Skip to main content
Unit of study_

NUTM3888: Metabolic Cybernetics

2024 unit information

Obesity is a worldwide health problem driven by a complex intersection between genetics and the environment. This interdisciplinary unit of study aims to explore recent advances in 'omics' technology and big data analysis. The focus will be on how to tackle highly complex questions such as why some individuals become obese and others don't. The problem will be presented from a range of societal, biological and evolutionary perspectives to increase the breadth of knowledge on the problem of obesity. You will be provided a research training opportunity to contribute to our understanding of the relevant problems of over-nutrition in our society. Collaborative research is supported by lectures and tutorials on nutrition science, systems thinking and data coding and analysis to deepen data literacy and enhance interdisciplinary communication and collaboration.

Unit details and rules

Managing faculty or University school:

Life and Environmental Sciences Academic Operations

Code NUTM3888
Academic unit Life and Environmental Sciences Academic Operations
Credit points 6
[(BCHM2X72 or BCMB2X01 or MEDS2003) and (BCHM2X71 or BCMB2X02 or DATA2002 or GEGE2X01 or MBLG2X7X or BIOL2XXX or PHSI2X0X or MEDS2001)] or (BMED2401 and BMED2405)
NUTM3004 or NUTM3002
Assumed knowledge:
PHSI2X0X and (MATH1XX5 or ATHK1001)

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

  • LO1. Explain the multilevel nature of obesity and how this influences the way research must approach the problem
  • LO2. Synthesise knowledge of the obesity/diabetes epidemic from a multifactorial perspective and appraise the use of interdisciplinary approaches to intervention, be they political, social, economic, medical, etc
  • LO3. Describe the components of a system and identify systems associated with obesity
  • LO4. Describe what ‘big data’ is and where it comes from
  • LO5. Identify and appraise contemporary research techniques eg ‘omics’ and explain how they contribute to the generation of ‘big data’ and systems biology
  • LO6. Develop empirical research skills, data analysis and visualisation skills, critical thinking and problem-solving skills
  • LO7. Use the statistical program R for basic descriptive analysis and visualisation of large biological/health data sets
  • LO8. Collaborate with experts across multiple disciplines in a larger team and integrate findings from across groups in a scientific oral presentation
  • LO9. Relate complex primary data to a wider health problem in the community (‘big picture’ view)
  • LO10. Represent significant complex findings in a creative way, making appropriate use of visual imagery to communicate with a non-specialist audience
  • LO11. Work effectively in an interdisciplinary group - with appropriate communication and collaboration skills

Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

Session MoA ?  Location Outline ? 
Semester 2 2024
Normal day Camperdown/Darlington, Sydney
Outline unavailable
Session MoA ?  Location Outline ? 
Semester 2 2020
Normal day Camperdown/Darlington, Sydney
Semester 2 2021
Normal day Camperdown/Darlington, Sydney
Semester 2 2021
Normal day Remote
Semester 2 2022
Normal day Camperdown/Darlington, Sydney
Semester 2 2022
Normal day Remote
Semester 2 2023
Normal day Camperdown/Darlington, Sydney

Modes of attendance (MoA)

This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.