Carnegie Mellon University

Quantitative Cell and Molecular Biology Laboratory

Course Number: 02-261

This is an introductory laboratory-based course designed to teach basic biological laboratory skills used in exploring the quantitative nature of biological systems and the computational reasoning required for performing research in computational biology. Over the course of the semester, students will perform various experiments and computationally analyze the results of these experiments. Students will also use computation to design experiments based on the data they collect. During this course students will be using traditional, well-developed techniques as well as automated lab equipment to answer scientific questions: How should different sources of DNA in a specimen be identified? What changes do cells undergo during apoptosis? Understanding the results of these experiments will require students to think critically about the data they generate, the appropriate controls required to support their conclusions, and the biological context within which these results were obtained. During this course students will gain experience in many aspects of scientific research, including: designing and executing protocols for traditional and automated experiments, computational processing and analysis of collected results and communicating results to peers and colleagues.

Course Outline: (1) 3-hour lab per week (1) 1-hour lecture per week. 9 units (12 units for CB majors). This course counts as a CSD Science and Engineering requirement as well as the lab requirement, and Dietrich College’s Modeling/Science Gen Ed requirement.

NOTE: Computational Biology majors should register for the 12-unit version of 02-261. All other students should register for 9 units.

Semester(s): Fall, Spring
Units: 9 (12 of CB Majors)
Prerequisite(s): 02201 or 15112
Location(s): Pittsburgh



Learning Objectives

During this course, the students should learn to: 

  • Interpret, follow, and write a scientific protocol
  • Propose, design, and perform scientific experiments
  • Develop and apply computational methods for data analysis
  • Present scientific results to colleagues

Key Topics: 

  • Sequencing and analyzing DNA
  • Developing computational methods for designing and performing PCR
  • Maintaining cell cultures
  • Acquiring brightfield and fluorescent microscopy images
  • Designing computational methods for image analysis
  • Designing protocols for automated experiments
  • Communicating results to peers and colleagues