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Joint CMU-Pitt Ph.D. Program in Computational Biology
Robert F. Murphy and Ivet Bahar, Directors
Curriculum - Core Course
02-760 Laboratory Methods for Computational Biologists
Computational biologists frequently focus on analyzing and modeling large amounts of biological data, often from high-throughput assays or diverse sources. It is therefore critical that students training in computational biology be familiar with the paradigms and methods of experimentation and measurement that lead to the production of these data. This new, one-semester laboratory course has been developed to give students a deep appreciation of the principles and challenges of biological experimentation. Touches upon a range of topics, including structural biology, genomics, proteomics, and bioimaging. A different laboratory method is covered each week, in the lab of a host faculty member who uses that method. The theory and practical aspects of each method are covered during a lecture session prior to each lab session. Students are required to submit a short lab report each week, summarizing the goals of the experiment, the critical steps and sources of error, and the analysis of the resulting data. With an emphasis on instrumentation and high-throughput data collection, this course is appropriate for students who have never taken a traditional undergraduate biology lab course, as well as those who have.
Grading: Letter grade based on class participation, quizzes, and lab reports.
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