Course Title: Modelling with Differential Equations

Part A: Course Overview

Course Title: Modelling with Differential Equations

Credit Points: 12.00

Important Information:




Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015


City Campus


171H School of Science


Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2023

Course Coordinator: Professor Lewi Stone

Course Coordinator Phone: +61 3 9925 1728

Course Coordinator Email:

Course Coordinator Location: 15.04.21

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities


Required Prior Study

You should have satisfactorily completed following course/s before you commence this course.

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.

Course Description

This course introduces modern mathematical modelling approaches using dynamical systems (largely differential equation approaches) that are relevant in many different applications and, in particular, for biological and ecological systems. In recent years, mathematical modelling has indeed become one of the most important research tools in biological research. 

We will introduce the basic concepts and methods for analysis of linear differential equations and their intricacies under forcing and with resonances. This will lead to  studying nonlinear dynamical systems taking advantage of applied bifurcation and chaos theory. 

These tools will be used, in particular, to explore complex biological systems ranging from epidemics and infectious diseases (eg., COVID),  to the periodic processes driving the heart and brain or for studying ecological processes such as species persistence and biodiversity. 

The assessment of this course will include implementing the techniques encountered in the lectures in a programming environment such as Matlab. 

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP083 - Bachelor of Science, BP245 - Bachelor of Science (Statistics) and BH119 - Bachelor of Analytics (Honours):

Knowledge and Technical Competence:

  • use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral) and to tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

On completion of this course you should be able to:

  1. Analyse linear and nonlinear dynamical systems and use them for modelling real problems, in particular in biology;
  2. Identify and apply key concepts of stability and bifurcation theory;
  3. Analyse and simulate some of the key mathematical models that have become famous in the biological sciences;
  4. Identify and apply key concepts of modelling oscillatory systems and biological synchrony;
  5. Run biological models through an implementation with a programming environment such as Matlab.
  6. Collaborate with peers to analyse and solve mathematical problems using communication strategies that optimise both team and individual performance

Overview of Learning Activities

This course is taught through a mix of lectorial instruction, some computer laboratory exercises and assignments  Key concepts will be explained in detail in lectorials. There are homework exercises (2) and a group assignment which also involves an oral presentation.   

Overview of Learning Resources


RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.

Overview of Assessment

Assessment Tasks

Assessment Task 1: Online quizzes
Weighting 20% 
This assessment task supports CLOs 1, 2

Assessment Task 2: In class mid semester problem based timed test I
Weighting 25% 
This assessment task supports CLOs 1, 2, 3, 4,

Assessment Task 3: Team based group assignment.  
Weighting 25%
This assessment task supports CLOs 1, 5, 6 

Assessment Task 4  Online timed summative test
Weighting 30% 
This assessment task supports CLOs 1, 2, 3, 4

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.