Mathematical Biology
The remarkable progress
being made in the biological sciences on many fronts is opening up lots
of new opportunities for joint research involving mathematicians. A
mathematical biologist with good applied and statistical modelling skills
will find themselves interacting with a great variety of people analysing
and synthesising data, developing theories, and constructing useful
predictive models. The information these models provide are essential
information for the management of those systems - whether that be in
the field of medicine, molecular biology or the environment.
At the University of Queensland we are actively involved in several ways: in computational
biosciences, in biostatistics, in mathematical ecology, and in physiological modelling.
For more information follow the links to people's personal web pages.
Physiological modelling of drug delivery; intra- and inter-cellular transport; pattern formation by competitive exclusion of cell types; evolution of urban populations.
PhD Projects
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Pattern formation in two dimensions by competitive exclusion
Bioinformatics, application of computational modelling, simulation and visualisation of complex systems; genetic regulatory process in cellular dynamics.
Biostatistics, application of statistical concepts and methods to health and medicine. Statistical epidemiology. Analysis of data from longitudinal studies of health and clinical trials.
Computational neuroscience, neural development, chemotaxis,
synaptic plasticity, maps in visual cortex.
Simulation and visualisation techniques for biological research
Phd projects
- Complex systems approaches to genetic regulation of plant development.
- Mathematical and computational methods for modelling the control of plant
development and function
- Computational modelling of intra-cellular processes
Scholarship available
Computational biology, protein simulations and calculations, genomic evolution.
Biostatistics, machine learning, development of discriminant and cluster analysis techniques for the classification of tissue samples, microarrays.
Stochastic modelling and simulation, mathematical population genetics,
statistical genetics.
Markov Chains, Probability Theory, Stochastic Modelling in Ecology, Parasitology
, Telecommunications, Epidemiology and Chemical Kinetics.
Population modelling, pest management, harvesting, ecological modelling.
Current Students
- Michael Bode, Analysis of metapopulation dynamics in the context of network theory. Advisors: Hugh Possingham, Kevin Burrage.
- Linda Cobiac, A fuzzy system for assessing sustainability of stormwater management scenarios. Advisors: Hugh Possingham, Milani Chaloupka, Jacqueline Robinson.
- Ben Gladwin, Long time scale simulations on large biological systems. Advisors: Thomas Huber, Kevin Burrage, Phil Pollett.
- Cindy Hauser, Modelling and monitoring the spatial and temporal dunamics of kangaroo populations. Advisors: Hugh Possingham, Anthony Pople, Phil Pollett.
- Ian Lenane, Mathematical modelling of large biomolecules - proteins, DNA and RNA. Advisors: Thomas Huber, Kevin Burrage, John De Jersey.
- Francis Pantus, Development and application of sensitivity analysis for high-dimensional ecosystem models. Advisors: Hugh Possingham, Bill Venables.
- Geoffrey Tacon, Mathematical modelling of liver kinetics. Advisors: Tony Bracken, Yuri Anissimov.