Laboratory Methods for Computational Biologists
Course Number: 02-760
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 one-semester laboratory course has been developed to give students a deep appreciation of the principles and challenges of biological experimentation. Students will explore a range of topics, including structural biology, genomics, proteomics, and bioimaging. Each broad topic is covered over a period of 3-4 weeks. Many lectures and labs are hosted by faculty who are experts in the field. Students are required to keep a detailed laboratory notebook, summarizing the goals of the experiment, critical steps, and 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, take-home exams, and a final project.
This course is specifically for CPCB Ph.D. students. Any other students interested in taking the course need Instructor permission.
Key Topics:
- Genomics
- Bioimage Informatics
- High Content Screening
- Structural Biology
Semester(s): Fall, Spring
Units: 6
Learning Objectives
By the end of this course, the students should be able to do the following:- Understand and describe basic theoretical concepts of wet-lab experimentation.
- Plan, organize, and execute (and sometimes repeat) an experiment to answer a research question
- Maintain a thorough laboratory notebook
- Analyze, interpret, and critique experimental data
- Present and discuss experimental results
Assessment Structure:
Notebooks, lab-related assignments and participation