I am fascinated by the natural world around us and how simple rules and heterogeneity can result in complex emergent behaviours. Heterogeneity is ubiquitous to life, from humans to bacterial communities. In many cases heterogeneity is more than just noise it enables resilience, job sharing, adaptability and robustness. To find more effective treatments for disease and to leverage heterogeneity in engineering we need to understand how the distribution and dynamics of systems affect optimal solutions in different scenarios. My work involves finding new ways of quantifying these dynamics and developing in silico models which can be used to optimise solutions and inform design principles.

I am currently an EPSRC doctoral prize fellow working on optimised and targeted control of bacterial biofilms. This work is conducted as part of the ‘Swarm engineering’ group in Engineering Mathematics and the ‘Biocompute lab’ which is part of biological sciences. 

My PhD in biological Physics was based in the new field of bacterial electrophysiology and around studying biofilms as active excitable matter. I combined experimental microbiology and fluorescence microscopy with mathematical tools for data analysis and agent-based in silico models. I used these methods to further understand collective behaviour and electrical communication in biofilms. Following my PhD worked as a Research Associate at the University of Bristol as part of a cross-disciplinary project aimed at using biomarkers for the early detection of brain tumours. I used a combination of continuum and hybrid in silico mathematical models to predict the levels of brain tumour biomarkers in patients.

The design space associated with drug development is rapidly evolving and expanding as is our ability to classify patients. This increase in potential is synonymous with a rapid expansion of the possible options. I have shown how in silico models can be used to explore this vast parameter space and find optimised quickly and cheaply. I have applied a range of modelling techniques (e.g., agent-base, continuum, machine learning) to a broad range of applications: nanotherapies for cancer, biomarkers for brain tumours, biofilm infections and snake antivenom treatments. To find more effective and efficient medical treatments as well as to leverage heterogeneity in engineering we need to start considering how the distribution and dynamics of systems affect optimal solutions in different scenarios.