DNA chips and micro-arrays have emerged over the last several years as powerful tools to measure the expression levels of thousands of genes in a living cell or tissue. For the first time, these techniques give a comprehensive picture of the levels of all genes simultaneously. The challenge of understanding and using this data raises very exciting and challenging mathematical problems. The course will deal with new and emerging techniques for analyzing such data. Depending on our progress, some or all of the following topics will be covered:

The course requires no prior knowledge in biology. All background will be provided in the lectures.

The course is open to all graduate students in computer science. No prerequisites are needed beyond graduate standing in CS. Interested undergraduate students, as well as non-CS students, should contact the instructor.

As some of the cutting edge research in the field of gene expression analysis is carried out in Israel, the course will include several guest lectures by leading experts in the field.

Course Outline

• Introduction: basic biological concepts, DNA chips technology
• Universal DNA arrays
• Pevzner's algorithm, error-prone models,
• Spectrum alignment
• Universal tag arrays
• Clustering algorithms
• Classical clustering algorithms: hierarchical, k-means, self organizing maps, principal components analysis
• HCS, CLICK, BioClust
• Applications: gene families, finding promotes, etc.
• Classification
• Supervised methods
• Feature selection
• Biclustering
• Church's technique, Getz et al.
• SAMBA
• Genetic networks
• Kauffman's model
• Binary network models
• complexity
• Network reconstruction
• Bayesian methods
• Experiment design