Computational Systems Biology (P01493)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-CSB

Systems Biology is the application of computational modelling and simulation to complex systems in biology. Examples include biochemical pathways, metabolic processes, protein complexes and information processing, genetic networks, self-organising systems, neuronal networks and cell-cell communication. This course will focus on the level of molecular and genetic systems and simulations.

Entry Requirements

? Pre-requisites : Bioinformatics 1 (or strong biological background) For Informatics PG students only, or by special permission of the School. Students are expected to have: - basic biological knowledge (BIO1 course) - basic mathematical knowledge of differential equations and linear algebra - basic programming skills, in any language.

? Co-requisites : Bioinformatics 2 is a good companion course.

Subject Areas

Home subject area

Computational Systems Biology, (School of Informatics, Schedule O)

Other subject areas

Life Science Computing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
09/01/2007	10:00	10:50	Lecture Room 3317, JCMB	KB

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	10:00	10:50	Central
Lecture	Friday	10:00	10:50	Central

Summary of Intended Learning Outcomes

On successful completion of the course, students will be able to demonstrate the following:
* Discuss the potential benefits and predictive value of systems biology approaches.
* Implement a molecular genetic model in an appropriate modelling framework.
* Compare and contrast existing models at the biochemical, genetic, proteomic and metabolic levels
* Discus the methods used to establish parameters in models and how to test and refine them.
* Discuss the mathematical basis for biomolecular simulations.
* Describe the limitations of modelling strategies.