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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

NUTM3888: Metabolic Cybernetics

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 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.

Code NUTM3888
Academic unit Life and Environmental Sciences Academic Operations
Credit points 6
Prerequisites:
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[(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)
Corequisites:
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None
Prohibitions:
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NUTM3004 or NUTM3002
Assumed knowledge:
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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