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Team:Newcastle University/Original Aims
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== Parallel Evolution == | == Parallel Evolution == | ||
| - | + | "An essential prerequisite for large-scale synthetic biology is accurate computational modelling of the biological system of interest. | |
| + | For small systems, involving few genes and gene products, in-depth knowledge of the biology of the organism may be a sufficient basis | ||
| + | upon which to design new genetic circuits, but larger systems have emergent global behaviours, and usually involve genes and interactions whose biological functions are poorly-understood. Computational modelling is essential for exploring the likely behaviour of large systems, and understanding the likely effects of modification to their components. | ||
| + | |||
| + | Modern high-throughput biology produces large amounts of data, often on a genome-wide scale, which should be valuable in constraining the initial design of computational models of large biological systems. However, such data is generated and stored in many formats in hundreds of large, diverse databases scattered around the world. Much of it does not immediately reach the literature, meaning that it is impractical for working biologists to access all of the data which could be valuable to them. Our current research involves large-scale data integration to automatically collate and update biological data into interactomes and integrated databases, putting large amounts of data in the biologist's hands in an easily accessed and manipulated format. We describe how we extending this research to support synthetic biology, by using data integration to build interactomes to act as a scaffold for constructing and parameterising dynamic models for large biological systems, using approaches based on evolutionary computation." | ||
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| + | '''Source:''' J. Hallinan, M. Pocock, M. Taschuk and A. Wipat. Data Integration to Constrain Computational Modelling in Synthetic Biology. [http://conferences.theiet.org/biosysbio/ BioSysBio IET 2008]. Poster presentation. | ||
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Revision as of 17:40, 17 June 2008
Newcastle University
GOLD MEDAL WINNER 2008
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Parallel Evolution
"An essential prerequisite for large-scale synthetic biology is accurate computational modelling of the biological system of interest. For small systems, involving few genes and gene products, in-depth knowledge of the biology of the organism may be a sufficient basis upon which to design new genetic circuits, but larger systems have emergent global behaviours, and usually involve genes and interactions whose biological functions are poorly-understood. Computational modelling is essential for exploring the likely behaviour of large systems, and understanding the likely effects of modification to their components.
Modern high-throughput biology produces large amounts of data, often on a genome-wide scale, which should be valuable in constraining the initial design of computational models of large biological systems. However, such data is generated and stored in many formats in hundreds of large, diverse databases scattered around the world. Much of it does not immediately reach the literature, meaning that it is impractical for working biologists to access all of the data which could be valuable to them. Our current research involves large-scale data integration to automatically collate and update biological data into interactomes and integrated databases, putting large amounts of data in the biologist's hands in an easily accessed and manipulated format. We describe how we extending this research to support synthetic biology, by using data integration to build interactomes to act as a scaffold for constructing and parameterising dynamic models for large biological systems, using approaches based on evolutionary computation."
Source: J. Hallinan, M. Pocock, M. Taschuk and A. Wipat. Data Integration to Constrain Computational Modelling in Synthetic Biology. [http://conferences.theiet.org/biosysbio/ BioSysBio IET 2008]. Poster presentation.
