Functional genomic hypothesis generation and
experimentation by a robot scientist
Nature, (15 January 2004)DOI: doi:10.1038/nature02236
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. Here we apply the 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.
