Software and Tools for the Simulation and Analysis of Biochemical Networks
The cell is an impressively complex jigsaw of biochemical networks whose components respond very precisely to external cues such as hormones and growth factors which control the cell’s behaviour. Understanding how these networks behave is a major challenge for the experimental biologist. Many of these networks have been found to be important in diseases such as cancer, heart disease and stroke, and are a focus for drug discovery efforts. However, the experimental testing of thousands of potential drug candidates consumes vast resources without the guarantee of any pay-back.
A user-friendly computing tool for the simulation and analysis of a diverse range of biochemical networks
An exciting, computer modeling based approach to understanding such biochemical networks is being led by Professor Gilbert and his colleagues at the University of Glasgow and the Beatson Institute. Bioinformaticians, computer scientists and cell biologists are bringing their expertise to bear on the challenge of developing a tool which should aid both basic and applied research, as well as demonstrating new and adventurous ways in which computational expertise can be applied to the field of bioinformatics.
Scottish Enterprise is funding two industrial PhD studentships in collaboration with Scottish biotechnology companies who are going to provide data on pharmacologically important biochemical networks
The approach which Professor Gilbert and colleagues are taking is quite distinct from the traditional path. The aim is to devise a model which is based on ‘concurrency theory’ – an approach commonly used to model computer and communication networks, but uniquely being applied here to receptor Elk SAP Gene Elk SAP Gene a biological problem. Concurrency theory has the strength of being able to model the connectivity between the components of a network as well as making logical judgements about the dynamic behaviour of the network. This is crucial for representing the complexities and uncertainties of biological systems.
A technology audit is planned to review the full potential of the software tools and databases that will arise from this project
- Improving the understanding of the effects of mutations
- Streamlining of the drug discovery process by more rapid lead-compound selection and identification of potential side-effects.
The project has already established links with the local biotechnology industry and intends to extend this through attending key conferences and delivering industry targeted seminars
The first challenge will be to represent the interacting biochemical networks in a manner that resembles an already developed application of concurrency theory. The team has begun by taking relatively simple and wellcharacterised biochemical network data and modeling them using this novel approach, with the aim of building up a generic model. There will be continuous cross-checking between the behaviour of the model and real experimental data, generated by the team itself, on a network of particular biomedical importance – the MAPK signalling network.
Cross-disciplinary understanding within the team is generating a unique pool of individuals with knowledge of both computing sciences and wet-lab biological sciences.
For further details on this project please see the project website or contact the team leader Professor David Gilbert at the Bioinformatics Research Centre, Department of Computing Science University of Glasgow email: firstname.lastname@example.org Tel: 0141 330 2563
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