eLearning

Ross D. King,
Jem Rowland,
Stephen G. Oliver,
Michael Young,
Wayne Aubrey,
Emma Byrne,
Maria Liakata,
Magdalena Markham,
Pınar Pir,
Larisa N. Soldatova,
Andrew Sparkes,
Kenneth E. Whelan,
Amanda Clare

The basis of science is the hypothetico-deductive method and the recording of experiments in
sufficient detail to enable reproducibility. We report the development of Robot Scientist “Adam,”
which advances the automation of both. Adam has autonomously generated functional genomics
hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses
by using laboratory automation. We have confirmed Adam’s conclusions through manual
experiments. To describe Adam’s research, we have developed an ontology and logical language.
The resulting formalization involves over 10,000 different research units in a nested treelike
structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical
description. This formalization describes how a machine contributed to scientific knowledge.

omputers are playing an ever-greater role
in the scientific process (1). Their use to
control the execution of experiments con-
tributes to a vast expansion in the production of
scientific data (2). This growth in scientific data,
in turn, requires the increased use of computers
for analysis and modeling. The use of computers
is also changing the way that science is described
and reported. Scientific knowledge is best ex-
pressed in formal logical languages (3). Only
formal languages provide sufficient semantic
clarity to ensure reproducibility and the free
exchange of scientific knowledge. Despite the
advantages of logic, most scientific knowledge is
expressed only in natural languages. This is now
changing through developments such as the
Semantic Web (4) and ontologies (5).
A natural extension of the trend to ever-greater
computer involvement in science is the concept of
a robot scientist (6). This is a physically imple-
mented laboratory automation system that exploits
techniques from the field of artificial intelligence
(7–9) to execute cycles of scientific experimenta-
tion. A robot scientist automatically originates
hypotheses to explain observations, devises exper-
iments to test these hypotheses, physically runs the
experiments by using laboratory robotics, inter-
prets the results, and then repeats the cycle.
High-throughput laboratory automation is trans-
forming biology and revealing vast amounts of
new scientific knowledge (10). Nevertheless, ex-
isting high-throughput methods are currently in-
adequate for areas such as systems biology. This
is because, even though very large numbers of
experiments can be executed, each individual ex-
periment cannot be designed to test a hypothesis
about a model. Robot scientists have the potential
to overcome this fundamental limitation.
The complexity of biological systems neces-
sitates the recording of experimental metadata in
as much detail as possible. Acquiring these meta-
data has often proved problematic. With robot
scientists, comprehensive metadata are produced
as a natural by-product of the way they work.
Because the experiments are conceived and ex-
ecuted automatically by computer, it is possible
to completely capture and digitally curate all as-
pects of the scientific process (11, 12).
To demonstrate that the robot scientist meth-
odology can be both automated and be made
effective enough to contribute to scientific knowl-
edge, we have developed Robot Scientist “Adam”
(13) (Fig. 1). Adam’s hardware is fully automated
such that it only requires a technician to period-
ically add laboratory consumables and to remove
waste. It is designed to automate the high-
throughput execution of individually designed
microbial batch growth experiments in micro-
titer plates (14). Adam measures growth curves
(phenotypes) of selected microbial strains (geno-
types) growing in defined media (environments).
Growth of cell cultures can be easily measured in
high-throughput, and growth curves are sensitive
to changes in genotype and environment.
We applied Adam to the identification of
genes encoding orphan enzymes in Saccharomy-
ces cerevisiae: enzymes catalyzing biochemical
reactions thought to occur in yeast, but for which
the encoding gene(s) are not known (15). To set
up Adam for this application required (i) a
comprehensive logical model encoding knowl-
edge of S. cerevisiae metabolism [~1200 open