Modelling human diseases

For the large majority of diseases, the speed at which we are moving towards effective all-encompassing treatments is slow in comparison to disease incidence. Fortunately, virtual or in silico clinical trials are ideally situated to respond to the needs of the medical industry and assist in more rapid translation of treatments from discovery to approved. The focus of this domain is on using mathematical models of diseases as a base from which predictions can be drawn for the effectiveness of novel and alternate treatment protocols. Researchers within this domain work with experimentalists and clinicians at all aspects of treatment development and aim to determine optimal therapeutic regimes for individuals that may also be robust at the cohort level. Virtual clinical trials (or in silico ‘twins’) have been used in preclinical research to make ‘go or no go’ decisions. Through applying this technique more readily to models in oncology, cardiology and neuroimmunology, we may be able to increase the number of treatments that reach the approved stage by explored more exhaustively the protocols which may result in a robust response at the personal and cohort level. In this domain, modelling techniques are vast and include deterministic models (e.g. system of ordinary differential equations, systems of partial differential equations) as well as stochastic models (e.g. random ODEs, agent-based models)

Some of the projects within this research domain are provided below.

Evaluating potential efficacy of experimental cancer treatment

Mathematics has been used for some time now to assist in the development and understanding of novel cancer treatments as well as answer major questions surrounding cancer growth. Using either deterministic mathematical models of computational stochastic simulations (e.g. agent-based models) we can gain a better understanding of cancer growth, resistance and treatment. Members of this domain have examined the impact of novel therapeutics such as virotherapy, immunotherapy and combination chemotherapies on cancer cell growth.

Key publications

• Jenner, A.L., Smalley, M., Goldman, D., Goins, W.F., Cobbs, C.S., Puchalski, R.B., Chiocca, E.A., Lawler, S., Macklin, P., Goldman, A. and Craig, M., 2022. Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy. iScience, 25(6). http://doi.org/10.1016/j.isci.2022.104395
• Jenner, A.L., Cassidy, T., Belaid, K., Bourgeois-Daigneault, M.C. and Craig, M., 2021. In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity. Journal for immunotherapy of cancer, 9(2). http://doi.org/10.1136/jitc-2020-001387

Modelling COVID-19

Modelling community response to outbreaks, such as that for COVID-19, as well as hesitancy to uptake preventative measures, such as vaccines, is crucial for preventing disease spread in our community. In the first six months of the COVID-19 pandemic a range of different responses were observed across the world. Not only are interventions diverse, the changes of behaviour in populations around the world has also varied. To account for this, our epidemiological modelling describes the tendency for a population to change their interaction patterns as the reports of cases and deaths increase. By including these mechanisms, the sensitivity and timing of the communities response could be estimated and compared against the size of the outbreaks across the globe.

Key publications

• T Balasubramaniam, DJ Warne, R Nayak, K Mengersen. (2022) Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization. International Journal of Data Science and Analytics. DOI

• D.J Warne, A Ebert, C Drovandi, W Hu, A Mira, K Mengersen. (2020) Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic. BMC Public Health 20:1868 DOI medRxiv.org

Research in this domain is conducted in collaboration with members of the:

• Illawarra Health and Medical Research Institute (IHMRI), Wollongong, Australia
• National Centre for Tumour Disease, Heidelberg, Germany
• Brain and Mind Centre, The University of Sydney, Sydney, Australia
• Centre for Immunology and Infection Control, QUT, Brisbane, Australia