Course coordinator
Associate Professor Adrian Oehmen
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Chemical Engineering School
Systems based analysis and design of biological systems for the production of valuable products. Assumed background: (i) understanding of biochemistry and microbiology equivalent to BIOE1001/CHEE1001, (ii) understanding of bioprocesses equivalent to BIOE4020/CHEE4020, (iii) basic computer skills.
Living organisms are increasingly used in the production of bulk and fine chemicals. The advantages are:
- Living organisms can carry out highly specific and complex chemical reactions schemes in water at low temperature and pressure.
- Living organisms are self-replicating “catalytic pellets” that can be designed at microscale and scaled to very large volumes using relative standard process technology.
However, living organisms were not designed specifically for producing the desired products and – in most cases – substantial reengineering is required to produce products at competitive costs.
ﾠ
Previously, improvements were achieved through random mutagenesis and selection of better strains. Using this approach, improvements were restricted to what was achievable within the confinement of a single organism and with a relatively limited number of modifications. Advances in molecular biology and genomics, however, have greatly extended the improvements possible with a combination of desirable traits from several organisms. With a nearly unlimited number of ways to modify an organism, the main challenge is to decide where to invest the effort. Metabolic engineering introduces a range of tools aimed at addressing this problem.
ﾠ
Though the aim might be very applied, the problem is very fundamental in nature. In order to predict how a given genetic modification might improve an organism, we need to know how the organism functions, i.e., we need to understand the translation between genome and phenotype, which is one of the most hotly pursued areas in biology. This course will take us to the boundaries of current understanding and practices, with industry and research lecturers discussing recently developed metabolic engineering applications for producing novel bioproducts. The use of metabolic modelling approaches in bioprocess engineering will be compared and contrasted with traditional bioprocess modelling approaches for the optimisation of different biological processes of industrial interest.
Course requirements
Assumed background
- An understanding of biochemistry and microbiology equivalent to BIOE1001
- Understanding of bioprocesses equivalent to BIOE4020
- Basic computer skills
Prerequisites
You'll need to complete the following courses before enrolling in this one:
CHEE4020 or BIOE4020
Incompatible
You can't enrol in this course if you've already completed the following:
CHEE4028 and CHEE7409
Course contact
Course staff
Lecturer
Associate Professor Adrian Oehmen
Associate lecturer
Dr Craig Barry
Ms Rosie Gillane
Dr Akhila Shon
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The course focus relates to advanced metabolic engineering approaches for optimising cellular metabolic pathways as well as molecular engineering of single enzymes. This includes for example the directed improvement of product formation or cellular properties through the modification of specific biochemical reaction(s) or the introduction of new reaction(s) with the use of recombinant DNA technology. Metabolic modelling approaches are explored in-depth in order to predict the impact of these changes in biochemical properties on the resulting industrial products that are created through bioengineering. The differences between bioprocess modelling and metabolic modelling approaches will also be explored, as well as situations where each modelling approach would be preferable for different biological processes.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Evaluate bio-based processes and apply advanced design and optimisation strategies towards a wide range of bioprocess applications.
LO2.
Interrelate knowledge of cellular metabolism and molecular biology with quantitative approaches for regulating cell function to achieve a desired purpose.
LO3.
Develop and apply metabolic modelling procedures to analyse metabolic networks.
LO4.
Interface between metabolic engineering, systems biology and synthetic biology, with an ability to cross-communicate within the bioscience, biotechnology and bioengineering fields.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Examination |
Exam-In-Semester During Class
|
20% |
3/09/2024 10:00 am
In Class |
| Practical/ Demonstration |
Undergraduate Laboratory Induction
|
Pass/Fail |
2/09/2024 1:00 pm |
| Practical/ Demonstration | Prac Report & Data analysis | 10% |
18/09/2024 4:00 pm |
| Paper/ Report/ Annotation |
Strain design & Metabolic model - Project
|
40% |
21/10/2024 4:00 pm |
| Examination |
Exam During Exam Period (School) - Strain design & Metabolic model - Presentation
|
30% Hurdle |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
Assessment details
Exam-In-Semester During Class
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 20%
- Due date
3/09/2024 10:00 am
In Class
- Learning outcomes
- L01, L02, L03, L04
Task description
This is a closed-book, in-person, invigilated exam. It will test students' knowledge about biological mass balances, course material covered from weeks 1 to 5 inclusive. Both theoretical (35%) and problem-based (65%) questions. A deferred examination will be offered to those students unable to attend and approved for a deferred examination.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any
attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Undergraduate Laboratory Induction
- Online
- Mode
- Activity/ Performance
- Category
- Practical/ Demonstration
- Weight
- Pass/Fail
- Due date
2/09/2024 1:00 pm
- Learning outcomes
- L01, L04
Task description
School mandatory completion of Undergraduate Laboratory Induction. Needed for all courses with a practical component.
Submission guidelines
Submitted electronically through Turnitin on Blackboard.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
The induction should be completed prior to the prac. Students who do not complete the online lab induction are not eligible to enter the lab and perform the prac.
Prac Report & Data analysis
- Mode
- Activity/ Performance
- Category
- Practical/ Demonstration
- Weight
- 10%
- Due date
18/09/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
Theoretical questions and calculations associated with the prac. Analysis of the experimental data set obtained. Full details and requirements are on Blackboard.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
Turnitin Assignment
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
Assessments must be submitted on or before the due date. Late submissions of assessment items will only be accepted if approval for late submission has been obtained prior to the due date.
