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Unit of study_

NUTM3888: Metabolic Cybernetics

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

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.

Unit details and rules

Unit code NUTM3888
Academic unit Life and Environmental Sciences Academic Operations
Credit points 6
NUTM3004 or NUTM3002
[(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)
Assumed knowledge

PHSI2X0X and (MATH1XX5 or ATHK1001)

Available to study abroad and exchange students


Teaching staff

Coordinator Kim Bell-Anderson,
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Online open book without invigilation
30% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment group assignment Research project: multimedia communication OR proposal presentation
Multimedia communication OR Project proposal pitch
15% Multiple weeks multimedia: 500 w, 3 min OR pitch: 10min
Outcomes assessed: LO8 LO11 LO10 LO9
Assignment Data visualisation exercises
10% Week 06 Variable
Outcomes assessed: LO6 LO7 LO10
Assignment Systems essay
10% Week 08 1000 words
Outcomes assessed: LO1 LO5 LO3
Assignment Research report: reflection
Written reflection
5% Week 11 2 pages
Outcomes assessed: LO8 LO11
Assignment Research project - lecturer evaluation
Online task
5% Week 11 Variable
Outcomes assessed: LO6 LO8 LO11
Assignment group assignment Research project report
15% Week 12 4000 words/ 10 pages
Outcomes assessed: LO1 LO11 LO10 LO9 LO8 LO7 LO6 LO2
Presentation group assignment Research project - oral presentation
Final project presentation
10% Week 12 5 minutes
Outcomes assessed: LO6 LO7 LO8 LO9 LO10 LO11
group assignment = group assignment ?
Type C final exam = Type C final exam ?

Assessment summary

  • Systems essay: This essay aims to assess how well you
    understand the systems perspective. Marks will be awarded based on how clearly you can communicate what systems thinking is.
  • Data visualisation exercises: This assessment aims to assess how
    effectively you can use data visualisation for conveying a clear message in order to answer a specific question and/or facilitate decision making. You will create two images, one as a visual representation of data, and the other a redesign of a diagram/pathway or other representation of a mechanism relating to obesity.
  • Research project: You will work in an interdisciplinary team setting to solve a real-life problem. You will be allocated to small teams with students from other units. Teams will produce a team oral presentation, a team multimedia/pitch presentation and a final report. Team process is also assessed by peer evaluation, individual reflections and submission of meeting minutes.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Multiple weeks Team research project Project (18 hr) LO1 LO2 LO6 LO8 LO9 LO10 LO11
Team research presentations Presentation (2 hr) LO8 LO9 LO10 LO11
Week 01 Obesity - A scientist's view Lecture (1 hr) LO1 LO2
Data, lies and visualisation Lecture and tutorial (1 hr) LO4 LO6 LO10
Data visualisation in R I Computer laboratory (2 hr) LO6 LO7
Week 02 Obesity - A clinician's view Lecture (1 hr) LO1 LO2
Systems biology Lecture (1 hr) LO3 LO5
Data visualisation in R II Computer laboratory (2 hr) LO6 LO7 LO10
Week 03 Genetics of obesity Lecture (2 hr) LO1 LO2 LO5
Week 04 Evolution of food and disease Lecture (1 hr) LO1 LO2
Nutrition in obesity Lecture (1 hr) LO1 LO2 LO9
Week 05 Nutrition as a complex system Lecture (2 hr) LO1 LO3 LO9
Week 06 Developmental origins Lecture (2 hr) LO1 LO2 LO9
Week 07 The psychology of obesity Lecture (1 hr) LO1 LO2
Neural control of energy balance Lecture (1 hr) LO1 LO2 LO9
Week 08 Physical activity Lecture (1 hr) LO1 LO2 LO9
Population and socioeconomic aspects of obesity Lecture (1 hr) LO1 LO2 LO9
Week 09 The microbiome in obesity Lecture (1 hr) LO1 LO2 LO5
Food processing Lecture (1 hr) LO1 LO2 LO9
Week 10 Statistics Lecture (2 hr) LO6 LO7
Week 11 Precision Medicine Lecture (1 hr) LO1 LO2 LO5

Attendance and class requirements

Attendance: All students are expected to attend all lectures, tutorials and datorials and group work team meetings. Absences from all scheduled group work team sessions must be explained
and supported by appropriate documentation. Note that the Faculty of Science has a minimum 80% attendance requirement for a student
to pass any unit of study. 

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

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

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

Assessment weighting has aligned with co-share Units of Study (STAT3888, FOOD3888, QBIO3888)


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.