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 labor...

• Type: Journal
• Year: 2010
• Where: DIS
• Authors: Chris Lovell, Gareth Jones, Steve R. Gunn, Klaus-Peter Zauner
• Keywords: Artificial Intelligence Techniques, Biological Response Characterisation, DIS 2010, Hypotheses, Theoretical Computer Science