An artificial experimenter for automated response characterisation

Biology exhibits information processing capabilities, such as parallel processing and context sensitivity, which go far beyond the capabilities of modern conventional electronic computation. In particular the interactions of proteins such as enzymes are interesting, as they appear to act as efficient biomolecular computers. Harnessing proteins as biomolecular computers is currently not possible, as little is understood about their interactions outside of a physiological context. Understanding these interactions can only occur through experimentation. However, the size and dimensionality of the available experiment parameter spaces far outsize the resources typically available to investigate them, creating a restriction on the knowledge acquisition possible. To address this restriction, new tools are required to enable the development of biomolecular computation. One such tool is autonomous experimentation, a union of machine learning and computer controlled laboratory equipment within in a closed-loop machine. Both the machine learning and experiment platforms can be designed to address the resource problem. The machine learning element attempts to provide techniques for intelligent experiment selection and effective data analysis that reduce the number of experiments required to learn from. Whilst resource efficient automated experiment platforms, such as lab on-chip technology, can minimise the volumes of reactants per experiment. Here the machine learning aspect of autonomous experimentation is considered. These machine learning techniques must act as an artificial experimenter, mimicking the processes of successful human experimenters, through developing hypotheses and selecting the experiments to perform. Using this biological domain as motivation, an investigation of learning from a small set of noisy and sometimes erroneous observations is presented. Presented is a principled multiple hypotheses technique motivated from philosophy of science and machine learning for producing potential response characteristics, combined with active learning techniques that provide a robust method for hypothesis separation and a Bayesian surprise method for managing the exploration{exploitation trade-off between new feature discovery and hypothesis disproving. The techniques are validated through a laboratory trial where successful biological characterisation has been shown

Name dc:type.qualificationname

Ph.D.

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

University of Southampton

Year dc:date.issued

2011

Author dc:creator

• Lovell, Christopher James

Advisor dc:contributor.advisor

• Zauner, Klaus-Peter