On completing a Ph.D. in the immunobiology of virus infection, my research emphasis shifted to virus - host interactions, and the molecular mechanisms of viral pathogenesis concerning the indigenous Australian alphavirus, Ross River (RRV). RRV is the leading cause of mosquito-borne viral disease in Australia, with close relatives around the world (e.g. Chikungunya virus) of human and animal health importance. This research was largely conducted in the laboratory, with major published findings including the elucidation of the molecular basis of antibody-dependent enhancement (ADE) of viral infection (a suspected key risk factor for dengue haemorrhagic fever and dengue shock syndrome) and a model of long-term viral persistence in host cells (macrophage).
Prior to my curent position at The ANU, I was Associate Professor of Bioinformatics (and prior to that Senior Lecturer in Molecular Biology) at The University of Canberra. I have also held positions with the Department of Microbiology and Immunology at the University of North Carolina (Chapel Hill), USA (ongoing with Carolina Vaccine Institute), the Australian Institute of Mucosal Immunology, and a post-doctoral term in the Faculty of Science, The ANU.
I have clinical experience in pathology laboratories, with B.Sc. and Honours (1st Class) degrees from The University of Newcastle and a doctorate from The John Curtin School of Medical Research, ANU.
There are two research foci being explored via integrative strategies that utilise machine learning, genetic data and biological validation through human pathology testing. These projects are:
- Hepatitis B virus (HBV) infection and/or disease. This project has extended into liver disease generally and the role of routine liver function tests (LFT) in diagnosis - broadly in silico virology and pathology;
- Discovery of genetic and laboratory (pathology) markers of myalgic encephalomyelitis (ME), also known as "Chronic Fatigue Syndrome" (CFS/ME). The genetics phase of this project is planned to commence in 2016 and continue into 2017. Also, pilot data from a 2011 - 2014 project has been analysed and informs a larger ongoing validation study on CFS/ME biomarker networks and immunology;
- Our group is also interested in conducting Systematic Review on biomedical topics of mutual interest.
These projects also contribute to the development of alternatives to animals in fundamental biomedical research.
Available student projects
As of 2016, there will be data available from past and current CFS/ME projects that will benefit from student involvement. A background in medical science and experience in statistics and/or data mining will be required. Other CFS/ME projects are available using systematic review methods and meta-analyses.
A project may also be available in the biological validation of machine learning models for HBV infection and disease, suitable for a medical science graduate with experience in laboratory diagnosis or pathology testing - involvement in this project will require specialised training and vaccination prior to commencement, due to contact with potentially infected human samples.
Through research collaborator Associate Professor Mauricio Arcos-Burgos (JCSMR), future projects for students with an interest in human genetics will be available, primarily as in silico investigations.
(1) Langley G, Austin CP, Balapure AK, Birnbaum LS, Bucher JR, Fentem J, Fitzpatrick SC, Fowle III JR, Kavlock RJ, Kitano H, Lidbury BA, et al. Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research. Environmental Health Perspectives. 2015 Nov;123(11):A268.
(2) Book Chapter - Animal Models of Alphavirus-induced Inflammatory Disease. Lara J. Herrero, Adam Taylor, Pierre Roques, Brett A. Lidbury and Suresh Mahalingam. In Alphaviruses. Eds S. Mahalingam, L. Herrero and B. Herring. Caister Academic Press (Poole, UK). 2016.
(3) Lidbury BA, Richardson AM, Badrick T. Assessment of machine-learning techniques on large pathology data sets to address assay redundancy in routine liver function test profiles. Diagnosis. 2015 Feb 1;2(1):41-51.
(4) Badrick T, Richardson AM, Arnott A, Lidbury BA. The early detection of anaemia and aetiology prediction through the modelling of red cell distribution width (RDW) in cross-sectional community patient data. Diagnosis. 2015 Sep 1;2(3):171-9.