Biological Modelling and Simulation (MAST30032)
Undergraduate level 3Points: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
This subject introduces the concepts of mathematical and computational modelling of biological systems, and how they are applied to data in order to study the underlying drivers of observed behaviour. The subject emphasises the role of abstraction and simplification of biological systems, where required details of the underlying biological mechanisms will be provided. Combined with an introduction to Monte Carlo methods for statistical inference, students will learn how to identify common patterns in the rich and diverse nature of biological phenomena and appreciate how the modelling process leads to new insight into biological phenomena.
- Modelling: Deterministic and stochastic population-level dynamic models; and agent-based computational models will be introduced and studied. Indicative examples will be drawn from health (e.g. infectious diseases, cell tumor growth, developmental biology), ecology (e.g. predator-prey systems, sustainable harvesting, environmental decision making) and biotechnology (e.g. biochemical and metabolic models).
- Simulation: Sampling based methods (i.e. Monte Carlo methods) for parameter estimation and hypothesis testing will be introduced, and their importance in modern computational biology discussed.
Intended learning outcomes
On completion of this subject, students should:
- Appreciate how abstraction and simplification of biological systems through modelling can provide new insight into biological phenomena
- Be able to distinguish between different approaches to modelling (deterministic, stochastic, agent-based, statistical) and critically evaluate the suitability of these alternative approaches for particular biological problems
- Be able to develop computer programs that implement and solve simple models of biological phenomena
- Be familiar with the concept of statistical simulation and its role in testing hypotheses and understanding model behaviour
- Use models and their application to data to formally evaluate biological hypotheses
- Demonstrate an understanding of how to interpret and critique the biological modelling literature
Generic skills
- modelling skills: the ability to abstract and generalise from observations of a complex system, providing an alternative perspective on the problem
- numerical and computer simulation skills: the ability to design simple computer programs to solve models and test hypotheses
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 17 January 2025
Eligibility and requirements
Prerequisites
Students must complete the following subject:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20030 | Differential Equations | Semester 2 (On Campus - Parkville) |
12.5 |
Plus one of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20004 | Probability |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| MAST20006 | Probability for Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
Plus one of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP10001 | Foundations of Computing |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| COMP20005 | Intro. to Numerical Computation in C |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| PHYC20013 | Laboratory and Computational Physics 2 |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
Other evidence of competence in computer programming can also be provided to meet this pre-requisite, including passing a relevant Year 12 school subject, or a statement of achievement from a relevant MOOC, or passing a programming competency test administered by another University of Melbourne School.
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: 17 January 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Literature Review
| Week 10 | 20% |
Written Assignment- Three assignments, containing mathematical analysis and simulation, due at regular intervals during semester (week 4, 8 and 12).
| Throughout the teaching period | 30% |
A written examination
| During the examination period | 50% |
Last updated: 17 January 2025
Dates & times
- Semester 1
Coordinator Edward Hinton Mode of delivery On Campus (Parkville) Contact hours 48 hours: 24 x one-hour lectures (2 per week), 12 x two-hour practice classes (1 per week) Total time commitment 170 hours Teaching period 26 February 2024 to 26 May 2024 Last self-enrol date 8 March 2024 Census date 3 April 2024 Last date to withdraw without fail 3 May 2024 Assessment period ends 21 June 2024 Semester 1 contact information
Time commitment details
170 hours
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 17 January 2025
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Bachelor of Science Informal specialisation Science Discipline subjects - new generation B-SCI Major Computational Biology - 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: 17 January 2025