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
Analysis and modelling of physiological systems. Modelling and simulation techniques for pharmacology and medical devices. Origins and characteristics of bio-signals and data; interference, artefact and simple noise removal techniques. Use of modelling and data analysis to understand normal function, impact of disease, and impact of intervention with medical devices and other treatments.
Modern day clinical medicine and biomedical sciences increasingly leverage engineering principles for the development of new surgical implants, instrumental analysis,ﾠ or medication strategies. This trend hasﾠled to the establishment of biomedical engineering as a field of study, which relies heavily on physiological systems understanding, computational simulations, and quantitative methods to manage, analyse, and build models from biomedical data to solve clinical problems. Biomedical engineers are often hired by medical product manufacturers and, more recently, have been incorporated in clinical engineering departments of major hospitals. Following from BIOE1001's introduction, BIOE3001 now aims to equip biomedical engineering students with a computational skillset inﾠ physiological systems modelling and biomedical data analysis supporting upper-level BIOE classes, research projects, and employment opportunities. This course aims to instil:
- An appreciation for how engineering models can be used to simulate physiological systems, leverage clinical data, and enhance patient treatment.ﾠ
- The knowledge and skills (both theoretical and numerical) required to develop computational methods and data analysis to formulate, implement, and assess biomedical models.
As an effort to be accessible and useful to the biomedical engineer specialised in mechanical, chemical, or electrical engineering, the 2024 version of this course will include a 2-hour lecture and 2-hour programming tutorial per week, where the seven core learning objectives are well-mapped to lecture content such as:
- Introduction to physiological system modelling
- Transport mechanics and the vascular system
- Cell dynamicsﾠ and the bloodﾠsystem
- Electrophysiology and the cardiac system
- Biomedical data and image analysis
- Parameter estimation and model assessment
- Epidemiology and biostatistics
Teaching & Learning activities allow the student to develop a conceptual understanding of traditional and current quantitative methods in biomedical engineering. Weekly tutorials with hands-on computer programming will strengthen the knowledge of how to formulate, program, and validate biomedical models and how to process and incorporate biomedical data. This knowledge will be assessed by oral presentation, programming demonstration, a project report, and a final exam.ﾠ
Course requirements
Assumed background
- Basic programming skills in Python (pre-req ENG1001).
- Basic formulation and solutions of ordinary differential-algebraic systems of equations (pre-req MATH1052/MATH1072).
- Basic understanding of physiological systems at cell, tissue, organ, and whole-organism scale (pre-req BIOE1001).
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BIOE1001 and (MATH1052 or MATH1072) and ENGG1001
Course contact
Course staff
Lecturer
Tutor
Miss Chloe De Nys
Mr Micah Clarke
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
- Lectures are scheduled on Tuesdays from 12 noon-2pm in 45-204 of the Mansergh Shaw Building of Mechanical and Mining Engineering.
- Tutorials are scheduled on Thursdays from 12 noon-2pm in 46-441 of the Andrew N. Liveris Building of Chemical Engineering.
Aims and outcomes
The design, manufacture, operation, and implementation of lifesaving biomedical technologies is increasingly dependent on quantitative methods in biomedical engineering. This course aims to equip students with an understanding of how to formulate, implement, and assess engineered models of physiological systems and biomedical data.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the key physiological systems in the human body, how they support life, and the principals of homeostasis.
LO2.
Apply mathematical models to describe and simulate the behaviour of human physiological systems, disease states, and treatment interventions.
LO3.
Implement and simulate physiological models using a computer programming language such as Python.
LO4.
Describe the limitations of mathematical models for describing physiological systems, and discuss the considerations associated with choosing appropriate model complexity.
LO5.
Describe the origins and characteristics of selected bio-signals and associated noise or interference; and to describe and implement noise removal strategies such as linear filtering, and signal averaging.
LO6.
Describe and implement strategies for parametrising physiological models using bio-signal data; and describe how these can be used to provide information to assist the clinical management of patients.
LO7.
Describe and interpret key concepts and terms in biostatistics and epidemiology used to describe the individual and population burden of disease and the impact of interventions.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Presentation |
Project Presentation Slides
|
with Oral Presentation below. |
19/08/2024 5:00 pm |
| Presentation |
Project Oral Presentation
|
10% |
20/08/2024 - 23/08/2024
Week 5 |
| Computer Code, Role play/ Simulation |
Project Programming Code
|
with Programming Demo below. |
16/09/2024 5:00 pm |
| Computer Code, Practical/ Demonstration |
Project Programming Demo
|
10% |
17/09/2024 - 20/09/2024
Week 9 |
| Paper/ Report/ Annotation |
Project Final Report
|
40% Hurdle |
21/10/2024 5:00 pm
Monday of Week 13 |
| Examination |
Paper Final Exam
|
40% Hurdle |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Project Presentation Slides
- Online
- Mode
- Written
- Category
- Presentation
- Weight
- with Oral Presentation below.
