Development of an Automated System for Long-Term Observation of Navigational Behavior in D. Melanogaster Larvae

Presenter: Maria Paz

Research Category: Physical and LIfe Sciences
Student Type: Graduate
Additional Authors: James Yu, Vivek Venkatachalam
PI: Vivek Venkatachalam
Award Winner Category: Physical and Life Sciences

Navigational behavior in an animal can be modified due to learning and can exhibit variations between individuals to create behavioral phenotypes. To achieve high precision and reliability for either of these phenomena, observations of individual animals must be over long timescales (10-100% of a lifetime). This can be difficult to carry out manually, so we implement and demonstrate an automated approach to achieve new orders of magnitude in precision and experimental duration with both free and biased roaming Drosophila melanogaster larvae. We modify a 3D printer system to automatically maintain exploratory behavior while confining the animals within the experiment area via a “pick-and-place” mechanism. Here, we demonstrate the ability of this system to quantify and observe significant changes in larva run speed, turn rate, and turn handedness over experimental durations of several hours, indicative of some previously unexamined behavioral adaptations and mechanisms in learning and memory. We will continue to further develop and exploit its efficiency, precision, and high customizability to explore other creative applications for the robotic system, such as implementing a similar approach for experiments with C. elegans and other model organisms.