Staff profile
Professor Liam O'Connor
Professor, Chair of Systems Biology and Bioinformatics
Faculty of Science, Technology and Engineering
School of Molecular Sciences AgriBio, the Centre for AgriBioscienceMelbourne (Bundoora)
- T: +61 3 9479 2537
- F: +61 3 9479 1266
- E: Liam.OConnor@latrobe.edu.au
- W: Biochemistry
Area of study
Biochemistry and Molecular Biology
Brief Profile
Professor Liam O'Connor has recently joined the La Trobe Institute for Molecular Science and AgriBio, the Centre for AgriBioscience as the Chair of Systems Biology and Bioinformatics. Immediately prior to this appointment, Professor O'Connor was the Director of Quantitative Biology at the Novartis Institutes for BioMedical Research (NIBR), where he had global responsibility for computational and systems biology for the Novartis research community.
Professor O'Connor began his career in computer science and mathematics before switching to biology. After his PhD at the Walter and Eliza Hall Institute in Melbourne, Australia and postdoctoral work at the MIT Center for Cancer Research, he joined Incyte Genomics, then MDS Proteomics, then Novartis Pharmaceuticals.
Research interests
Professor O'Connor's Systems and Computational Biology lab is interested in understanding the global changes associated with state changes in biological systems. Some of our areas of investigation are:
- Changes in cellular developmental and molecular pathways during tumour formation, maintenance and spread.
- Molecular genetics of microorganism populations in rumen flora.
- Building signalling pathway-based ontologies for biological systems, particularly those pathways dysregulated in disease.
- Computational method development, including the application of purpose-built biological computing environments to our data
Post-genomic biology is rapidly becoming an information–based discipline, and today’s biologist needs the skills to use, access and exploit electronic information just as urgently as laboratory-based expertise. The emerging field of Integrative Biology is gaining new insights into how biological systems work by bringing together biological datasets from disparate technologies such as gene expression microarrays, next-generation sequencing and quantitative tandem mass spectrometry. Merging these datasets requires us not just to re-examine our reductionist thinking about biology, but to develop technical expertise in areas such as:
- Computational Biology
- Molecular Biology
- Bioinformatics
- Applied biostatistics
- Development and application of biological data standards
- Data storage, including distributed storage
Research specialisation
- Systems and computational biology


