Using a Guided User Interface and Automated Robot to Image Caenorhabditis Elegans
Advisor(s)
Anthony Fouad, University of Pennsylvania
Location
Room A117
Start Date
26-4-2019 11:05 AM
End Date
26-4-2019 11:20 AM
Abstract
From aging research to paramount biological discoveries such as apoptosis, C. Elegans has been the crux of many research problems in the past decade. However, they possess certain qualities, such as their microscopic size, that make repetitive lab work extremely tedious for human labor and thus a strong candidate for automation. To automate the process of preparing worms for certain experiments, we designed and coded software that was able to accurately detect worms in a grayscale image. To do this, we analyzed the contour curves of a regular roundworm and also designed an efficient guided user interface to improve usability. Through our efforts, we were able to produce a robot capable of moving in three lateral directions with a camera mounted gantry system. It is capable of fine tuning images and live feed, as well as recognizing worms with great accuracy. Furthermore, the robot is also mounted with a metallic wire pick that is used to pick up worms and transfer them to new plates. The pick is retractable and can also autonomously sterilize itself. Since the pick would need to have extreme precision to pick up a worm, it would need to be extremely sensitive. In solving this issue, we developed two ways: image analysis and capacitive touch sensing. The image analysis simply detected small changes in image gradience while the capacitive touch sense small changes in electric current. Overall, the project demonstrated the opportunities of automation and how it can efficiently minimize tedious lab procedures. Not only is the robot intuitive, but also highly distributable and reproducible for other laboratories.
Using a Guided User Interface and Automated Robot to Image Caenorhabditis Elegans
Room A117
From aging research to paramount biological discoveries such as apoptosis, C. Elegans has been the crux of many research problems in the past decade. However, they possess certain qualities, such as their microscopic size, that make repetitive lab work extremely tedious for human labor and thus a strong candidate for automation. To automate the process of preparing worms for certain experiments, we designed and coded software that was able to accurately detect worms in a grayscale image. To do this, we analyzed the contour curves of a regular roundworm and also designed an efficient guided user interface to improve usability. Through our efforts, we were able to produce a robot capable of moving in three lateral directions with a camera mounted gantry system. It is capable of fine tuning images and live feed, as well as recognizing worms with great accuracy. Furthermore, the robot is also mounted with a metallic wire pick that is used to pick up worms and transfer them to new plates. The pick is retractable and can also autonomously sterilize itself. Since the pick would need to have extreme precision to pick up a worm, it would need to be extremely sensitive. In solving this issue, we developed two ways: image analysis and capacitive touch sensing. The image analysis simply detected small changes in image gradience while the capacitive touch sense small changes in electric current. Overall, the project demonstrated the opportunities of automation and how it can efficiently minimize tedious lab procedures. Not only is the robot intuitive, but also highly distributable and reproducible for other laboratories.