Computational Biology – Spring 2017

Botany 4550/5550 and Computer Science 5010

Instructor: Alex Buerkle
Class meetings: Enzi STEM Building (north of Lewis Street) Room 155, 3:10–5:00 p.m. on Tuesday and Thursday

During the semester, dynamic content for the course will be available on the Course wiki (restricted to class participants).

Course syllabus (spring 2017, pdf)

Most subdisciplines in modern biology involve the analysis of large amounts of data, inference based on probabilistic models, or both. This course exists to help students gain skills in data analysis and combines elements of applied computational science (data wrangling, computer systems, etc.) with probability and statistics. These are challenging aspects of modern research and can take years to master, which might be one reason why some people neglect to develop their skills in this area. However, you will be able to learn and do new useful things very quickly.

Computational biology can open a world of possibilities for research and employment. The computational and analysis skills we will practice are increasingly imperative for modern biologists (e.g., see the mention of computation, statistics and quantitative analysis in many job advertisements). Without them the scope and ambition for biological research are unnecessarily constrained. In addition, the problems and computational approaches we will study are good application domains for students from outside biology who are interested in pursuing computational science. This course will be motivated by practical applications of probability, simple mathematics and computational tools to biology. In each section of the course we will begin with biological questions and then investigate computational methods for graphical and statistical analysis of real data sets.

I have a few main goals in this class: 1) to discover the importance of probability, mathematics and computational methods in biology, 2) to understand philosophies and conceptual frameworks underlying commonly-used statistics and analytical methods, and 3) to become proficient with analytical and computational tools that can be applied to biological problems. I hope that by the end of the course you will have an appreciation of the diversity of applications of these analytical tools and the role they play in modern biology, and have begun to develop proficiency in these areas.

Required course materials:

  1. Jones, O., R. Maillardet, and A. Robinson. 2014. Introduction to Scientific Programming and Simulation Using R. Second Edition.
    This book will be available from the UW and online bookstores, possibly including some used copies. The full book is also available for free through the UW Library (click View Full Text on the following page and follow the link). Please find access to the Jones book (2nd edition) in the first week of classes, so that you can complete the reading assignments. I believe there is value in owning a paper copy of the book.
  2. Additional materials will be distributed in class or linked from the course website.