Quantitative Biology Group

The Quantitative Biology Group is an interdisciplinary research team that works at the intersection of biological data, computational simulation, and cutting-edge mathematical modelling.

We use computational and mathematical techniques on newly-created data to deliver biological insight. Our work aims to transform biology into an engineering discipline. This pursuit of a ‘quantitative biology revolution’ requires novel computational and mathematical techniques; accordingly, our group advances theoretical, experimental, and ultimately clinical understanding of the natural world.

We favour models that are intuitive, simple, and revealing; interdisciplinary training that encompasses both theory and experimental data collection; and working on problems that really matter.  We’ve got room for more students and are open to new collaborations, so if you’ve got ideas or any of our themes sound interesting get in touch!

Biological characterization of next-generation therapeutics

Imagine a future where drugs are exquisitely targeted to specific parts of the body - for example, chemotherapeutics that only attack a tumour, or genetic therapies that fix a defective organ without toxic side effects. Synthetic biology and nano-engineering offer a myriad of techniques to develop these ‘targeted therapeutics’. End-results can range from engineering a virus to deliver RNA to decorating a nanoparticle with targeting ligands. While research groups and companies around the world are racing to develop new targeted therapeutics, there’s still a lot we don’t know about why or how a particular candidate therapy is working. Our approaches isolate biological kinetics of interest, enabling us to measure how well targeted therapeutics are performing and determine where, how, and why they’re failing.

Translating between biological scales - Predicting in vivo behaviour from in vitro data

Biological behaviour can be probed using experiments that vary in their complexity, reproducibility, and cost. In vitro experiments with cells are relatively well controlled, but it’s not clear how to translate them to predict what will happen in complex biological environments like blood, organ systems, or the whole-body. We’re developing cutting-edge approaches that aim to bridge this ‘in vitro - in vivo gap’ and fit together simple biological experiments to predict complex biological behaviour. Ultimately, our goal is to reduce the need for animal trials and de-risk human trials, making drug development safer, faster, and more cost-effective. This work will also help us understand complex biological environments, like the human body.

Automation and Standardization of Research

We believe that robotics and high-throughput data collection will underpin all future biological research. We’re developing new automation platforms to supercharge the collection of biological data. These designs are low-cost, easy to repair, and easy to iterate on - making research more equitable and giving scientists worldwide access to high-throughput equipment.  Additionally, it’s not enough to just produce a lot of data - data from different sources needs to be comparable. Standardisation and meta-research efforts complement automation. Our quantitative research focus has allowed us to define the information that should be reported about an experiment, improve experimental metrics, and suggest more effective experimental designs using mathematical models.

Project Opportunities

We're always happy to discuss projects and science if you've got an idea. We also have clear opportunities in the following areas. If you're interested, get in touch!

Developing an open-source liquid handling robot

Modelling how specific organs respond to targeted therapeutics

Determining intracellular trafficking kinetics of targeted therapeutics

Optimising experimental designs given external, laboratory-based constraints