Penalties Apply for Late Submission
Refer PPL Assessment Procedure Section 3 Part C (48)
Strain design & Metabolic model - Project
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
21/10/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
This individual report is about in silico strain design and metabolic modelling:
Each student will metabolically engineer the strain to maximize production of a particular product using constraint-based methods. The project report should include their coding in Excel or other software.
Refer to Blackboard for full task description.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
Assignments to be submitted via Turnitin
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
Assessments must be submitted on or before the due date. Late submissions of assessment items will only be accepted if approval for late submission has been obtained prior to the due date.
Penalties Apply for Late Submission
Refer PPL Assessment Procedure Section 3 Part C (48)
Exam During Exam Period (School) - Strain design & Metabolic model - Presentation
- In-person
- Mode
- Oral
- Category
- Examination
- Weight
- 30% Hurdle
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Learning outcomes
- L01, L02, L03, L04
Task description
Each student will give a short 8 minute oral presentation about their project and present their approach and model code. This will be accompanied by an 8 minute interview to discuss in detail the project outcomes and their metabolic modelling methodology. The interview will include questions on applications covered in the industry and research seminars.
Refer to Blackboard for full task description. A hurdle is associated with this assessment item.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any
attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | no planning time minutes |
|---|---|
| Duration | 30 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Other |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
Assessments must be submitted on or before the due date. Late submissions of assessment items will only be accepted if approval for late submission has been obtained prior to the due date.
Penalties Apply for Late Submission
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: 0-24.9% overall Low Fail. Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: 25-44.9% overall Fail. Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: 45-49.9% overall AND/OR Marginal Fail. Demonstrated evidence of developing achievement of course learning outcomes. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: 50-64.9% overall AND >50% on the presentation/interview Pass. Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: 65-74.9% overall AND >60% on the presentation/interview Credit. Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: 75-84.9% overall AND >70% on the presentation/interview Distinction. Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: 85-100% overall AND >80% on the presentation/interview High Distinction. Demonstrated evidence of exceptional achievement of course learning outcomes. |
Supplementary assessment
Supplementary assessment is available for this course.
Supplementary assessment will take the form of a presentation/interview on project tasks to be defined by the course coordinator.
Additional assessment information
Use of calculators - Only University approved and labelled calculators can be used in all quizzes or exams for this course. Please consult ﾠhttps://my.uq.edu.au/services/manage-my-program/exams-and-assessment/sitting-exam/approved-calculators ﾠfor information about approved calculators and obtaining a label for non-approved calculators.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Recommended articles: in class discussion
Impact of synthetic biology and metabolic engineering on industrial production of fine chemicals
https://doi.org/10.1016/j.biotechadv.2015.02.011
The Future of Metabolic Engineering and Synthetic Biology: Towards a Systematic Practice
https://doi.org/10.1016/j.ymben.2012.02.001
Analysis of omics data with genome-scale models of metabolism
ﾠdoi:ﾠ 10.1039/c2mb25453k
ﾠ
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Lecture |
Network Analysis: FBA Constraint-based metabolic network models, including methods such as flux balance analysis. Learning outcomes: L01, L02, L03, L04 |
Lecture |
Systems Biology and strain design Systems biology, using information/data from -omics technologies and integration into metabolic models for strain design. Learning outcomes: L01, L02, L03, L04 |
|
Lecture |
Industry and Research Lectures Guest speakers will discuss about applications within metabolic engineering Learning outcomes: L01, L02, L03, L04 |
|
Lecture |
Course Introduction Introduction to metabolic engineering. Outline of important concepts and industrial applications. Learning outcomes: L01, L02, L03, L04 |
|
Lecture |
Cell metabolism and metabolic mass balances Cell metabolism: thermodynamics, metabolic building blocks, redox and energy metabolism, metabolites and cell growth. Mass balances associated with cell metabolism for biological networks. Learning outcomes: L01, L02, L03, L04 |
|
Lecture |
Bioprocess Models Modelling of bioprocesses - stoichiometric and kinetic matricies and their application Learning outcomes: L01, L03 |
|
Lecture |
Algae and phototrophic metabolism Overview of algae and phototrophic bioprocesses and their applications Learning outcomes: L01, L02, L04 |
|
Lecture |
Metabolic Flux Analysis Introduction to metabolic flux analysis and its applications Learning outcomes: L01, L02, L03, L04 |
|
Lecture |
Molecular Biology / Engineering Genomes Application of molecular biology in the context of metabolic engineering, approaches such as CRISPR/Cas for genome alteration. Factors to be considered for metabolic pathway design. Learning outcomes: L01, L02, L04 |
|
Practical |
Prac A prac will be performed focussing on CRISPR/Cas9 and the material of week 6, with an associated prac report Learning outcomes: L01, L02, L04 |
Policies and guidelines
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course:
- Safety Induction for Practicals
Course guidelines
Anyone undertaking courses with a practical component must complete the UQ Undergraduate Student Laboratory Safety Induction and pass the associated assessment.
Specific instructions, usage guidelines and rules for each of the undergraduate laboratories will be delivered as part of each course.
In some cases, students may be required to attend a specific face-to-face laboratory induction/training session.