To provide the students with a more immersive research experience, we have designed the modules to cover one common research topic giving the students experience in using results from previous modules to inform the design of experiments for the next. For each area, we have designed course modules covering general topics such as experimental design, limitations of common methods, cutting-edge and high throughput techniques, potential sources of error in data, and the preservation, analysis, and presentation of data. To give students this knowledge, insight, and experience, the joint Carnegie Mellon–University of Pittsburgh PhD program in Computational Biology (CPCB) has developed the Laboratory Methods for Computational Biologists (LMCB) course as a core course in the CPCB curriculum to provide a hands-on, research-oriented laboratory experience in four major areas: genomics, microscopy and bioimaging, high content screening, and X-ray crystallography.

A strong grasp of the provenance of relevant biological data is essential for computational biologists to effectively critique and incorporate data into their research efforts or to generate the data themselves, the latter of which is becoming more and more prevalent. Graduate students in computational biology have strong quantitative backgrounds but are often quite limited in their understanding of the theory, approach, and practice of biological experimentation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: The LMCB class is a core course in the CMU-Pitt PhD Program in Computational Biology, which is supported by an NIBIB T32 award. PLoS Comput Biol 16(6):Įditor: Scott Markel, Dassault Systemes BIOVIA, UNITED STATESĬopyright: © 2020 Ayoob, Kangas. Citation: Ayoob JC, Kangas JD (2020) 10 simple rules for teaching wet-lab experimentation to computational biology students, i.e., turning computer mice into lab rats.

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