30 Jul 2024
DOI: 10.17630/f2a655d8-85c7-4bc0-b01b-b6f67aab2f07
Dataset
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