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Team:Newcastle University/Evolutionary Algorithm
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Newcastle University
GOLD MEDAL WINNER 2008
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Evolutionary Algorithm
Aim:
Develop a system that will evolve genetic circuits represented as networks that meet the functional requirements specified by the team's target application.
Traditional genetic engineering techniques have built small biological circuits by hand. However, this approach will not scale to whole-organism engineering. For synthetic biology at this scale computational design will be essential. “Soft” computing techniques such as evolutionary computation and computational intelligence were developed to handle exactly this sort of large, complex, hard-to-define problem.
Objectives:
- Reads parts list from parts repository
- Reads constraints on parts assembly (from Constraints Repository)
- Evolutionary algorithm assembles part models to a larger model
- Simulates the behaviour of the composite model
- Reads desired 'input' behaviour from Workbench
- Reads desired 'output' behaviour from workbench
- Assesses fitness
- Mutates the model
- Output the fittest model as CellML (to workbench)
Contributors:
Lead: Team:Newcastle University/Mark Wappett
