An integrated systems level approach to bacterial adaptations to stress
MICROSYST will exploit well-characterised bacterial systems. The availability of total genomic sequences, robust predictions of gene function and the relative ease with which large populations can be manipulated and sampled, should allow rapid progress during the limited lifetime of studentship projects. Our biological focus is on the mechanisms by which bacteria (Escherichia coli, Propionibacterium acnes and Neisseria species) adapt to changing and stressful environments, such as surfaces, including skin, in diverse environments such as bioreactors, wastewater treatments and during pathogenesis. Our modelling efforts will focus on providing a principled framework in which deterministic, ODE-based models can be supplemented and tuned by the use of experimental data gathered by the biologists. A unifying theme is the adaptability mediated in each species by two-component regulatory systems (TCSs) but at increasing levels of complexity in the three bacteria (Neisseria, 3-4 TCSs; Propoionibacterium acnes, 9; Escherichia coli, c.30). Delineation of component interactions at microscopic and macroscopic levels will generate reconstructed biochemical reaction networks that underlie cellular functions.
The White Rose Studentship Network
This network was established in Oct 2008 as one of the White Rose Studentship Networks between the Universities of Leeds, Sheffield and York. The people involved are:
| Biology | Computing | |
| Sheffield |
Robert Poole Jeff Green Alison Graham (SysMO) Matt Rolfe (SysMO) Tacita Nye |
Guido Sanguinetti Richard Clayton Shehzad Asif |
| York |
James Moir Gavin Thomas Andrew Schofield |
Jamie Wood |
| Leeds |
Keith Stephenson Keith Holland Richard Bojar John Wright Tom Lin |
Chris Needham Netta Cohen Carmen Molina-Paris Christine Staiger Margaritis Volotis |
Systems Biology is a new approach to the study of living systems. By integrating quantitative experimental data with predictive computational modelling, systems biology is already revolutionising the way in which biologists think, and is making the outputs of biological research more accessible to industry and policy-making.