Outline

Overview

Genetic technologies and genetic engineering allows us to manipulate genes and their expression, to enable several outcomes, including the development of precise models of disease and targeted therapeutics for genetic disorders. The field of genetic technologies and the number of biomedical applications are rapidly expanding. In fact, more than 1000 gene technology-related clinical trials are currently active, globally. This unit introduces the details and principles of genetic technologies for biomedical engineers. It includes much of the background knowledge necessary for biomedical engineers to work in the field of genetic engineering and technologies. There is coverage of basic and relevant molecular biology, as well as an emphasis on quantitative analyses. The course largely focuses on the various techniques used to modify genes and/or their expression, and particularly the application of these techniques, including both therapeutic development and development of scientific models. A strong emphasis is placed on application, and the course includes substantial discussions of commercial, safety, ethical, and regulatory considerations

Unit details and rules

Academic unit	Biomedical Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Introductory molecular biology (e.g. BMET1961), BMET3971/9971
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Collin Anderson, collin.anderson@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Final exam covering all material from the semester	30%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	EOI Expression of interest for grant application using genetic technologies	30%	Progressive	2 pages	AI allowed
Outcomes assessed:
Data analysis	Computational Data Analysis 1 Students will submit computational data analyses in week 5	5%	Week 05 Due date: 29 Mar 2026 at 23:59	Small continuous assessment	AI allowed
Outcomes assessed:
In-person written or creative task	Mid-Semester Quiz Mid-semester multi-page quiz	15%	Week 07	85 minutes	AI prohibited
Outcomes assessed:
Data analysis	Computational Data Analysis 2 Students will submit additional computational data analyses in week 6	5%	Week 07 Due date: 12 Apr 2026 at 23:59	Small continuous assessment	AI allowed
Outcomes assessed:
Interactive oral	Group Presentation Small group presentation on a pitch on a new genetic medicine application	15%	Week 12	8 minutes + 4 minutes for questions	AI limited - refer to Canvas
Outcomes assessed:
= group assignment ?

Assessment summary

This unit will be comprised of 6 primary assessments. Small, analytical assessments will be due in weeks 5 and 6, enabling students to practice data analysis in the context of fundamentals learned, particularly in weeks 2 through 5.

Next, an 85-minute mid-semester quiz will be held in class on Wednesday of week 7.

On Friday of week 7, we will introduce 2 major assessments, giving students 5 and 6 weeks to complete the major assessments.

The first major assessment is a group presentation-based "Pitch" assessment to be delivered in week 12 in groups of 4. Groups will work together to propose a coherent, novel therapeutic strategy to utilize genetic technologies to treat a health condition of their choice, presenting to a "panel" (Drs Collin Anderson and Hamish Fernando). Contributions to the group will be evaluated using Sparkplus, which may result in different mark allocations across individual group members.

The second major assessment is an "EOI" due in week 13, with a formative draft due for feedback (and marks) in week 10. This assessment is just two pages long plus references, but it should be viewed as a significant challenge. Students will create an expression of interest (EOI) for funding that proposes the use of genetic technologies in a context outside of disease or treatment. This assessment is inspired by real-world Australian Research Council Discovery Project expressions of interest, a highly competitive application for federal funding that many academic engineers apply for.

Last, a final exam will be held during the final exam period.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Submitted work is of exceptional standard, beyond expectations expected of senior undergraduate engineering students. These submissions often satisfy extensive and correct critical analysis (86-90) and provide novel and thought-provoking yet reasoned out discussion points resulting from further independent learning (91+).
Distinction	75 - 84	Submitted assessment tasks are of very good standard. Typically, these assessments are very good in the descriptive nature, with a decent attempt at the critical analysis and thinking in the submission, but gaps are present.
Credit	65 - 74	Submitted assessment tasks are of good standard. Typically, these assessments are good in the descriptive nature, but lack the proper critical analysis and thinking in the submission (65-69), or some attempt has been made but mostly basic in nature (70-74)
Pass	50 - 64	All requirements of assessment met with the minimum satisfactory standard.
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.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard late penalties apply for all assessments.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Formal exam period	Final Exam	Assessment (2 hr)
Week 01	Lessons of history: successful and unsuccessful applications of genetic technologies	Lecture (3 hr)
Week 02	Revision in fundamentals of molecular biology	Lecture (3 hr)
Week 03	Genetic inheritance, chromosomes, mutations	Lecture (3 hr)
Week 04	Gene isolation, cloning, quantitative DNA, RNA, and protein analyses	Lecture (3 hr)
Week 05	Gene discovery in the context of disease, bioinformatics, additional quantitative analyses, and CRISPR	Lecture (3 hr)
Week 06	Genetic models of disease, optogenetics	Lecture (3 hr)
Week 07	Mid-semester quiz, research proposals and literature review	Lecture (3 hr)
Week 08	Gene replacement and silencing, other therapeutic strategies	Lecture (3 hr)
Week 09	Considerations in therapeutic development	Lecture (3 hr)
Week 10	Commercial considerations in genetic technology and engineering applications	Lecture (3 hr)
Week 11	Regulatory, safety, and ethical considerations	Lecture (3 hr)
Week 12	Class presentations	Lecture (3 hr)
Week 13	Lessons from presentations, Revision	Lecture (3 hr)

Attendance and class requirements

Attendance in person is not compulsory, but it is very strongly recommended. Many lectures will include "lectorial"-style components, with small-group discussions in class, and this cannot be effectively replicated through watching lecture recordings.

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. Describe and analyse the basic principles underlying genetic engineering and technologies.
LO2. Describe and compare the different techniques used to modify genes and their applications.
LO3. Determine reasonable genetic engineering strategies in the context of various applications.
LO4. Assess and critique potential applications of genetic technologies from the context of practicality, safety, ethics, and regulation.
LO5. Analyse and apply a range of quantitative techniques related to genetic technologies.
LO6. Identify disorders for which the application of genetic technologies may generate a viable therapeutic strategy, and propose a coherent research strategy to test this therapeutic strategy.

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.

This unit of study is running for the first time in 2026, and thus there is not a previous iteration from which changes can be made.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

BMET5935: Biomedical Application in Genetic Technology

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links