I am a postgraduate researcher with a very keen interest in mathematical modelling and simulation, especially of biological and physiological processes. I'm a firm believer that mathematics is what we need to understand and explain the very complex dynamics that act across multiple scales in these processes, given that experimentation is rarely able to examine different scales at the same time or comment on how one scale 'talks' to another. Currently I am working with Prof. Kevin Burrage on the problem of variability in the heart. The significant differences between the hearts of different members in the population leads to differential susceptibilities to pathological conditions and differential responses to treatments of these conditions. The most common mathematical machinery for simulating the very complex electrical activity of the heart are partial differential equations, which are deterministic and cannot naturally take this variability into account. We are using advanced statistical techniques to build data-calibrated populations of models, which act as in silico representations of a population that can elucidate what differences underlie groups within a population, or predict for example the success or failure of medical treatments.
Partial differential equations
populations of models
sequential Monte Carlo
Doctor of Philosophy (QUT)
Bachelor of Science (Mathematics/Physics) Hons. (QUT)
Everybody is different, and every body is different. Significant variability is a common feature of all of the physiological systems that compose the function of the human body, and understanding this variability is critical to explaining differences in susceptibility to pathological conditions, and also to explaining how medical treatments can potentially succeed or fail even when applied to treat the same condition.
Invited talks, refereed proceedings and other conference outputs
Lawson, B. A. J., Jakes D., Burrage K., Burrage P., Drovandi C. C., & Bueno-Orovio A.
(2019). Perlin noise for automatic generation of complex spatial patterns: an application to cardiac fibrosis. ANZIAM.
Lawson, B. A. J., Burrage K., Burrage P., Turner I., Santos R. W. dos, Jakes D., et al.
(2019). Mathematically Coping with Microfibrosis. Mathematical and Computational Biology Seminar Series.