A real-time interactive, fully automated, low-cost and scalable biology cloud experimentation platform could provide access to scientific experimentation for learners and researchers alike.
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We are grateful to the members of the Riedel-Kruse and Blikstein Labs, N. Cira, G. Harrison and the teachers and students who participated. This project was supported by an NSF Cyberlearning grant (#1324753) and NSF awards IIS-1216389, OCI-0753324 and DUE-0938075.
Illustration of interactive joystick experiment on the platform: A user visits the cloud lab website and runs a live experiment on a particular BPU ('eug15'). In the live view the user tests euglena response to four LEDs one at a time with a virtual joystick, while watching a live video feed of the actual LED going off. The Euglena exhibits negative phototaxis by swimming away from each LED in turn (compare also to Fig. 4a in main paper).
Batch mode experimentation and a workflow on the cloud lab platform from a user's point of view (for example as in user study Figs. 4a,b):). A user uploads two batch experiments as text scripts (both JSON and CSV formats) at the same time. The system routes these experiments to the best available BPUs, while avoiding the apparently suboptimal ones. The user then downloads the data from a previously run experiment and investigates a preprocessed video where Euglena and their tracks are automatically traced. This video has a corresponding data file in JSON format that can be processed in Matlab through an API that we provide. This API can export track information in a MS Excel format, CSV, for easier manipulation.
Examples of Euglena variety of behaviors that can be observed on this platform (passive observation as well as active experimentation): A. Euglena, seen through a 10x objective, responding to all four LED directions applied sequentially. B. Euglena, seen through a 4x objective, responding to all four LED directions applied sequentially. C. Euglena responding to light shone at an angle. D. This clip shows how a Euglena can be virtually controlled to follow a path with our joystick interface. E. The microfluidic chip getting overpopulated as seen through a 10x objective. F. The microfluidic chip getting overpopulated as seen through a 4x objective. G. In some scenarios, the linear motility of the Euglena population tends to decrease while they spin vigorously in response to light. H. Cell division events captured during a time lapse
Average orientation (in acute angle, degrees) of Euglena population in response to different LED and no-light conditions: No light stimulus was provided during the first 60s when the Euglena were randomly oriented leading to an average acute angle close to 45°. Each LED was then shone by itself for 30s in sequence, and the Euglenas move away from light every time. The average orientation of all the Euglenas per frame is plotted against time, which shows clear measurable alternating Hill type signals. No light was shone during the last 60s when the cell population converged back to random orientations. We ultimately use this orientation to measure responsiveness of a BPU as discussed in section 2.2.
Illustration of modeling interface as used in second study (Figs. 4c,d): 7th and 8th grade students investigated three parameters: surge, coupling and roll that drive a model Euglena to follow a predefined path upon light stimulus with a joystick. Only the name of the surge parameter was exposed while the other two were unnamed for students to find out as an exercise. The video demonstrates different combinations of parameters to demonstrate their effects on the model as well as to highlight the overall descriptive power of this model (compare also Supplementary Video 3 for related real behaviors): A. The simulation is run without changing the initial parameter values, which only sets surge to a non-zero number. The model Euglena propels without responding to any light. B. The coupling parameter is set to a positive number (15). This time the model Euglena exhibits positive phototaxis, i.e. move towards light. C. Coupling is set to a negative number (−15), the Euglena exhibits negative phototaxis as expected but does not respond to the “Right” LED because the model Euglena was sampling light only from the left as there was no spin. D. Roll is set to a small positive number (2), which lets Euglena see light in all directions, but the response is slow which results in a wobbly path with large amplitude upon light changes. E. Roll is set to 4 and the surge is decreased which corresponds to a near optimal setting. In this case, the Euglena responds to light stimulus in manner that is consistent with reality. F. Roll is set to 5 and coupling to a large negative number, which makes Euglena to tumble and spin uncontrollably.
Illustration of the iLab user study (Figs. 4e,f): This video demonstrates how users can operate the cloud lab from a third party education content management website, in this case iLab 6. A student would login with her iLab credentials, and choose one of the tasks assigned by her teacher. A task contains lessons about Euglena and accompanying quizzes. The images used in this lesson were taken from Wikipedia (https://en.wikipedia.org/wiki/Euglena). In page 3 of this lesson, the student uses a simple interface to design an experiment with light stimulus and timing. The student can get an estimate of how long her experiment will take for the cloud lab to run before submitting it as a batch experiment directly to the cloud lab through the iLab interface. iLab will then fetch the data when the experiment is over and annotated the data with light and timing information which the student can investigate and use to answer further test questions. Due to screen recording, the video player view on page 4 had flickering, which was filtered out for the purpose of clarity. The student can run as many experiments as she wants.
About this article
Design Guidelines and Empirical Case Study for Scaling Authentic Inquiry-based Science Learning via Open Online Courses and Interactive Biology Cloud Labs
International Journal of Artificial Intelligence in Education (2017)