We’re is looking to build a system-wide computational capacity that will enable us to deliver interdisciplinary solutions to innovative research questions using type 2 diabetes as a model disease system.

We intend to develop multiple computational solutions to form a series of digital knowledge associated with type 2 diabetes and utilise these to uncover mechanisms intrinsic to specific phenotypes or discover new metabolic sub-classes.

In this node, we aim to promote strong collaboration between clinicians, biologists, mathematicians, engineers and computational scientists.

It is well established that interdisciplinary research is critical in the future understanding of complex diseases and our ability to achieve targeted or personalised medicine.

Currently, the typical extent of interdisciplinary collaborations is often limited to a specific biological question that requires some computational solution. 

Modelling the intersection of cardiovascular disease, obesity and diabetes complex systems and their interactions is possible today through computational and bioinformatics-based tools.

The team engages researchers across three faculties with multiple disciplines including information technology, imaging, diagnostics, molecular prognostic biomarkers, statistics, biology and clinical sciences.

Methods developed here can eventually be extended to other non-communicable diseases and hence will enable future development of stratified medicine and targeted treatment.

Methods developed here can eventually be extended to other non-communicable diseases and hence will enable future development of stratified medicine and targeted treatment.

• Professor Lisa Bero
• Professor David James (Project Node Leader)
• Associate Professor Peter Kim
• Associate Professor Simon Poon
• Dr Arash Sadrieh
• Professor Stephen Twigg
• Professor Jean Yang (Project Node Leader)