Next generation bioscience centres in Edinburgh, Nottingham and Oxord awarded £27M
The new centres focus specifically on: dynamic biological systems, such as biological clocks, the nature and behaviour of plant roots and signalling pathways in bacteria and yeasts. They will also have an 'outreach' function, stimulating systems biology research across the whole of biological science research. They will combine experimentation with computer simulations in order to process experimental results, design new experiments and to generate generic and predictive 'solutions' that are widely applicable. The centres will also produce a new generation of young scientists able to work at the interface between experimentation, modelling and theory outside the constraints of current disciplinary boundaries.
The University of Edinburgh is committed to a multidisciplinary approach that integrates the life sciences with the physical sciences. The Centre for Systems Biology at Edinburgh aims to model dynamic biological systems - focusing on RNA metabolism, the interferon pathway and circadian rhythms - and will bring together researchers from informatics, molecular plant sciences, medicine and cell and molecular biology among others.
The Centre for Plant Integrative Biology (CPIB) at the University of Nottingham will develop a 'virtual root' which will serve as an exemplar for using Integrated Systems Biology to model multi-cellular systems. The Nottingham Centre will integrate advanced experimental and imaging approaches with innovative mathematical, engineering and computer science research in conjunction with Rothamsted Research and several international collaborators.
The Oxford Integrative Systems Biology Centre will tackle a range of biological problems concerning network pathways. A major interdisciplinary initiative, the centre involves members of the Departments of Biochemistry, Pathology, Chemistry, Mathematics, Statistics, Engineering and Computation. The research will look at the complex language which single cell organisms use to control their behaviour. The aim of the project is to develop robust predictive models of these highly complex models.
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