- Due date
19/08/2024 5:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L04
Task description
Throughout the semester, students will conduct a physiological modelling and programming project. In week 5, students will present an oral presentation outlining the project motivation, clinical approaches, and model design, formulation, and expected results.
Presentations are expected to run 8 min (roughly 8 slides of content), with 4 min available to answer examiner questions.
On the Monday prior to the oral presentations, students must submit their presentation slides in powerpoint (.pptx) or .pdf format. These must be identical to those used in their oral presentation later in the week.
AI and ChatGPT Statement: This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT 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 generative AI or MT 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 and MT tools.
Submission guidelines
Submit presentation slides by Turnitin on Blackboard.
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
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
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)
Project Oral Presentation
- Identity Verified
- In-person
- Mode
- Activity/ Performance, Oral
- Category
- Presentation
- Weight
- 10%
- Due date
20/08/2024 - 23/08/2024
Week 5
- Other conditions
- Student specific, Time limited.
- Learning outcomes
- L01, L02, L04
Task description
Throughout the semester, students will conduct a physiological modelling and programming project. In week 5, students will present an oral presentation outlining the project motivation, clinical approaches, and model design, formulation, and expected results.
Presentations are expected to run 8 min (roughly 8 slides of content), with 4 min available to answer examiner questions.
On the Monday prior to the oral presentations, students must submit their presentation slides in powerpoint (.pptx) or .pdf format. These must be identical to those used in their oral presentation later in the week.
The programming demonstrations will run in individual 15 minute time slots out-of-class-hours during week 5 from Tuesday to Friday (20th August to 22nd August). Time slots will be between 9am and 4pm either in-person in seminar room 46-970. Students must reserve an available demonstration time slot on Blackboard in advance of week 5.
AI and ChatGPT Statement: This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT 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 generative AI or MT 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 and MT tools.
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
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
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)
Project Programming Code
- Online
- Mode
- Written
- Category
- Computer Code, Role play/ Simulation
- Weight
- with Programming Demo below.
- Due date
16/09/2024 5:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04
Task description
Throughout the semester, students will conduct a physiological modelling and programming project. In week 9, students will present a programming demonstration outlining model suitability, implementation, programming syntax, simulation success, plotting, commenting, and formatting. Demonstrations are expected to run 8 min, with 4 min available to answer examiner questions.
On the Monday prior to the programming demonstrations, students must submit their programming code as a python file (.py) or copy-pasted into a word document (.docx). This must be identical to the one used in their programming demonstration later in the week.
AI and ChatGPT Statement: This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT 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 generative AI or MT 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 and MT tools.
Submission guidelines
Submit in a .py or a .docx file to Turnitin through Blackboard.
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
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
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)
Project Programming Demo
- Identity Verified
- In-person
- Mode
- Activity/ Performance, Oral
- Category
- Computer Code, Practical/ Demonstration
- Weight
- 10%
- Due date
17/09/2024 - 20/09/2024
Week 9
- Other conditions
- Student specific, Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
Throughout the semester, students will conduct a physiological modelling and programming project. In week 9, students will present a programming demonstration outlining model suitability, implementation, programming syntax, simulation success, plotting, commenting, and formatting. Demonstrations are expected to run 8 min, with 4 min available to answer examiner questions.
On the Monday prior to the programming demonstrations, students must submit their programming code as a python file (.py) or copy-pasted into a word document (.docx). This must be identical to the one used in their programming demonstration later in the week.
The programming demonstrations will run in individual 15 minute time slots out-of-class-hours during week 9 from Tuesday to Thursday (17th September to 19th September). Time slots will be between 9am and 4pm either in-person in seminar room 46-970. Students must reserve an available demonstration time slot on Blackboard in advance of week 9.
AI and ChatGPT Statement: This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT 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 generative AI or MT 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 and MT tools.
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
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
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)
Project Final Report
- Hurdle
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40% Hurdle
- Due date
21/10/2024 5:00 pm
Monday of Week 13
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
Throughout the semester, students will conduct a physiological modelling and programming project. In week 13, students will submit a final project report. This project report is intended to assess student's ability to implement their own developed mathematical system in a programming language, and assess the accuracy of their mathematical system compared to biomedical data. This report will also reiterate capabilities to describe and formulate a mathematical system to describe a different physiological system under homeostasis, disturbances, and treatment. Strong reports will also undertake substantial literature review.
On the Monday of Week 13, students must submit their programming report in word document format (.docx) which includes a bibliography of references and an appendix including all programming code. The programming code copied into the appendix of the word document must be identical to that used to generate figures and results throughout the report.
This assessment is a hurdle. This means that you must achieve >/= 45% for this assessment item in order to be eligible to pass this course.
AI and ChatGPT Statement: This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT 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 generative AI or MT 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 and MT tools.
Hurdle requirements
This is assessment is a hurdle. This means that you must achieve >/= 45% for this assessment item in order to be eligible to pass this course.Submission guidelines
Submitted electronically through Turnitin and/or Blackboard.
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
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
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)
Paper Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40% Hurdle
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L04, L05, L06, L07
Task description
This is an open book final exam at the end of semester summarizing all concepts throughout the class. This is a paper exam, signifying that no programming will be performed, although programming approaches and understanding can be questioned. Students will complete on campus as an invigilated exam.
This is assessment is a hurdle. This means that you must achieve >/= 45% for this assessment item in order to be eligible to pass this course.
AI and ChatGPT Statement: This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
This is assessment is a hurdle. This means that you must achieve >/= 45% for this assessment item in order to be eligible to pass this course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open Book examination |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
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: Little or no knowledge demonstrated, major assessment items missed or largely incomplete. Typically an overall mark of less than 20%. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Poor knowledge, reasoning, and implementation. Typically an overall mark of 20-44.9% OR an overall mark >=45% but < 25% on either the final project OR the final exam. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Fails to satisfy basic requirements for a passing grade. THIS IS A FAILING GRADE. Typically an overall mark of 45-49.9% OR an overall mark >=50% but < 45% on either the final project AND/OR the final exam. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Good knowledge; basic reasoning skills and implementation demonstrated. Typically a mark of 50-64.9% AND at least 45% on both the final project AND the final exam separately. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Good knowledge; good reasoning skills and implementation. Typically an overall mark 65-74.9% AND at least 45% on both the final project AND the final exam separately. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Very good knowledge plus good complex reasoning skills and skilled implementation. Typically an overall mark of 75-84.9% AND at least 45% on both the final project AND the final exam separately. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Excellent knowledge with excellent complex reasoning skills and excellent implementation. Typically an overall mark of 85-100% AND at least 45% on both the final project AND the final exam separately. |
Additional course grading information
The final project and the final exam are both hurdles in this class. This means that students must obtain at least 45% on BOTH the final project AND the final exam SEPARATELY to receive a final course grade of 4 or higher.
Supplementary assessment
Supplementary assessment is available for this course.
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.
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 |
|---|---|---|
Lecture |
Physiological Systems & Homeostasis - LECTURE 1 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Physiological Systems & Homeostasis - TUTORIAL 1 Learning outcomes: L01, L02, L03 |
|
Lecture |
Introduction to Biomedical Model Formulation - LECTURE 2 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Introduction to Biomedical Model Formulation - TUTORIAL 2 Learning outcomes: L01, L02, L03 |
|
Lecture |
Transport and Vascular Blood Flow - LECTURE 3 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Transport and Vascular Blood Flow - TUTORIAL 3 Learning outcomes: L01, L02, L03 |
|
Lecture |
Transport and Vascular System Mechanics - LECTURE 4 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Transport and Vascular System Mechanics - TUTORIAL 4 Learning outcomes: L01, L02, L03 |
|
Workshop |
Project Oral Presentations - ASSESSMENT 1 Learning outcomes: L01, L02 |
|
Lecture |
Biochemical Reactions and the Immune System - LECTURE 5 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Biochemical Reactions and the Immune System - TUTORIAL 5 Learning outcomes: L01, L02, L03 |
|
Lecture |
Cell Dynamics and the Immune System - LECTURE 6 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Cell Dynamics and the Immune System - TUTORIAL 6 Learning outcomes: L01, L02, L03 |
|
Lecture |
Electrophysiology and the Cardiac System - LECTURE 7 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Electrophysiology and the Cardiac System - TUTORIAL 7 Learning outcomes: L01, L02, L03 |
|
Lecture |
Mid Semester Review - LECTURE 8 Learning outcomes: L01, L02, L03 |
|
Tutorial |
Mid Semester Review - TUTORIAL 8 Learning outcomes: L01, L02, L03 |
|
Lecture |
Biomedical Signal Analysis - LECTURE 9 Learning outcomes: L04, L05, L06, L07 |
|
Tutorial |
Biomedical Signal Analysis - TUTORIAL 9 Learning outcomes: L04, L05, L06, L07 |
|
Lecture |
Parameterisation and Model Complexity - LECTURE 10 Learning outcomes: L04, L05, L06, L07 |
|
Tutorial |
Parameterisation and Model Complexity - TUTORIAL 10 Learning outcomes: L04, L05, L06, L07 |
|
Lecture |
Biomedical Image Analysis - LECTURE 11 Learning outcomes: L04, L05, L06, L07 |
|
Tutorial |
Biomedical Image Analysis - TUTORIAL 11 Learning outcomes: L04, L05, L06, L07 |
|
Lecture |
Epidemiology and Biostatistics - LECTURE 12 Learning outcomes: L04, L05, L06, L07 |
|
Tutorial |
Epidemiology and Biostatistics - TUTORIAL 12 Learning outcomes: L04, L05, L06, L07 |
|
Workshop |
Project Final Report - ASSESSMENT 3 Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Lecture |
Final Semester Review - LECTURE 13 Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
|
Tutorial |
Final Semester Review - TUTORIAL 13 Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
Policies and procedures
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
Safety Induction for Practicals
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.