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Unit of study_

QBIO2001: Molecular Systems Biology

Semester 1, 2021 [Normal day] - Remote

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.

Unit details and rules

Unit 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

Available to study abroad and exchange students


Teaching staff

Coordinator Mark Larance,
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final online exam
Timed exam on Canvas
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Online task Quizzes
Quiz (Weeks 6,8,12)
20% Multiple weeks 3 online quizzes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Small continuous assessment Lab notebook
Lab notebook
20% Ongoing See Canvas
Outcomes assessed: LO3 LO7 LO10
Presentation Presentation
Oral presentation online
10% Week 13 3 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Type B final exam = Type B final exam ?

Assessment summary

  • Quizzes: The quizzes are similar to, but shorter than, the tasks and exercises completed in the labs. 
  • Presentation: The presentation will be delivered via Zoom
  • Lab books: To be completed after each lab session.
  • Final exam: The exam will assess content from the lectures throughout the course.

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.

Result name

Mark range


High distinction

85 - 100

At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.


75 - 84

At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.


65 - 74

At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.


50 - 64

At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory 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
Week 01 Biological introduction Lecture (1 hr)  
Introduction to modelling Lecture (1 hr)  
Introduction to simbiology Practical (3 hr)  
Week 02 Metabolism Lecture (1 hr)  
Modelling metabolism Lecture (1 hr)  
Metabolism Practical (3 hr)  
Week 03 Gene regulation Lecture (1 hr)  
Modelling gene regulation Lecture (1 hr)  
Gene regulation Practical (3 hr)  
Week 04 Cellular signalling Lecture (1 hr)  
Modelling cellular signalling Lecture (1 hr)  
Signalling networks Practical (3 hr)  
Week 05 Synthetic biology Lecture (1 hr)  
Introduction to R Lecture (1 hr)  
Introduction to R Practical (3 hr)  
Week 06 Component of experiment Lecture (1 hr)  
Experimental variability and replications; hypo and statistical power Lecture (1 hr)  
R: small scale data (chip seq) Practical (3 hr)  
Week 07 Randomisation and blocking Lecture (1 hr)  
Data visualisation Lecture (1 hr)  
Tableau Practical (3 hr)  
Week 08 Data types (static images) Lecture (1 hr)  
Data types (live cell imaging) Lecture (1 hr)  
Week 09 Presentation skills Lecture (1 hr)  
Data types: mass spectrometry Lecture (1 hr)  
R: large scale data Practical (3 hr)  
Week 10 Data types: proteomics applications Lecture (1 hr)  
Hypothesis generating experiments Lecture (1 hr)  
R: large scale data Practical (3 hr)  
Week 11 From genomics to big data Lecture (1 hr)  
Biological network reconstruction and visualisation Lecture (1 hr)  
R: large scale data Practical (3 hr)  
Week 12 Biological network analysis and visualisation Lecture (1 hr)  
Meta-analysis Lecture (1 hr)  
R: large scale data Practical (3 hr)  
Week 13 Disease Lecture (1 hr)  
Examination questions and answers Lecture (1 hr)  

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. 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

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.

No changes have been made since this unit was last offered

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General laboratory safety rules

  • No eating or drinking is allowed in any laboratory under any circumstances 

  • A laboratory coat and closed-toe shoes are mandatory 

  • Follow safety instructions in your manual and posted in laboratories 

  • In case of fire, follow instructions posted outside the laboratory door 

  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory 

  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service:


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

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