The Robot Scientist Project
(invited lecture for DS 2005)
Author: Ross D. King
Affiliation: Department of Computer Science,
The University of Wales, Aberystwyth, Penglais, Aberystwyth,
Ceredigion, Wales, U.K.
Source: Discovery Science, 8th International Conference,
DS 2005, Singapore, October 2005, Proceedings,
(Achim Hoffmann, Hiroshi Motada, Tobias Scheffer, Eds.),
Lecture Notes in Artificial Intelligence 3735, pp. 16 - 25, Springer 2005.
Abstract.
The question of whether it is possible to automate the scientific
process is of both great theoretical interest and increasing practical
importance because, in many scientific areas, data are being generated
much faster than they can be effectively analysed. We describe a
physically implemented robotic system that applies techniques from
artificial intelligence to carry out cycles of scientific
experimentation. The system automatically originates hypotheses to
explain observations, devises experiments to test these hypotheses,
physically runs the experiments using a laboratory robot, interprets
the results to falsify hypotheses inconsistent with the data, and then
repeats the cycle. We applied this system to the determination of
gene function using deletion mutants of yeast (Saccharomyces
cerevisiae) and auxotrophic growth experiments. We built and tested a
detailed logical model (involving genes, proteins and metabolites) of
the aromatic amino acid synthesis pathway. In biological experiments
that automatically reconstruct parts of this model, we show that an
intelligent experiment selection strategy is competitive with human
performance and significantly outperforms, with a cost decrease of
3-fold and 100-fold (respectively), both cheapest and
random-experiment selection. We have now scaled up this approach to
discover novel biology. To achieve this we combined our
logical reasoning approach with bioinformatics. We have built a
logical model of all known S. cerevisiae metabolism. In this model
there are still reactions where the gene encoding the enzyme is
unknown.
We demonstrate that we can automatically hypothesize these genes,
and generate "wet" biological evidence that either confirms or
contradicts this hypothesis. This approach can also be used to
automatically test biological genome annotations.
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