Wetlab
To streamline our work in the lab, we worked with our faculty advisor to develop a series of videos, quizzes, and lab sheets that corresponded to each step in the cloning workflow. The videos were required to be watched ahead of time, to gain a familiarity with the protocol, and the quiz had to be completed. Each wetlab trainee printed out the lab sheet for the day, filled it out as the procedure was completed, and turned it in. This allowed us to document the progress of each trainee, as well as any potentially contaminated aliquots that were resulting in inconclusive results. Ultimately, the implementation of this notebook enables wetlab workflow to be compatible with an atypically large group of student researchers.
One complete experimental trial lasts about two weeks, taking 1 hour in lab each day, allowing flexibility in scheduling:
- Week 1
- Day 1: Experiment Introduction
- Day 2: Polymerase Chain Reaction (PCR)
- Day 3: Gel electrophoresis confirmation and DNA purification
- Day 4: Golden Gate assembly
- Week 2
- Day 5: Transformation
- Day 6: Liquid culture inoculation
- Day 7: Miniprep and sequencing
Computational Biology
The computational trainings were first developed by each of our projects, as onboarding resources and experiment design were primarily spearheaded by the project leads with iterations of feedback from the directors. This development process took about three weeks to aggregate resources, relevant papers, as well as testing software functionality and sample datasets; designed notebooks were expected to take no more than 8 hours to complete.
At the onset of the trainings, students were then given two weeks to complete their necessary notebooks, including reading the paper, walking through the introductions to their project and necessary softwares, and learning how to troubleshoot code or such software by testing it on shared data. Upon the completion of these introductory components, students were then tasked with completing their mini-experiment, generating a novel code implementation of an important task in their team’s actual project, or learning how to generate protein candidates and analyze them through a few selected quantitative tools. Project leads monitored progress and checked in with students, fostered collaborative sessions for students to work through the notebooks together, and assisted students with software downloading and setup. Because of the entirely computational, and thus drylab, nature of these notebooks, students were free to work on any components of the notebook at any point during the two weeks, break up the various components, and troubleshoot runs in groups with ease; this last component was greatly facilitated by the collaborative work sessions the project leads organized. Project leads also aggregated students’ data, code, and initial analyses, to do further benchmarking and analyze results in totality. This analytical period took place after the conclusion of the two week student sprint.
The full Jupyter notebook walkthroughs are available on our GitHub.
