VISITING PROFESSOR Dr Stuart Johnston graduated with a Bachelor of Mathematics (Hons) in 2013 from the Queensland University of Technology. In 2017, he completed a PhD in Mathematical Biology under the supervision of Prof. Matthew Simpson at the Queensland University of Technology, focused on mathematical models of collective cell behaviour. Dr Johnston joined Prof. Edmund Crampin's Systems Biology Laboratory as a Postdoctoral Research Fellow at The University of Melbourne and the Australian Research Council (ARC) Centre of Excellence in Convergent Bio-Nano Science and Technology. During this time, he focused on developing mathematical models of the interactions between nanoparticles and cells, to understand and design future medical treatment delivery options. In 2020, Dr Johnston commenced an ARC Discovery Early Career Research Award Fellowship. In this Fellowship, he researched the ability of organisms to navigate through environments in the presence of uncertainty and noise, with a focus on cell transport and whale migration. Following the Fellowship, Dr Johnston took up a position as a Senior Lecturer in 2023. Mathematical biology is a rapidly evolving area of mathematics. As our understanding of biology grows, so too does the breadth of mathematics required to design techniques and tools to interpret and predict biological processes. This course will cover a broad range of mathematical techniques that have been used to describe biological processes. These techniques will be introduced in the context of developing and exploring mathematical models of specific biological phenomena. Moreover, the subjective modelling choices that have to be made when designing new mathematical models will be explored. As part of this exploration, connections between classes of models under particular mathematical limits will be highlighted, and the consequences of selecting one mathematical framework over another. It will also be demonstrated how different modelling choices influence the tools required to estimate parameters from biological data.

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Mathematical models (with corresponding analysis techniques) that will be covered in this course may include: discrete-time models, dynamical systems and ordinary differential equation models, individual-based models, and partial differential equation models. Biological processes that will be explored in this course via mathematical models may include: population dynamics in ecology, infectious disease dynamics, collective cell behaviour, collective animal migration, and tumour growth.

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P.D.2-2 - Ottobre