Computational biology

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1 hour / session

6.006 Introductions to Algorithms

6.041SC Probabilistic Systems Analysis and Applied Probability

7.01SC Fundamentals of Biology

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Genomes: Biological sequence analysis, hidden Markov models, gene finding, comparative genomics, RNA structure, sequence alignment, hashing

Networks: Gene expression, clustering / classification, EM / Gibbs sampling, motifs, Bayesian networks, microRNAs, regulatory genomics, epigenomics

In addition to the technical material in the course, the term project provides practical experience doing these things:

There will be five problem sets during the semester, each including 3–5 problems for all students and a lab problem which is optional for undergraduate students. The problem sets will include both theoretical and programming problems. For programming problems, we provide skeleton code in Python, but you are welcome to write solutions in any language.

There will be one quiz, in class, which will cover all material covered up to that point. There will be no final exam. The quiz will include true / false questions, short answer questions, practical problems using algorithms covered in class, and one or two problems extending ideas seen in class.

Students will work on a final project with deliverables due at several milestones during the term as marked on the course schedule. The first part of the term will be spent identifying a topic relevant to the course materials, planning the project, writing an NIH-style proposal, and reviewing the proposals of your peers. The second part of the term will be focused on completing the project, writing the report, and presenting the results. Details of what is expected by each milestone will be posted on the course website.

You may either work alone or with one partner; however, teams and graduate students will be expected to undertake more ambitious projects. Part of the final project grade will depend on the challenge and originality of your project.

We anticipate projects of a few types:

Each student will contribute to the scribe notes, which are chapters of the course textbook. Several students may be assigned to work together on a single lecture / chapter depending on course enrollment. As a scribe, you are expected to do the following:

The course textbook is the compiled scribe notes. The entire course textbook is available in the readings section.

You may also find the following optional texts helpful:

Durbin, Richard, Sean R. Eddy, Anders Krogh, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.

Jones, Neil C., and Pavel Pevzner. An Introduction to Bioinformatics Algorithms. MIT Press, 2004. ISBN: 9780262101066. [Preview on Google Books]

Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern Classification. John Wiley & Sons, 2003. ISBN: 9789814126021.

Recitations will be held on Fridays, during which we will both review the lecture material and discuss additional aspects of it. Since there is only one recitation section, we will not be able to accommodate all scheduling conflicts. Therefore, attendance is not mandatory. Material in the recitation notes may appear on the quiz.

You are welcome to collaborate on problem sets and the final project. However:

Don't show me this again

This is one of over 2,200 courses on OCW. Find materials for this course in the pages linked along the left.

MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum.

No enrollment or registration. Freely browse and use OCW materials at your own pace. There's no signup, and no start or end dates.

Knowledge is your reward. Use OCW to guide your own life-long learning, or to teach others. We don't offer credit or certification for using OCW.

Made for sharing. Download files for later. Send to friends and colleagues. Modify, remix, and reuse (just remember to cite OCW as the source.)

Learn more at Get Started with MIT OpenCourseWare