10 simple rules for teaching wet-lab experimentation to computational biology students, i.e., turning computer mice into lab rats

摘要

Graduate students in computational biology have strong quantitative backgrounds but areoften quite limited in their understanding of the theory, approach, and practice of biologicalexperimentation. A strong grasp of the provenance of relevant biological data is essential forcomputational biologists to effectively critique and incorporate data into their research effortsor to generate the data themselves, the latter of which is becoming more and more prevalent[1,2]. To give students this knowledge, insight, and experience, the joint Carnegie Mellon–University of Pittsburgh PhD program in Computational Biology (CPCB) has developed theLaboratory Methods for Computational Biologists (LMCB) course as a core course in theCPCB curriculum to provide a hands-on, research-oriented laboratory experience in fourmajor areas: genomics, microscopy and bioimaging, high content screening, and X-ray crystal-lography. For each area, we have designed course modules covering general topics such asexperimental design, limitations of common methods, cutting-edge and high throughput tech-niques, potential sources of error in data, and the preservation, analysis, and presentation ofdata. To provide the students with a more immersive research experience, we have designedthe modules to cover one common research topic giving the students experience in usingresults from previous modules to inform the design of experiments for the next. The LMCBcourse provides foundational and experiential wet-lab training for the benefit of nascentcomputational scientists. Here, we provide some of the guiding principles and approaches thatwe’ve used to establish, evolve, and shape our course. While we use our course as the frame-work for the presented rules, they are broadly applicable and adaptable to similar graduate andundergraduate programs. Therefore, this article will appeal to anyone interested in approachesfor providing hands-on training in experimental techniques to computational trainees, partic-ularly to faculty and program directors of computational biology and related graduate orundergraduate programs that want to provide this foundational course-based research trainingto their students.