02-250 Introduction to Computational Biology

• 02-250 is a required course that is recommended to be taken in the spring of the second year (after taking 02-201, 15-110 or 15-112).

Computational Biology Minors

• 02-250 OR 02-251 is required for the minor and should be taken early enough to be able to take the advanced elective course.

• DNA and RNA sequence analysis
• Sequence databases
• Gene expression analysis
• Genome assembly
• Binding site prediction
• Phylogenetics and human genetic variation
• Difference and differential equation-based biological models
• Biochemical, neuronal, cell cycle models
• Compartmental models and pharmacodynamics
• Biological image analysis

• Basic familiarity with cell and molecular biology (e.g., 03-121 Modern Biology or Biology Advanced Placement), including the central dogma, Mendelian genetics, cell structure, biochemical reactions, and the cell cycle
• Familiarity with concepts of imperative (procedural) programming (prerequisite: 02-201, 15-110, or 15-112), including flow control, loops, function and arguments, data types, program structure and modularity
• Ability to write short programs in a procedural language, preferably Python, and familiarity with basic Unix commands (cd, ls, cp, mv)

• Grades are determined 50% by homeworks, 10% by participation in recitations, 15% by a midterm exam, and 25% by a final exam.
• Homework is assigned in class each Friday and is due by 10:30 am on the due date (usually the following Thursday). All assignments are submitted electronically through Canvas. 3 late days total can be used without penalty during the semester, but only 1 late day can be used per assignment. There are no partial late days. Late homeworks will not otherwise be accepted unless you have made prior arrangements for an extension.  Please note that extensions will only be granted under exceptional circumstances.

1. Learn major biological data types, the methods by which they are produced, and their uses.
2. Learn to critically assess the reliability of biological data sources.
3. Learn essential concepts of statistics and algorithms needed to productively use database search and inference tools and interpret their results.
4. Learn to synthesize results from different data sources and select sources appropriate to a given problem.
5. Learn about of the major repositories of biological data and the tools to access them.
6. Learn to independently research a biological question using online resources.
7. Learn how to pose biological questions through mathematical models and reason about the assumptions and limitations of those models.
8. Learn to simulate the behavior of simple biological models.
9. Learn basic image processing methods and concepts of biological image analysis.
10. Learn concepts behind supervised and unsupervised machine learning as applied to biological problems.

PiazzaAutolab

Instructors and TA office hours

Prof. Pfenning Tuesdays 10AM-11AM GHC 7711                        Prof. Murphy Mondays 3:30-5:00 GHC 7723
Easwaran Ramamurthy  Wednesdays 5:00-7:00                      NSH 1505
Allyson Lawler  Tuesdays Tuesdays 3:00-5:00
MI 290

Pre-requisites:

02-201, 15-110, or 15-112 AND 03-121 or 03-131 or 03-151

Anti-Requisites:

03-252, 03-251, 02-252, 02-250, 02-251, 02-252