Robot automates science
By
Kimberly Patch,
Technology Research NewsWhat better entity to assign repetitive scientific tasks, like working out the function of specific genes, than a robot?
A group of researchers from from the University of Wales, Robert Gordon University in Scotland, and the University of Manchester in England have put together a robot scientist that can devise a theory, come up with experiments to test the theory, carry out the experiments, and interpret the results.
The researchers put the system through its paces testing yeast genes, and also had a control group of computer scientists and biologists perform the same task. "The robot performed as well as the best humans," said Ross King, a professor of computer science at the University of Wales.
The researchers also showed that the robot scientist's method of selecting experiments was both faster and cheaper than choosing the cheapest experiment or simply choosing experiments randomly, said King. The robot scientist was three times cheaper than choosing the cheapest experiment and 100 times cheaper than random selection, according to King.
The approach could make scientific research less expensive, and could be applied within a few years in areas where the level of laboratory automation is already high, like drug design, said King. Today's state-of-the-art drug design uses brute force automation.
The robot scientist consists of a computer running artificial intelligence software, a fluid-handling robot, and a plate reader that checks the experimental results for variables like color.
The software allows the system to "infer new scientific hypotheses and plan efficient experiments to test those hypotheses," said King. The robot conducts experiments by dispensing and mixing liquids, then measuring the growth of yeast using the plate reader, which feeds the results back into the system, he said. There is no human input in the design of experiments or interpretation of data, King added.
The researchers gave the robot the task of testing gene functions in Saccharomyces cerevisiae, also known as baker's yeast. "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," said King.
The robot scientist looks for the function of a given gene using knockout strains of yeast that have had one gene removed. Observing how yeast grows, or does not grow, on surfaces that contain specific chemicals gives the investigator clues about different possible functions for the gene, he said. "This is like trying to understand what the different components in a car do by removing them one by one."
The robot evaluates the results against the set of hypotheses, interprets the results to eliminate hypotheses that are inconsistent with the data, generates new hypotheses, and repeats the process, said King. This is the same type of cycle human scientists use to understand the world, he said.
This standard process is relatively tedious for humans to carry out, however. The functions of about 30 percent of the 6,000 genes contained in baker's yeast are still unknown, said King. "With many of these genes thought to be common to the human genome, they could prove to be medically important," he said.
The software challenges involved in constructing the robot scientist included encoding all the relevant background information in a form that the system could use, developing a way of inferring possible hypotheses, and developing a way of inferring efficient experiments, said King. The engineering challenge was to put everything together into a working system, he added.
The researchers have demonstrated the system as a proof of principal. The next step is to show that the system can discover the function of genes that are currently unknown, said King.
The researchers drew on a 30-year history of research in artificial intelligence to make the system, said King. "The application of artificial intelligence to science is known as the field of scientific discovery," King said. " I think the main step forward of our work is... connecting such programs to a physical robotic system."
The work is solid, and important, according to Pat Langley, director of the Institute for the Study of Learning and Expertise located at Stanford University. It differs from previous systems that use artificial intelligence to control robotic equipment because it takes advantage of background knowledge, is aimed at designing efficient experiments, and uses a closed experimental loop so that the results inform the next hypothesis.
The work is part of a branch of artificial intelligence, dubbed active learning, that develops algorithms that weigh the odds of hypotheses being correct and the costs of potential experiments to determine the optimal series of experiments to eliminate all but the correct hypothesis.
In general, techniques for cognitive science and artificial intelligence should be as applicable to modeling and replacing scientific discovery and experiment design as for more mundane tasks, said Langley. The researchers work is a step along these lines, he said.
The ideas have precedents, said Raul Valdes-Perez, president of Vivisimo, Inc. and an adjunct associate professor of computer science at Carnegie Mellon University. But "I would say that this is the first convincing demonstration of a link between completely automated physical experimentation and hypothesis generation and testing," he said.
The robot scientist could be ready for practical use in three to six years, according to King. The first practical use is likely to be making drug design more efficient, he said.
Ross's research colleagues were Kenneth E. Whelan, Ffion M. Jones and Philip G. K. Reiser of the University of Wales, Christopher H. Bryant of the Robert Gordon University in Scotland, Stephen H. Muggleton of Imperial College, London in England, Douglas B. Kell of The University of Manchester Institute of Science and Technology (UMIST) in England, and Steve Oliver of the University of Manchester in England. The work appeared in the January 15, 2004 issue of Nature. The research was funded by the UK Biotechnology and Biological Sciences Research Council, and the UK Engineering and Physical Sciences Research Council.
Timeline: 3-6 years
Funding: Government
TRN Categories: Artificial Intelligence; Robotics; Applied Technology
Story Type: News
Related Elements: Technical paper, "Functional Genomics Hypothesis Generation and Experimentation by a Robot Scientist," Nature, January 15, 2004
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January 28/February 4, 2004
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