Predictive Life Sciences
Predictive Life Sciences promotes interdisciplinary approaches to life science research. This includes systems biology (the use of mathematical modelling as part of an integrative loop with experimental techniques); synthetic biology (combining science and engineering in order to design novel biological functions and systems); and bioinformatics (computer analysis of biological data).
Rather than being devoted to a single set of research questions, Predictive Life Sciences aims to give greater prominence and cohesion to researchers using mathematics and/or computational modelling as part of biological and biomedical research, and to engage others in exciting, new collaborations.
Biological and biomedical research is facing a huge change. With the advent of high throughput methods generating fine-scale biomolecular data, scientists need to be able to predict possible outcomes in situations well beyond the span of available data, using reliable dynamical models that are both mechanistically-derived and biologically well-informed.
The University is uniquely placed to deliver this research - the theme’s activities promote and generate new collaborations between parts of the University that would otherwise be organisationally, physically, and/or intellectually separate. Research strengths include:
- biochemistry, cell biology and chemistry, including mechanistic and structural biology
- medical research, in particular neuroscience, cardiovascular science and social medicine
- statistics and artificial intelligence
- imaging and microscopy, including nanoscience
- behavioural and theoretical biology, including animal welfare and behaviour
- nonlinear science and mathematical modelling.