Syllabus

Course Meetings Times

Lectures: 2 sessions / week; 1.5 hours / session

Recitations: 1 session / week; 1 hour / session

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science, but not necessarily both. The scripting language Python—which is widely used for bioinformatics and computational biology—will be used; foundational material covering basic programming skills will be provided by the teaching assistants. Graduate versions of the course involve an additional project component.

Prerequisites

There are different prerequisites for the various versions of the course. See the table for clarification.


DEPARTMENT COURSE
NUMBER
LEVEL PREREQUISITES RECITATIONS ASSIGNMENTS PROJECT GRADING
7.01 7.05 or 5.07 6.00 or 6.01 18.440 or 6.041
Biology 7.36 Undergraduate Agree_2.jpg Agree_2.jpg Agree_2.jpg   Optional; review of lectures   No Grading scheme 1
7.91 Graduate Agree_2.jpg Agree_2.jpg Agree_2.jpg   Optional; review of lectures   Yes Grading scheme 2
Biological Engineering 20.390 Undergraduate Agree.jpg Agree.jpg Agree.jpg   Optional; review of lectures   No Grading scheme 1
20.490 Graduate Agree.jpg Agree.jpg Agree.jpg   Optional; review of lectures   Yes Grading scheme 2
Electrical Engineering and Computer Science 6.802 Undergraduate Agree_2.jpg     Agree_2.jpg Optional; review of lectures   No Grading scheme 1
6.847 Graduate Agree_2.jpg     Agree_2.jpg Required; cover additional material related to artificial intelligence Additional artificial intelligence problems will be assigned Yes Grading scheme 3
Health Sciences and Technology HST.506 Graduate Agree.jpg     Agree.jpg Optional; review of lectures   Yes Grading scheme 2

Textbook

This textbook is recommended for the course:
Buy at Amazon Zvelebil, Marketa J., and Jeremy O. Baum. Understanding Bioinformatics. Garland Science, 2007. ISBN: 9780815340249. [Preview with Google books]

The instructors have also selected the following texts as particularly useful in specific areas, if you are looking for more information:

Background in Python Programming

Buy at Amazon Ascher, David, and Mark Lutz. Learning Python. 2nd ed. O'Reilly Media, Inc., 2003. ISBN: 9780596002817. [Preview with Google Books]

Buy at Amazon Martelli, Alex. Python in a Nutshell. O'Reilly Media. 2006. ISBN: 9780596100469. [Preview with Google Books]

Background in Molecular Biology, Cell Biology, and Biochemistry

Buy at Amazon Watson, James D., Tania A. Baker, et al. Molecular Biology of the Gene. Benjamin Cummings, 2013. ISBN: 9780321762436.

Buy at Amazon Alberts, Bruce, Alexander Johnson, et al. Molecular Biology of the Cell. Garland Science, 2007. ISBN: 9780815341055.

Buy at Amazon Berg, Jeremy M., John L. Tymoczko, et al. Biochemistry. W. H. Freeman, 2008. ISBN: 9781429235020.

Buy at Amazon Branden, Carl I., and John Tooze. Introduction to Protein Structure. Taylor & Francis, Inc., 2000.

Buy at Amazon Petsko, Gregory A., and Dagmar Ringe. Protein Structure and Function. Oxford University Press, 2008. ISBN: 9780199556847.

Other Useful References for the Class

Buy at Amazon Leach, Andrew. Molecular Modelling: Principles and Applications. Prentice Hall, 2001. ISBN: 9780582382107.

  • An in-depth treatment of molecular mechanics and other modeling methods.

Buy at Amazon Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman and Hall / CRC, 2013. ISBN: 9781439837177. [Preview with Google Books]

  • An introduction to topics related to biological networks from a physicist's perspective.

Buy at Amazon Durbin, Richard, Sean R. Eddy, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998. ISBN: 9780521629713. [Preview with Google Books]

  • Contains more in-depth discussion of many of the algorithms and methods discussed in class.

Buy at Amazon Glantz, Stanton. Primer of Biostatistics. McGraw-Hill Medical, 2011. ISBN: 9780071781503. [Preview with Google Books]

  • Covers basic statistics with applications to biology and medicine.

Buy at Amazon Gonick, Larry, and Woollcott Smith. The Cartoon Guide to Statistics. Paw Prints, 2008. ISBN: 9781435242715.

  • A very useful introduction or refresher. Fun, but generally accurate.

Assignments

Five written or computer-based problem sets will be assigned. These are designed to promote deeper understanding of the principles and algorithms discussed in class and to provide hands-on experience with bioinformatics tools. The scripting language Python—which is widely used for bioinformatics and computational biology—will be used in the problem sets. More information is in the Assignments section.

Exams

There will be two 80–minute exams. The material covered on each exam is non-cumulative; consult the Calendar to see which topics were covered on each exam. There is no final exam.

Project

Students in one of the graduate versions of this course will complete a computational biology research project. See the table for clarification. The project is designed to give you practice in applying computational methods to contemporary problems in biology. Students design and carry out projects working in a group or by themselves. All students will provide online feedback (peer review) on the presentations. More information is in the Project section.

Grading

The different versions of this course have different grading schemes. See the table for clarification. An additional 1% extra credit may be awarded for exceptional class participation.

Grading Scheme 1

ACTIVITIES PERCENTAGES
Problem Sets (out of 100 points) 36
Exams 62
Peer Review 2

Grading Scheme 2

ACTIVITIES PERCENTAGES
Problem Sets (out of 100 points) 30
Exams 48
Project 20
Peer Review 2

Grading Scheme 3

ACTIVITIES PERCENTAGES
Problem Sets (out of 100 points) 25
Extra Artificial Intelligence Problems 5
Exam 48
Project 20
Peer Review 2