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DD2450 Algorithmic Bioinformatics 6.0 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus DD2450 (Autumn 2009–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

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

Intended learning outcomes

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.

Literature and preparations

Specific prerequisites

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

Recommended prerequisites

One of the courses 2D1353/DD1352 Algorithms, Data Structures, and Complexity and DD2354 Algorithms and Complexity or DD2352 Algorithms and Complexity or the equivalent. Contact the instructor to find out if other courses may suffice.


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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.

Examination and completion

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

Grading scale

A, B, C, D, E, FX, F


  • 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.

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see:

Other requirements for final grade

Written exercises (OVN1; 6 university credits).

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • 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.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Biotechnology, Computer Science and Engineering, Information Technology, Information and Communication Technology

Education cycle

Second cycle

Add-on studies

Please discuss with the instructor.


Lars Arvestad, e-post: