DD2450 Algorithmic Bioinformatics 6.0 credits

Algorithms for problems such as alignment, phylogeny, sorting by reversals. An introduction to Hidden Markov Models.

After successfully taking this course, students will be able to:

• give their own account gene regulation, the central dogma, mutations that affect the genome, and experimental high-throughput techniques,
• implement, describe, and discuss the algorithms treated during the course as well as how they relate to each other,
• apply the fundamental algorithm design methodologies dynamic programming, MCMC, and EM to problems in computational biology and bioinformatics,
• apply, describe, and discuss the modeling principles parsimony, maximum likelihood, and bayesian modelling.

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B or equivalent.

To be announced at least 4 weeks before course start at the web page for the course. Previous year material produced at the department was used.

• ÖVN1 - Exercises, 6.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Written exercises (OVN1; 6 university credits).

Lars Arvestad

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.