Courses in the Department of Computer Science
CSC 2417H Algorithms for Genome Analysis
This graduate course will cover some exciting algorithms that have been developed to analyze genomic and functional data, including Genome comparison and assembly, gene prediction, localization of regulatory elements in the genome, and analysis and comparison of biological networks. While the emphasis of the class will be on discrete algorithms, we occasionally will talk about probabilistic models (such as HMMs), and the interplay between discrete and probabilistic models. The course is intended for computer science graduate students, and all of the required biology will be explained in the class. Students in biological and related sciences with a strong computational background are encouraged to participate.
CSC 2418H Computational Structural Biology
In the post-genomic era, several key problems in molecular biology center on the determination and exploitation of three-dimensional protein structure and function. This graduate course will cover the computational aspects of Structural Biology - the modeling and computer simulation of structure, function, and dynamics of biological molecules. We will study algorithms to facilitate protein structure determination by X-Ray Crystallography and Nuclear Magnetic Resonance (NMR) Spectroscopy, Protein-Protein Interactions, Computer-Assisted Pharmaceutical Design, and Structure/Function Analysis. In addition to covering historic and contemporary algorithms, we will discuss open problems plaguing the field. The course is intended for computer science graduate students, and all the required biology will be explained in the class. Students in biological and related sciences with a strong computational background are strongly encouraged to participate.
CSC2431H Topics in Computational Molecular Biology
Fall 2008 - This seminar based course will begin (first two weeks) with an
introduction to three open problems in computational biology followed by
a few background lectures on relevant biology. We will then transition
to tackling two or three of the presented problems as a group. We will
read appropriate grounding literature on all three projects and then
explore solutions through the proposal of ideas from various
computational subdisciplines. Students in the class are encouraged to
consider how their research areas can be used in tackling these
problems. The topics this year include: choice of synonymous 'words' in
gene coding, geometric feature identification in protein structure, and
computer aided drug discovery. These problems are most likely to
manifest as challenges in machine learning, machine vision,
computational geometry, statistics, and of course computational biology.
Although a formal background in biology is not required, students with a
more recent exposure to biology or computational biology (i.e.
CSC2417/CSC2418) will have an easier time grasping key biological
relevance. Contact instructor if you're unsure.
The topics discussed in this course vary from year to year and generally depend on the instructor.
Graduate Bioinformatics Courses Outside the Dept of Computer Science
BME1412H Engineering Models in Biology
BME1413H Biological Communication Processes
JTB2010H Proteomics and Functional Genomics
JTB2020H Applied Bioinformatics
MBP1010H Quantitative Biology - Statistical Methods
MBP1011H Foundations of Bioinformatics
MBP1024Y Advanced Medical Imaging
MIE1511 Data Integration in Life Sciences
Computational Biology students usually take many of the graduate classes offered by the Department of Computer Science.