This node is established under the biology and solutions domains and integrative systems and modelling research theme at the Charles Perkins Centre which aims to provide an interdisciplinary research environment to examine complex conditions such as cardiovascular diseases, obesity and diabetes. These complex diseases are caused and affected by many factors such as the genome, epigenome, environment and their interactions with each other; hence there is a clear research need to find solutions that sit at the intersection of these multiple disciplines.
Modelling of such complex systems and their interactions is possible today through integration of new omics methods with computational and bioinformatics-based tools. This node will encourage knowledge and technology sharing to promote strong interdisciplinary collaborations between clinicians, biologists and bioinformaticians including post-graduate students to achieve its goals.
Our collaboration team have complementary expertise in areas such as single cell isolation, library preparation, sequencing and back-end bioinformatics analysis. Approachable “technique champions” act as a first point of contact for researchers to discuss and plan experiments.
Analysis of large volumes of data generated can be particularly intimidating for new users. Our team has analytical expertise and provides guidelines for staff and students with minimal or no bioinformatics experience. Integration of mouse and human data or across multiple omics technologies requires medium to advanced bioinformatics capabilities.
Computational biology expertise closes the gap between biology, statistics and bioinformatics. Node members work cooperatively with the flow cytometry core, Sydney Informatics Hub and other research nodes to ensure completion of challenging experiments and derivation of meaningful conclusions from these complex datasets.
Drug resistance and toxicities affect nearly 50 percent of Australians with terminal illnesses such as cancers and neurodegenerative diseases. Our research will improve diagnosis of diseases by characterising accurately the feature of a given disease. This will hasten the adoption of personalised medicine to treat patients with more tailored therapeutics, leading to reduction in healthcare costs and improved patient care.