CourseSpecifications
Valid as from the academic year 2024-2025

Computational Biology (C003789)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
Dutch
Gent

Lecturers in academic year 2024-2025
Dawyndt, PeterseminarlectureWE02

Offered in the following programmes in 2024-2025
Bachelor of Science in Computer Science
Master of Science in Teaching in Science and Technology(main subject Mathematics)
Master of Science in Mathematics

lecturer-in-charge
crdts
666offeringAAATeaching languages
Dutch
Keywords
Computational methods, molecular biology, genome structure, gene prediction, sequencealignment, phylogenetics, comparative genomics, gene expression analysis
Position of the course
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use
their internal clock? To what extent do bacterial species exchange genetic material? The
genomic revolution in biology enables us to answer such questions. But the revolution would
have been impossible without the support of powerful computational and statistical methods
that enable us to exploit genomic data (1.3). Solving the current and future problems in thisintriguing field (1.2) requires training of a next generation of researchers that are fluent in thelanguages of mathematics, computer science and biology (3.1,3.2,3.5).
Discover the world of computational genomics through a number of real case studies that areworked out in Matlab/Python. These cases will allow the students to gradually work themselves
into the terminology and basic principles of molecular biology, and give them a flavour of thedifferent computational techniques used in this ever-expanding research domain (2.1,2.2,2.4).
This course provides a roadmap to navigate entry to this field (1.6,5.1,5.5). It guides thestudents through key achievements of bioinformatics, using a hands-on approach. Statisticalsequence analysis, sequence alignment, hidden Markov models, gene and motif finding andmore (1.1), are introduced in a rigorous yet accessible way.
Contents• Introduction to cell biology• Sequence statistics• Gene finding• Sequence alignment• Probabilistic sequence models (hidden Markov models)• Genetic variation within and between species• Evolution: natural selection at the molecular level• Phylogenetic analysis• Whole genome comparisons• Gene expression patterns• Identification of regulatory sequences(Approved)

1Initial competences
An understanding of basic probability theory, algorithmic concepts and programming skills areessential. Prior experience in the Python programming language and being able to work withmodules for scientific computing (numpy, scipy, matplotlib) are required. No previous
knowledge of molecular biology will be assumed, as time will be spent throughout the course toelaborate on the biological background. It is primordial to have an certain interest in thebiological aspects of the problem domain.
Final competences
1 Understand statistical and algorithmic approaches to sequence and gene expression analyis.
2 Understand why and how mathematics and computer science can play a role in modern
1 biology.
3 Read and understand recent articles on genomic topics, and in particular assess the data1 analysis aspects of them.
4 Be familiar with the standard tools and problems, and current and future goals of the
1 research domain of computational biology.
5 Access and manipulate real genomic data.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture
Extra information on the teaching methods
Guided practical sessions in PC room, with regular reporting of the executed analysis.
Study material
Type: Handbook
Name: Bioinformatics algorithms: an active learning approach
Indicative price: Free or paid by faculty
Optional: yes
Language : English
Author : Compeau P, Pevzner P
ISBN : 978-0-99037-461-9
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Usability and Lifetime within the Course Unit : one-time
Usability and Lifetime within the Study Programme : one-time
Usability and Lifetime after the Study Programme : occasionally
Type: Slides
Name: Slides shown during the lectures.
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
ReferencesDurbin R., S. Eddy, A. Krogh en G. Mitchinson. Biological Sequence Analysis - Probabilistic
models of proteins and nucleic acids, Cambridge University Press, 1998. (ISBN-13: 9780521629713 | ISBN-10: 0-521-62971-3)
Felsenstein J., Inferring Phylogenies, Sinauer Associates, 2003. (ISBN-13: 978-0878931774 |ISBN-10: 0-878-93177-5)
Gusfield D., Algorithms on Strings, Trees, and Sequences: Computer Science andComputational Biology, Cambridge University Press, 1997. (ISBN-13: 978-0521585194 | ISBN10: 0-521-58519-8)
Jones N.C. en P.A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press,Cambridge, MA, 2004. (ISBN-13: 978-0262101066 | ISBN-10: 0-262-10106-8)(Approved)2

Setubal C. en J. Meidanis, Introduction to Computational Molecular Biology, PWS Publishing,
1997. (ISBN-13: 978-0534952624 | ISBN-10: 0-534-95262-3)
Mount D.W., Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor Laboratory
Press, 2001. (ISBN-13: 978-0879697129 | ISBN-10: 0-879-69712-1)
Course content-related study coaching
Algorithmic aspects of the computational biology that are touched upon during the lectures, arefurther illustrated by means of a series of case studies that are worked out in Matlab or Python.
Some case studies are an integral part of the lectures, while others need further elaboration by
the students during practical sessions in a PC-room or as obligatory projects. The electronic
learning-environment Ufora plays a central role in the guided self-learning process of thestudents, as it enables the exchange of feedback and additional study material between
lecturer and students.
Assessment moments
continuous assessment
Examination methods in case of periodic assessment during the first examination period
Examination methods in case of periodic assessment during the second examination period
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Extra information on the examination methods
Students are assessed by the combination of a series of programming assignments(permanent evaluation: 60%) and an individual project (exam: 40%).
Calculation of the examination mark
Permanent evaluation (60%) and project elaborated in exam period (40%). At least half of thepoint must be obtained for both components: permanent evaluation and project elaborated inexam period. If at least half of the points was obtained for only one of the two components, thefinal score is based only on the other component, which is then weighted 100%.
For the second examination period, alternative assignments are indicated for the four
assignments (other than those from the first examination period). The project just stays thesame. Students can retake each of these assignments/project in the second examination
period. If they don't retake an assignment, they keep their score for that assignment fromthe first exam period.

(Approved)3