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: