Computational biology and biophysics

This project is led by a multidisciplinary team of cell biologists, immunologists, mathematicians and physicists: Professors Sarah Russell, Damien Hicks, Andrew Clayton and Federico Frascoli and Dr Mirren Charnley.

The work aims to apply new methods to determine how extrinsic, intrinsic and stochastic inputs are coordinated to determine cell fates such as differentiation, death and proliferation. A major focus is on cells of the adaptive immune system (T cells), but the project also explore other systems such as development of the roundworm (c.elegans).

The project is expected to yield an experimental and analytical platform for further investigations into a broad range of biological questions, and to provide new knowledge of this fundamental problem. This platform should support further work that ultimately provides new models for tissue and immune cell regeneration, and new manufacturing platforms for therapies for humans and livestock, among other benefits.

The aim of this project, led by Dr Brett Cromer, is to develop and characterise an artificial synapse to advance our understanding of synaptic function and contribute to the development of neuronal network models. By combining principle from neuroscience, molecular biology and bioengineering, Dr Cromer’s research aims to address the challenges and explore the potentials of an artificial synapse in mimicking and studying synaptic communication in neural systems.

The overarching goal of this project, led by Professor Andrew Clayton, is to provide a world-class research and training environment at the interface of biology and physics. Professor’s  Clayton team aim to:

• use light to probe and perturb macromolecular structure, interactions and dynamics in living cells
• improve our understanding of diseases, such as cancer, at the molecular level
• provide fundamental knowledge that will ultimately improve wellbeing

Mathematical biology is an active and fast growing interdisciplinary area in which mathematical concepts and techniques are being applied to a variety of problems in the biological sciences. The mathematical biology group at the Department of Mathematics is very active in the mathematical modelling of a number of fundamental biological processes including:

• predator-prey models for the control of insect populations, animals and pests
• predictive modelling for the development and spread of infectious diseases and epidemics,
• models for mathematical immunology, including cancer therapies, autoimmune diseases and lymphocytes growth and differentiation
• dynamical equations for improved approaches against climate change, in particular in relation to algal blooms, fruit maturation, growth and development of ecosystems of different types
• stochastic and computational models of bacteria and active particles on thin films

This project, led by Professor Justin Leontini, investigates the mechanics of gas transport in the human airway. Its primary aim is to understand how oxygen can be provided to, and carbon dioxide removed from, a patient's lungs. The projected impact is the development and optimisation of medical ventilation equipment and practices that allow more patients to heal faster, especially those with damaged and delicate lungs.

The project uses supercomputers to simulate the turbulent flows in the airway, and to process and augment data from MRI experiments. The project collaborates with clinicians at the Murdoch Children's Research Institute, health scientists at the University of Melbourne, and research engineers at 4DMedicalR&D.

This project, led by Dr Nadezda Sukhorukova, investigates clustering methods for time series biological data. Using k-means methods and their modifications, Dr Sukhorukova studies how the brain activity measured by electroencephalogram (EEG) from epileptic patients can be assigned into distinguishing classes.

Electrical stimulation has been used to restore sensory functions in people who lost their vision or hearing. A novel way to stimulate neurons is to combine conventional electrical stimulation with targeted optical stimulation. The aim of this project is to explore the effects of electrical and light stimulation on neural responses in computer simulations.

The regulation of a homeostatic state within the body is crucial for our survival in the everchanging world. The regulation circuits hardwired within our bodies including the sympathetic nervous system (SNS) have been studied for decades.

Large populations, however, live with biological complications such as hypertension. Current analysis of the regulatory system employs microneurography to investigate muscular sympathetic nervous activity (MSNA) to infer the activity of the SNS.

A common analysis technique, burst frequency, gives insight into overall SNS activity. However, possible biomarkers could be overlooked in data losses attributed to this analysis technique. The aim of this project is to use machine learning techniques to classify healthy people and people with hypertension based upon their MSNA data.

Hypertension remains a major cause of death and disability from cardio-vascular disease, stroke and dementia. Directly and effectively targeting sympathetic outflow in hypertension remains problematic because the precise brain pathways responsible for driving sympathetic activity are not known.

Our hypothesis is that specific brain regions and their network connections drive the excess sympathetic outflow in the various forms of hypertension. The aim of this project is to explore this hypothesis by analysing recently recorded brain signals and sympathetic nervous outflow collected simultaneously, using magnetoencephalography (MEG) and muscle sympathetic nerve activity (MSNA) techniques, in healthy participants.

This project, led by Dr Kwong Ming Tse, investigates head injury biomechanics and its prevention. Using advanced computer head models, Dr Tse and his team develop next-generation protective equipment, including combat helmets and airbag bicycle helmet.

Dr Tse’s group also works with medical experts in defining medical problems in engineering terms and finding the best optimal engineering solutions that satisfy the clinical needs. Through his efforts, Dr Tse has been successful in providing an improved understanding of surgical outcomes.

Dr Luis Eduardo Cofre Lizama uses cutting-edge technologies such as virtual reality, motion analysis, and wearables for rehabilitation. Dr Cofre Lizama’s team works with a wide variety of stakeholders including clinicians, sports scientists and engineers. In this project, the team explores the use of non-linear metrics to study balance control and stability in a broad range of clinical populations.