Computing Science - BSc & Advanced MSc / MSci option, 2007-2008

Module home page:

David Gilbert []

Course definition, and What are the differences in this module between the H and M levels?

Why study this module?

Related course Advanced Research Readings in Computer Science - Bioinformatics and Systems Biology (campus access only). You are welcome to attend!

Announcement: SIMAP Computational Workshop 28 & 29 january, open to all!

Timetable: Term 2, 2007-2008.
Lectures: Tuesday, 15.00-16.00, Bioinformatics Research Centre (Note change from University Gardens 7:101);
Thursday 15.00-16.00, Boyd Orr Lecture room E (note change)
Lab: Thursday, 16.00-17.00, computer lab, Boyd Orr 618 (level 4)

Note You will need to ensure that you have the line
source /usr/local/lab/lab.env
in your ~/.cshrc in order to use all of the software in the practicals.
Alternatively, you can type the command at the unix prompt when starting each session:-
source /usr/local/lab/lab.env

Course staff
Course lecturer: Professor David Gilbert
Guest lecturers: Tamara Polajnar, Xu Gu, Robin Donaldson, Dr Susan Rosser (IBLS), Glasgow 2007 iGEM team.
Course demonstrator: Xu Gu
Computing systems support officer: Robin Donaldson

Bioinformatics - the subject area: Bioinformatics can be defined as the application of techniques from computer science to solve problems in molecular biology. This exciting area is a relatively young field, and the pace of research is driven by the large and rapidly increasing amount of data being produced from, for example, efforts to sequence the genomes of a variety of organisms. The areas where computer science can be applied range from assembly of sequence fragments, analysis of DNA, RNA and protein sequences, prediction and analysis of protein sequence and function, and the analysis and simulation of general metabolic function and regulation. Bioinformatics is certainly not number-crunching for molecular biologists, but is about the application of techniques from computer science such as modelling, simulation, data abstraction, data manipulation and pattern discovery techniques in order to analyse biological data. The data generated by the experimental scientists requires annotation and detailed analysis in order to turn it into knowledge which can then be applied to improving health care via, for example, new drugs and gene therapy, medical practices, and food production - all of which are now high-profile issues nationally.

The amount and variety of biological data now available, together with techniques developed so far have enabled research in Bioinformatics to move beyond the study of individual biological components (genes, proteins etc) albeit in a genome-wide context to attempt to study how individual parts cooperate in their operation. Bioinformatics as a scientific activity has now moved closer to the area of Systems Biology which seeks to integrate biological data as an attempt to understand how biological systems function. By studying the relationships and interactions between various parts of a biological system it is hoped that an understandable model of the whole system can be developed.

The course will also present an introduction to Synthetic Biology.

Prior knowledge: The course will focus on computing techniques used to analyse, organise and display biological data, rather than on biology. You do not need to have a biological background to do the module - the course will give you the specific knowledge required. It will be supported by members of the Bioinformatics Research Centre, who have backgrounds in biology, bioinformatics and computer science.

Slides / handouts

1: 8,10 Jan: Introduction; Molecular Genetics Lab 1
2: 15,17 Jan Sequence Comparison (1) Lab 2
3: 22,24 Jan Sequence Comparison (1), Sequence Comparison (2) Labs 2 & 3
4: 31 Jan Sequence Comparison (2) Lab 3 Coursework out Lecture on 29 January postponed.
5: 5, 7 Feb Multiple Sequence Alignment Lab 4
6: 12, 14 Feb Multiple Sequence Alignment, Patterns and Profiles;
Viruses: Viruses etc 1, 2, 3; Recombination (David Leader)
Lab 5
7: 19,21 Feb Scoring matrices; Lawrence Hunter - Text mining: Automated Aides for Generating Scientific Insights Lab 6
8: 26,28 Feb Text mining (Tamara Polajnar); Phylogenetics; Systems Biology - introduction No Lab - extra lecture
9: 4,6 Mar Dynamic modelling (1) (Xu Gu); Model checking (Robin Donaldson) Lab 9
10: 11,13 Mar Modelling & modularisation Lab 10


Exams Sample exam paper, with appendix, and sample answers.

Support material:

Course texts and required reading:

Other texts of interest: On-line resources: