PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Characterising Enzymes for Information Processing: Towards an Artificial Experimenter
Chris Lovell, Gareth Jones, Steve Gunn and Klaus-Peter Zauner
In: 9th International Conference on Unconventional Computation, 21-25 Jun 2010, Tokyo, Japan.


The information processing capabilities of many proteins are currently unexplored. The complexities and high dimensional parameter spaces make their investigation impractical. Difficulties arise as limited resources prevent intensive experimentation to identify repeatable behaviours. To assist in this exploration, computational techniques can be applied to efficiently search the space and automatically generate probable response behaviours. Here an artificial experimenter is discussed that aims to mimic the abilities of a successful human experimenter, using multiple hypotheses to cope with the small number of observations practicable. Coupling this approach with a lab-on-chip platform currently in development, we seek to create an autonomous experimentation machine capable of enzyme characterisation, which can be used as a tool for developing enzymatic computing.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7099
Deposited By:Chris Lovell
Deposited On:02 March 2011