Computational Methods in Systems Biology - module definition

Systems Biology: 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 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.

Prior knowledge: None required, but Basic Bioinformatics useful.

Context: Bioinformatics Strand, MSc IT, Department of Computing Science, University of Glasgow.

Aims:

  • To provide an introduction to the area of Systems Biology
  • To provide an introduction to the biological background to biochemical networks (metabolic, regulatory, signalling).
  • To introduce algorithms, tools, techniques and databases for the modelling and analysis of biochemical networks.
  • To introduce the principles of basic low-throughput biology techniques used by biologists, such as enzyme kinetic measurements, blotting etc.
  • To introduce the principles of high-throughput techniques and associated databases and analysis, such as mass spectrometry, gene expression array
  • Machine learning techniques for bioinformatics and systems biology

    Intended learning outcomes:

  • Ability to model various biochemical networks using a variety of techniques.
  • Ability to use a range of tools, techniques and databases for biochemical network analysis.
  • Ability to construct databases for biochemical networks.
  • Understand the principles of analysis of low and high-throughput data sets.
  • To apply a variety of machine learning techniques to biological data sets.

    Delivery: 24 hours lectures and 12 hours labs over 12 weeks.

    Assessment: 70% examination, 30% coursework