MANCHESTER, England, Jan 15. -- A British research team has developed a "robot scientist" that generates hypotheses about the function of particular genes in baker’s yeast, then designs and carries out experiments to test them, according to new research published in the journal Nature this week.
"This research is very exciting, as we have handed control of the experiment over to the robot, so there is no human intellectual input in the design of the experiments, or the interpretation of data," said Ross King, a professor at the University of Wales, Aberystwyth, who participated in the study, authored by Stephen Oliver of the University of Manchester. "There is increasing need for automation in the biological sciences, and although the problems we set for the robot were relatively simple, we have shown that it could be used to help solve real-world problems."
The researchers gave the robot the problem of discovering the function of different genes in baker’s yeast (Saccharomyces cerevisiae). The functions of about 30 percent of the 6000 genes in yeast are still unknown. Since many of these genes are thought to be common to the human genome, they could prove to be medically important in the future. The research involved using "knockout" strains of yeast that have had one gene removed. By observing how the yeast grows -- or doesn’t grow -- on defined chemical substrates, it is possible to start establishing different possible functions for the gene being investigated.
"This is like trying to understand what the different components in a car do by removing them one by one," said King.
The robot scientist generates a set of hypotheses from what it knows about biochemistry and then plans an experiment that will eliminate as many hypotheses as possible, as fast and as cheaply as possible. It conducts experiments by dispensing and mixing liquids and then measuring the growth of yeast using an adjacent plate reader that feeds the results back into the system. The robot then evaluates the results against the set of hypotheses, generates new hypotheses, and the process starts again -- the same type of cycle human scientists use to understand the world.
The researchers said although artificial intelligence has made a number of significant contributions to scientific discovery over the past 30 years, its general impact on experimental science has been limited -- but this may be about to change with the increased use of automation in scientific research. The need for automation is particularly important in the branch of science known as systems biology, where scientists are trying to understand how genes work together to form living cells.
For more information, visit: www.aber.ac.uk