Students are expected to have taken the following courses prior to entering the CBB program:
- Mathematics through at least differential equations and linear algebra
- A course in probability and at least one other course in statistics
- Courses in programming, data structures, and algorithms
- A course in genetics
- A course in cell and molecular biology
- A course in biochemistry or organic chemistry
Otherwise competitive students lacking one or more of these requirements will be expected to satisfy these prerequisites prior to or during their first year.
Computational biology is a dynamic, broad, relatively new, and rapidly evolving field, and the CBB program is designed with this in mind. As a result, the program does not have a heavy emphasis on required courses, although all CBB students must complete one course approved by the CBB Curriculum Committee in each of the following areas:
- Quantitative Reasoning (3 graded credits)
- Predictive & Mechanistic Modeling (3 graded credits)
- Algorithmic & Computational Foundations (3 graded credits)
- Biological & Clinical Context (6 graded credits)
- Enrollment in the Journal Club (CBB 511, 4 x 1 graded credits) and Research Seminar (CBB 510S) for the first four semesters
Quantitative Reasoning (3 Credits Required):
Learning Objective: The student will develop the ability to select, apply, and interpret appropriate statistical models in the context of interdisciplinary biomedical research.
Primary Recommendation:
CBB 540 / STA 613 Statistical Methods for Computational Biology
Approved Alternatives:
| Course Number | Course Name |
|---|---|
| BIOSTAT 905 | Linear Models and Inference |
| BIOSTAT 906 | Statistical Inference |
| BIOSTAT 915 | High-dimensional Statistics and Machine Learning |
| BME 580 | An Introduction to Biomedical Data Science |
| CEE 580 | Data Science and Machine Learning for Engineers |
| ECE 685D | Deep Learning |
Other courses require approval by the DGS
Predictive and Mechanistic Modeling (3 Credits Required):
Learning Objective: The student will build and analyze predictive and mechanistic models that integrate biomedical complexity with computational and mathematical frameworks.
Primary Recommendation:
CBB 561 Computational Sequence Biology
CBB 914 Graphical Models for Biological Data
Approved Alternatives:
BIOSTAT 710 Statistical Genetics and Genetic Epidemiology
BIOSTAT 826 Deep Learning for Health Data
Other courses require approval by the DGS
Algorithmic & Computational Foundations (3 Credits Required):
Learning Objective: The student will acquire foundational algorithmic skills and computational techniques for efficiently processing and analyzing large-scale biomedical data.
Primary Recommendation:
COMPSCI531D Introduction to Algorithms
Approved Alternatives:
CBB 634 Genetic Algorithms
CBB 663 Algorithms in Structural Biology and Bioinformatics
COMPSCI 565 Modern Optimization for Statistical Learning
Other courses require approval by the DGS
Biological & Clinical Context (6 Credits Required):
Learning Objective: Develop biological or clinical insight sufficient to pose and refine computational research questions in real biomedical contexts.
Approved Courses:
| Course Number | Course Name |
|---|---|
| CBB 663 | Algorithms in Structural Biology and Biophysics |
| CBB 574 | Modeling & Engineering Gene Circuits |
| MGM 732 | Human Genetics |
| BME 503 | Computational Neuroengineering |
| BME570L | Introduction to Biomolecular Engineering |
| CBB 561 | Computational Sequence Biology* |
| BIOSTAT 826 | Deep Learning for Health Data* |
| CBB 520 | Genome Tools & Technologies |
Other courses require approval by the DGS. This category, in particular, may require customization to each student’s research interests, and it is recommended that students discuss appropriate courses with the advisory committee and mentors.
*These courses can only be used in this category if not used to satisfy another one.
Most students take a broad set of elective CBB courses as they define their own paths through the program. The Advisory Committee and the Dissertation Committee work with the student to design a series of elective courses to take beyond the core courses. Acceptable electives in other departments include statistical, computational, and biological course offerings that support each student’s specific research interests.
CBB students invite faculty speakers from on and off campus and organize the Computational Biology Seminar. All first- and second-year CBB students register for seminar and journal club for course credit. Students in their third year and beyond present their research as part of this series.
During their first year, students complete three or four research rotations with CBB faculty members. These rotations introduce the students to the flavor of research in a particular group and may be structured as a self-contained research project, a tutorial-level independent study, or an experience working in an experimental laboratory. Students are required to select one rotation with a primarily experimental faculty member, and one with a primarily computational faculty member. Each rotation lasts roughly 8 weeks, typically two in the fall and two in the spring of the first year.