Computational evolutionary biology

Lectures: 2 sessions / week, 1.5 hours / session

Computational evolutionary biology draws, naturally enough, from both the disciplines of evolutionary biology and computer science. We expect participants to have some familiarity with at least the basics of molecular biology; elementary probability theory, calculus and differential equations; and some facility in at least one programming language and/or mathematical modeling.

The material covered in this course is selected in such a way that at its completion, you should be able to understand current papers in the field of computational evolutionary biology.

Rice, Sean. Evolutionary Theory: Conceptual and Mathematical Foundations. Sunderland, MA: Sinauer, 2004. ISBN: 9780878937028 .

Hall, Barry.Phylogenetic Trees Made Easy. Sunderland, MA: Sinauer, 2004. ISBN: 9780878933129 .

Hartl, D., and A. Clark. Principles of Population Genetics. 3rd ed. Sunderland, MA: Sinauer, 1997. ISBN: 9780878933068 .

Futuyma, D. Evolutionary Biology. 3rd ed. Sunderland, MA: Sinauer, 1997. ISBN: 9780878931897 .

Felsenstein, J. Inferring Phylogenies. Sunderland, MA: Sinauer, 2003. ISBN: 9780878931774 .

Ewens, W. Mathematical Population Genetics. 2nd ed. Vol. 1. New York, NY: Springer-Verlag, 2004. ISBN: 9780387201917 .

Weiss, K., and A. Buchanan. Genetics and the Logic of Evolution. New York, NY: Wiley-Liss, 2004. ISBN: 9780471238058 .

For those seeking a review of relevant biology, see Weiss and Buchanan. Futuyma provides an excellent overall view, while Hartl and Clark serves as a standard reference for population genetics. For those desiring more mathematical depth, see Felsenstein and Ewens.

This course is discussion and lab oriented. That is, the work of the course is done via active class participation and a series of laboratory exercises. There are no exams, in particular, there will be no final exam. Class participation will be encouraged in part by dividing each class period into two parts: the first a more standard lecture, and the second a more interactive session that will be led by class members in reaction to questions and readings appropriate for that day's topic. Laboratories will be handed out approximately every two weeks. The final project will involve an element of non determinism, i.e., so-called 'free will', in that you will be able to choose your own project and combine elements from the previous laboratories, or do something completely new. We will get started on the final projects early, since we aim for your team to present your project results in class. For the final project, we will have people work in teams of 2 or 3 (but not more, and at my urging, not fewer - solos are discouraged, but, like all labs, collaboration is encouraged - see below).

The laboratory exercises are designed to be carried out mostly on your own personal computer, or alternatively on an MIT server. The software, along with related software you may find helpful, is given in the labs section. Generally, you will be able to download the software and datasets to your own computer to do the laboratory exercises.

The assignments are due at the end of class on the due date, i.e., 4PM on the specified date.

Please construct (simple) Web pages for your lab reports, and email the root URL to me. If you do not know how to construct Web pages like this one, ask us - it is not hard to learn.

You have up to 30 (thirty) late days to use up, that can be distributed among your laboratory projects. However, the last project must be turned in during the last week of class, even if you have not used all of your days by then.

Once you use up your late days, late projects will not earn any points, even though they might be considered in borderline cases for the final grade. Thus try to turn in all projects, even though you might feel they are not to be counted. If you do not turn in a final (joint) project, you will receive an I (incomplete) for the class, and will have to make this up by next term (the incomplete will note that 80% of the coursework has been completed).

Cooperative work is strongly encouraged; you are free to work together on laboratory assignments. However, aside from the final project, you must write up and turn in your own work. Please write the names of the people with whom you worked at the top of the first page. Exact copies of laboratory reports will not be acceptable. (Something other than your name and those of your co-workers must be different!) The aim of the course (and its pedagogical philosophy) is to learn about computational evolutionary biology. You will learn more if you actually do the laboratory assignments.

Final grades will be determined on the basis of the following weighting scheme.

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