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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
TDA507 - Computational methods in bioinformatics
 
Syllabus adopted 2014-02-25 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Computer Science and Engineering, Information Technology
Department: 37 - COMPUTER SCIENCE AND ENGINEERING


Teaching language: English
Open for exchange students
Block schedule: B

Course elements   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0113 Written and oral assignments 7,5c Grading: TH   7,5c    

In programs

MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 2 (elective)
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (elective)

Examiner:

Bitr professor  Graham Kemp

Replaces

TDA505   Three-dimensional structure TDA506   Structural bioinformatics

Course evaluation:

http://document.chalmers.se/doc/7a4a04eb-03d6-4506-8c42-8f31e6a20665


  Go to Course Homepage

 

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Essential: an introductory programming course.
Essential: mathematics (including discrete mathematics).
Desirable: data structures.
Desirable: programming skills in C. However, knowledge of Java or another procedural programming language should be sufficient to enable students to read and modify example C programs in practical classes.
Desirable: familiarity with some basic chemistry concepts (including atoms and molecules, chemical bonding).

Aim

This course demonstrates how computational methods that have possibly been presented in other computing courses can be applied to solve problems in an application area.

We look at problems related to the analysis of biological sequence data (sequence bioinformatics) and macromolecular structures (structural bioinformatics). Computing scientists need to be able to understand problems that originate in areas that may be unfamiliar to them, and to identify computational methods and approaches that can be used to solve them. Biological concepts needed to understand the problems will be introduced.

This is an advanced level course which uses research articles as the main reference materials. Reading research articles is valuable training for scientists and researchers. These demonstrate how to present ideas and methods, and how to critically evaluate them. Developing skill in reading research articles is useful preparation for future scientific investigations, and one's own scientific writing can improve through reading.

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding
* describe bioinformatics problems and computational approaches to solving them
Skills and abilities
* implement computational solutions to problems in bioinformatics
Judgement and approach
* summarise problems and methods described in research articles
* critically discuss different methods that address the same task
* identify situations where methods can be applied across different application areas.

Content

Computational methods and concepts featured in this course include: dynamic programming; heuristic algorithms; graph partitioning; image skeletonisation, smoothing and edge detection; clustering; sub-matrix matching; geometric hashing; constraint logic programming; Monte Carlo optimisation; simulated annealing; self-avoiding walks.

Biological problems featured in this course include: sequence alignment; domain assignment; structure comparison; comparative modelling; protein folding; fold recognition; finding channels; molecular docking; protein design.

Organisation

Lectures and practicals.

Literature

Lecture handouts; web-based resources; selected research articles.

Examination

Assignments. Grading: Not Passed, 3, 4, 5.


Published: Fri 18 Dec 2009. Modified: Mon 28 Nov 2016