At the Systems Biology Laboratory, University of Melbourne, we build and analyse mathematical models of biological processes, pathways and networks, and the cellular geometries within which these processes take place. We apply these models to problems in human health and physiology, including cancer and heart disease.

Our research falls broadly into two areas of systems biology: Computational Cell Biology and Regulatory Network Inference and Analysis. Within each of these broad areas we are pursuing several different projects, and developing common mathematical approaches and computational tools.

Computational Cell Biology:

Cellular function is determined by the complex network of interacting biological processes occurring within and between cells. Integrative modelling provides a means of assessing the quantitative contribution of each of these components, and assessing potential therapeutic strategies in disease. With a focus on understanding regulatory mechanisms, we are developing biophysically-based models of a range of cellular processes in relation to cardiac cell function and heart disease.

It is well established that cellular structure affects its function and that cellular function can in turn trigger structural remodeling. But can we predict the effect of a structural alteration on cellular function? Do we know the mechanism of how cellular function drives structural remodeling? Our mission is to address these questions by developing a computational modeling framework for simulating cellular systems biology in the 3D dimensional organization of the cell and its local environment. This framework will integrate state-of-the-art structural imaging data and quantitative functional data from the latest biological assays to create realistic simulations of structure-driven function and function-driven structural remodeling.

Regulatory Network Inference and Analysis:

Despite major advances in molecular biology and genetics, our understanding of the molecular networks that control cell function, and the way in which dysregulation of these networks promotes diseases, including cancer, remains far from complete. Developing avenues for new therapies and diagnostic tests relies on our comprehension of the molecular mechanisms regulating the progress of disease. A Systems Biology approach to studying disease progression is to construct network models from high-quality, high-throughput molecular data, and to interrogate these networks through computational analysis.

We are combining novel methods developed at the Systems Biology Laboratory with other techniques for network-based analysis, and applying this approach to datasets in a variety of cancers and tumour types to uncover potential targets for further investigation. We are also integrating clinical information such as patient history, survival, tumor grade and age, with molecular data to improve the predictive power and clinical applicability of this approach.