PhD Projects

We are currently recruiting PhD and research masters students for a number of projects. The following list describes some of the available project areas.

Please contact Prof Edmund Crampin ( to find out more about these project areas, and to discuss application processes and funding opportunities.

Modelling emergent behaviour in engineered cell populations

Supervisers Dr Stuart Johnston, Dr Matt Faria, Prof Edmund Crampin

Biology is full of examples where complex behaviour exhibited by a cell population emerges from relatively simple behaviour at the level of an individual cell. Bacterial populations can exhibit synchronised behaviour or express large amounts of bioluminescence due to interactions between individual bacteria. Synthetic biology is a promising area of research where cells are engineered to exhibit desired behaviour. However, predicting the emergent behaviour of a population from the individual cell behaviour is not straightforward.

As such, it is difficult to rationally design synthetic cells that give rise to specific emergent behaviour. Individual-based mathematical models can account for cell-cell interactions, cell-environment interactions and heterogeneity within a cell population, while still describing the complex gene regulatory network that dictates an individual cell’s behaviour. Further, these models allow for the resulting emergent behaviour of the population to be examined.

This project will involve the development of an individual-based modelling framework, suitable for examining how changes in the gene regulatory network influence emergent behaviour. The modelling framework will be applied to examine the collective navigation present in swarming Bacillus subtilis populations. The approaches developed in this project will be relevant for understanding the mechanisms behind antibacterial resistance and novel drug delivery techniques.

Whole cell modelling: design tools for synthetic biology

Supervisers Prof Edmund Crampin, Prof Michael Stumpf, Dr Michael Pan, Prof Peter Gawthrop

Whole cell modelling has the aim of developing comprehensive, predictive models of cells for biotechnology and biomedical applications. Whole cell models will underpin design in synthetic biology, and allow researchers to predict the effects of genetic manipulations without the need to do challenging experiments.

However, understanding how biological cells work requires combining information from many different domains – biochemical, electrical, mechanical, and across different measurement technologies – proteomics, metabolomics, transcriptomics, electrophysiology, and so on. Working out how these different aspects of cells interact is a major challenge. Predicting what happens if the system is altered (in disease, or through biotechnology, for example) is even harder.

We are applying a set of techniques which engineers have developed to understand multi-domain man-made systems, to biological systems. This approach tracks the flow of energy through a cell’s network of biochemical reactions. This allows us to effectively combine different aspects of the cell within the same unifying mathematical description. One of the reasons engineers have used these methods for man-made systems is because they want to be able to control them. In addition to understanding cell function, ultimately this approach will lead to the ability to more easily and reliably modify biological systems with predictable outcomes – so that we can better understand and hence treat disease, and so that we can design new biological systems for biotechnological and biomedical applications, and by applying engineering design principles we can design new biological systems (synthetic biology).

Join a team here at the University of Melbourne including Prof Edmund Crampin, Prof Michael Stumpf, Prof Karin Verspoor, Prof Peter Gawthrop, Dr Michael Pan, and Dr Heejung Shim, and learn how to model whole cells as part of a multidisciplinary & supportive research team!

The virtual heart cell project

Supervisers Prof Edmund Crampin, Dr Vijay Rajagopal

This project brings together a multi-disciplinary team of scientists in physiology, computational biology and cellular mechanics to gain a more comprehensive, integrated understanding of how heart cell structure and function determines the heart health and disease.

We are developing a three-dimensional, biophysically realistic computational model of heart cell structure and function (a ‘virtual heart cell’) with two specific aims:

  1. To use the models to understand the effect of sub-cellular structural and biochemical alterations on cardiac cell performance;
  2. To explore novel treatment strategies using this new virtual cell environment that enables us to appreciate the integrated and multi-faceted response of the cell to a range of clinical therapies.

Modelling cell-nanoparticle interactions for nanomedicine

Supervisers Prof Edmund Crampin, Dr Matt Faria, Dr Stuart Johnston

Determining the affinity of a nanoparticle for a cell is an important step in characterizing the biological activity of that nanoparticle. This is usually determined in vitro by incubating cells with a solution of particles and then measuring cellular association or uptake. Comparing these measurements for different particle types is desirable. Unfortunately, it does not make sense to compare them directly, as the physical properties of the system can lead to substantially different amounts of particles being presented to the cell. For instance, consider a solution with particles that sediment rapidly compared to a well-dispersed colloidal suspension of particles. Clearly, if incubated in vitro with cells at the bottom of a well, the former solution will sediment onto the cells and a greater percentage of particles will be presented. This is recognized as a problem in the field, but attempts to characterize, control for, or model these effects are rare.

As part of the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, we are working with a variety of experimental groups to build computational models using to predict bulk movement of particles in solution and interaction / uptake by cell populations. This will allows us to predict the presentation of particles to cells and subsequent cell-particle associations, based on nanoparticle physical properties, and by extension, to control for these physical effects and compare different particles.

Genome-wide metabolic network modelling: targeting host-parasite interactions

Supervisers Prof Edmund Crampin, Prof Malcolm McConville

Leishmania is a parasite that causes a spectrum of devastating human diseases in the tropics and subtropics. The parasite targets macrophages, one of the major cell types in the human body’s immune system, where they reside within the lysosome compartment that is normally involved in killing invading pathogens. There is intense interest in understanding how the Leishmania parasite survives in these cells and, in particular with the view of identifying new drug targets and better therapies. One of the key areas of research is understanding how the parasite metabolism interacts with the host cells.

We will be building detailed mathematical models of Leishmania metabolism to be able to better predict the consequences of genetically or chemically inhibiting particular enzymes or pathways. We will use data from comprehensive metabolomics experiments, being undertaken by Prof Malcolm McConville’s team at Bio21 Institute, which measure the rates of different parts of the metabolic network under different conditions using advanced mass spectrometry approaches. This information will then be used to explore key parts of the parasite metabolism in order to understand how the parasite can be targeted pharmacologically without harming the host.