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Unit of study_

QBIO2001: Molecular Systems Biology

Experimental approaches to the study of biological systems are shifting from hypothesis driven to hypothesis generating research. Large scale experiments at the molecular scale are producing enormous quantities of data ("Big Data") that need to be analysed to derive significant biological meaning. For example, monitoring the abundance of tens of thousands of proteins simultaneously promises ground-breaking discoveries. In this unit, you will develop specific analytical skills required to work with data obtained in the biological and medical sciences. The unit covers quantitative analysis of biological systems at the molecular scale including modelling and visualizing patterns using differential equations, experimental design and data types to understand disease aetiology. You will also use methods to model cellular systems including metabolism, gene regulation and signalling. The practical program will enable you to generate data analysis workflows, and gain a deep understanding of the statistical, informatics and modelling tools currently being used in the field. To leverage multiple types of expertise, the computer lab-based practical component of this unit will be predominantly a team-based collaborative learning environment. Upon completion of this unit, you will have gained skills to find meaningful solutions to difficult biological and disease-related problems with the potential to change our lives.

Code QBIO2001
Academic unit Life and Environmental Sciences Academic Operations
Credit points 6
Assumed knowledge:
Basic concepts in metabolism; protein synthesis; gene regulation; quantitative and statistical skills

At the completion of this unit, you should be able to:

  • LO1. Model cellular processes using differential equations
  • LO2. Describe gene regulation, metabolic networks and signalling networks
  • LO3. Apply differential equation models of cellular processes using standard computational toolboxes for systems biology
  • LO4. Outline the principles and applications of synthetic biology
  • LO5. Discriminate between types of experimental designs and apply the appropriate statistical techniques
  • LO6. Describe experimental data types and experimental processes for quantitative biology
  • LO7. Analyse small-scale biological data using standard computational toolboxes for statistical analysis
  • LO8. Evaluate tools designed for “big data” analysis quantitative biology
  • LO9. Apply “big data” methods to the analysis of disease-related datasets
  • LO10. Analyse large-scale biological data using standard computational toolboxes for