Autonomous Experimentation: Coupling Machine Learning with
Computer Controlled Microfluidics

Lovell, C. J., Jones, G. and Zauner, K. P. (2009) Autonomous Experimentation: Coupling Machine Learning with
Computer Controlled Microfluidics. In: ELRIG Drug Discovery, 7-8th September 2009, Liverpool.

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Abstract

Modelling biological systems is impaired by the cost of
experimentally obtaining the data required to build the
models. The resources available to perform experiments are
typically very limited compared to the size of parameter
spaces and the complexity of the systems under
investigation. However, the confluence of laboratory
automation and the low cost of computing resources make it
practicable to apply a closed-loop strategy, where each
experimental observation allows the computer to reason the
experiment to perform next. By doing so, autonomous
experimentation tries to capture the efficiency of
experimentalists in navigating a seemingly boundless space
of potential experiments. While computers can at most
represent a very limited knowledge context in which they
interpret their observations, they do have the benefit of
being able to contemplate many thousands of hypotheses in
parallel.

We will report on the development of an autonomous
experimentation setup that devises hypotheses and decides
on experiments which are then physically performed on a
microfluidic device, all without human interaction. The
purpose of our implementation is the investigation of
biomolecular substrates for novel computing devices, however
our approach is not specific to this application.

Funding by Microsoft Research is gratefully acknowledged.