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An Artificial Experimenter for Enzymatic Response Characterisation

An Artificial Experimenter for Enzymatic Response Characterisation
An Artificial Experimenter for Enzymatic Response Characterisation
Identifying the characteristics of biological systems through physical experimentation, is restricted by the resources available, which are limited in comparison to the size of the parameter spaces being investigated. New tools are required to assist scientists in the effective characterisation of such behaviours. By combining artificial intelligence techniques for active experiment selection, with a microfluidic experimentation platform that reduces the volumes of reactants required per experiment, a fully autonomous experimentation machine is in development to assist biological response characterisation. Part of this machine, an artificial experimenter, has been designed that automatically proposes hypotheses, then determines experiments to test those hypotheses and explore the parameter space. Using a multiple hypotheses approach that allows for representative models of response behaviours to be produced with few observations, the artificial experimenter has been employed in a laboratory setting, where it selected experiments for a human scientist to perform, to investigate the optical absorbance properties of NADH.
ISBN: 978-3-642-16183-4
42-56
Springer Verlag
Lovell, Chris
1ac8eed7-512f-4082-a7ab-75b5e4950518
Jones, Gareth
469d05ca-944e-43cd-91bc-12074c13848e
Gunn, Steve
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97
Lovell, Chris
1ac8eed7-512f-4082-a7ab-75b5e4950518
Jones, Gareth
469d05ca-944e-43cd-91bc-12074c13848e
Gunn, Steve
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97

Lovell, Chris, Jones, Gareth, Gunn, Steve and Zauner, Klaus-Peter (2010) An Artificial Experimenter for Enzymatic Response Characterisation. In, 13th International Conference on Discovery Science. Springer Verlag, pp. 42-56.

Record type: Book Section

Abstract

Identifying the characteristics of biological systems through physical experimentation, is restricted by the resources available, which are limited in comparison to the size of the parameter spaces being investigated. New tools are required to assist scientists in the effective characterisation of such behaviours. By combining artificial intelligence techniques for active experiment selection, with a microfluidic experimentation platform that reduces the volumes of reactants required per experiment, a fully autonomous experimentation machine is in development to assist biological response characterisation. Part of this machine, an artificial experimenter, has been designed that automatically proposes hypotheses, then determines experiments to test those hypotheses and explore the parameter space. Using a multiple hypotheses approach that allows for representative models of response behaviours to be produced with few observations, the artificial experimenter has been employed in a laboratory setting, where it selected experiments for a human scientist to perform, to investigate the optical absorbance properties of NADH.

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Published date: 18 November 2010
Organisations: Agents, Interactions & Complexity, Electronic & Software Systems

Identifiers

Local EPrints ID: 271593
URI: https://eprints.soton.ac.uk/id/eprint/271593
ISBN: ISBN: 978-3-642-16183-4
PURE UUID: f4ac44c6-751b-4d87-9981-6cdbf2633695

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Date deposited: 24 Sep 2010 15:46
Last modified: 19 Jul 2019 22:12

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Contributors

Author: Chris Lovell
Author: Gareth Jones
Author: Steve Gunn
Author: Klaus-Peter Zauner

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