Outline

Overview

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

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic concepts in metabolism; protein synthesis; gene regulation; quantitative and statistical skills
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Alistair Senior, alistair.senior@sydney.edu.au
Guest lecturer(s)	Melanie White, melanie.white@sydney.edu.au
	Jacqueline Matthews, jacqueline.matthews@sydney.edu.au
	Jean Yang, jean.yang@sydney.edu.au
	Greg Neely, greg.neely@sydney.edu.au
Lecturer(s)	James Burchfield, james.burchfield@sydney.edu.au
	Pengyi Yang, pengyi.yang@sydney.edu.au
	Alistair Senior, alistair.senior@sydney.edu.au
	Edward Hancock, edward.hancock@sydney.edu.au
	David James, david.james@sydney.edu.au
Tutor(s)	Sophie Lucic Fisher, sophie.lucicfisher@sydney.edu.au
Tutor(s)	Fahad Ali, fahad.ali@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final online exam The exam will have 30 MCQs (50%), and 2 long answer questions (50%).	50%	Formal exam period	2 hours
Outcomes assessed:
Online task	Quizzes There is one quiz for each section of the course, testing practical skills.	20%	Multiple weeks	Released 4 weeks prior to due date.
Outcomes assessed:
Small continuous assessment	Lab notebook Lab notebooks from the preceding we need to handed in on Sundays.	20%	Ongoing	See Canvas
Outcomes assessed:
Presentation	Presentation Oral presentation in-person/online. Evaluating understanding of data.	10%	Week 13	3 minutes
Outcomes assessed:
= Type B final exam ?

Assessment summary

Quizzes: The quizzes are similar to, but shorter than, the tasks and exercises completed in the labs. There are 3-quizzes, one covering each section of the course.
Presentation: The presentation will be delivered in-person or via Zoom depnding on availability and location.
Lab books: To be completed after each lab session.
Final exam: This assessment is compulsory and failure to attend, attempt, or submit will result in the award of an AF grade. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

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	Description
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.
Distinction	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.
Credit	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.
Pass	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.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
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)
	Modelling Metabolism	Practical (3 hr)
Week 03	Gene regulation	Lecture (1 hr)
	Modelling gene regulation	Lecture (1 hr)
	Modelling Gene regulation	Practical (3 hr)
Week 04	Cellular signalling	Lecture (1 hr)
	Modelling cellular signalling	Lecture (1 hr)
	Modelling Signalling networks	Practical (3 hr)
Week 05	Disease Example - Synthetic biology Case Study	Lecture (1 hr)
	Introduction to R	Lecture (1 hr)
	Introduction to R	Practical (3 hr)
Week 06	Statistical Principles 1	Lecture (1 hr)
	Anatomy of an Experiment	Lecture (1 hr)
	Analysis of a basic experiment	Practical (3 hr)
Week 07	Static Images	Lecture (1 hr)
	Dynamic Live Cell Imaging	Lecture (1 hr)
	Analysis of an Imaging Experiment	Practical (3 hr)
Week 08	Principles of Data Visualisation	Lecture (1 hr)
Week 08	Statistical Principles 2	Lecture (1 hr)
Week 09	From Genomics to Phenotypic Big Data	Lecture (1 hr)
Week 09	Correlations, Big Data and Multiple Hypothesis Testing	Practical (3 hr)
Week 10	Presentation Skills	Lecture (1 hr)
	Overview of a Mass Spec Experiment and Proteomics	Lecture (1 hr)
	Proteomics, T-Tests and Multiple Hypothesis Testing	Practical (3 hr)
Week 11	Disease Example - Monogenic vs Complex GWAS	Lecture (1 hr)
	Network Reconstruction 1	Lecture (1 hr)
	Networks	Practical (3 hr)
Week 12	Disease Example - Meta-Analysis in Stroke	Lecture (1 hr)
	Network Reconstruction 2	Lecture (1 hr)
	Clustering	Practical (3 hr)
Week 13	Revision for Exam	Lecture (1 hr)
Week 13	Disease Example - Big Data in Cancer	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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

Minor changes to the teaching staff and the lectures have been made since the unit last ran.

Additional information

Science student portal

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: unihealth.usyd.edu.au/

Disclaimer

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBIO2001: Molecular Systems Biology

Semester 1, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect