Mathematical Biology (MAST90011)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look up fees |
Modern techniques have revolutionised biology and medicine, but interpretative and predictive tools are needed. Mathematical modelling is such a tool, providing explanations for counter-intuitive results and predictions leading to new experimental directions. The broad flavour of the area and the modelling process will be discussed. Applications will be drawn from many areas including population growth, epidemic modelling, biological invasion, pattern formation, tumour modelling, developmental biology and tissue engineering. A large range of mathematical techniques will be discussed, for example discrete time models, ordinary differential equations, partial differential equations, stochastic models and cellular automata.
Intended learning outcomes
After completing this subject, students will:
- appreciate the context in which continuum and discrete modelling may arise in mathematical modelling;
- have high level mathematical tools and knowledge that can be used to model a range of problems in mathematical biology;
- have the ability to implement physically justified approximations to solve complex problems;
- have been exposed to both computational and analytical tools, and understand the various contexts in which they can be applied;
- have the ability to pursue further studies in this and related areas.
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 31 October 2023
Eligibility and requirements
Prerequisites
The following subject, or equivalent:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20030 | Differential Equations | Semester 2 (On Campus - Parkville) |
12.5 |
Or both of the following:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30030 | Applied Mathematical Modelling | Semester 1 (On Campus - Parkville) |
12.5 |
MAST30029 Partial Differential Equations (pre-2014)
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 October 2023
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
| Description | Timing | Percentage |
|---|---|---|
Up to 50 pages of written assignments (three assignments worth 20% each, due early, mid and late in semester)
| Throughout the teaching period | 60% |
A written examination
| During the examination period | 40% |
Last updated: 31 October 2023
Dates & times
- Semester 2
Principal coordinator James McCaw Mode of delivery On Campus (Parkville) Contact hours 36 total, comprising one two-hour lecture and one one-hour practice class per week. Total time commitment 170 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020 Semester 2 contact information
Time commitment details
170 hours
Last updated: 31 October 2023
Further information
- Texts
Prescribed texts
TBA
Recommended texts and other resources
Edelstein-Keshet, L. Mathematical Models in Biology. McGraw Hill, 1987.
Murray, J. D. Mathematical Biology. Springer Verlag, 1990 (or the new 2 Volume Third edition, 2003).
Britton, N. F. Essential Mathematical Biology, Springer, 2003.
Dr Vries, G., Hillen T., Lewis, M., Muller, J. and Schonfisch, B. A Course in Mathematical Biology. SIAM, 2006. - Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Informal specialisation Mathematics and Statistics - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 31 October 2023