Computational biology

Transforming the genetic discovery landscape. We create affordable, robust computing methodologies and tools for the detection of predictive and diagnostic genomic signatures of interlinked DNA mutations from existing and forthcoming genome wide databases.

The official completion of the first draft of the human genome in February 2001 marked the arrival of systematic, quantitative scientific methods and modern engineering to the biology of living cells. A wide range of novel DNA-level measuring technologies which have emerged in the last ten years, herald the arrival of personalised medicine where the diagnosis, prognosis and treatment of diseases can be dealt with on a case-by-case basis, improving the efficacy of treatments while lowering costs and side effects. The realisation of these promises is conditioned on the harnessing of relevant information, hidden in unprecedented quantities of data which are being generated by the ever expanding array of genomic technologies. This harnessing requires the emergence of novel sciences and technologies and the “convergence” of disparate research fields including medicine, biology, chemistry, biochemistry, computer science, and mathematics merged with advanced engineering for the robust delivery of solutions. The Centre for Neural Engineering has created such a multi-disciplinary environment, where “convergence” is taking place. The Computational Biology group plays a vital role in the team providing expertise in developing and applying advance data processing techniques. Collectively, the team members have a wide experience in a broad spectrum of academic research fields, from pure mathematics, artificial intelligence, bioinformatics in research and the commercial deployment of diagnostic tests for cancer, heart disease and diabetes, to years of industrial experience in mainstream industries and both the creation and running of start-up companies.

In particular, the team has developed unique techniques for the “intractable” problem of genome wide search for epistasis in Genome Wide Associations Studies (GWAS). The solution leverages recent advances in gaming technology, specifically, the development of powerful Graphical Processing Units (GPUs) used in interactive games. This in combination with novel analytical techniques customized to take advantage of GPU capabilities enables the practical systematic search for signatures of interlinked of DNA mutations, for thousands of diseases and other traits of interest. This unique technology is now applied primarily to neurological and mental disorder datasets researched in the CfNE creating a unique, inter-disciplinary effort integrating multiple technologies and methods to identify novel biomarkers and drug targets for those diseases. The Computational Biology team is also collaborating on a number of other research projects with other biomedical laboratories in Australia and overseas, including breast and prostate cancers, autoimmune diseases, heart diseases and diabetes.

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