Nnews
Science | DOI: 10.1145/2160718.2160723
Neil Savage
Automating scientific
Discovery
Computer scientists are teaching machines to run experiments, make
inferences from the data, and use the results to conduct new experiments.

THe PoWer of computers to juggle vast quantities of data has proved invaluable to science. In the early days, machines performed calculations that would have taken humans far too long to perform by hand. More recently, data mining has found relationships that were not known to exist—noticing, for instance, a correlation between use of a painkiller and incidence of heart attacks. Now some computer scientists are working on the next logical step—teaching machines to run experiments, make inferences from the data, and use the results to perform new experiments. In essence, they wish to automate the scientific process.

 

Photogra Ph coUrteSy of aberySt Wyth UniVer Sity

One team of researchers, from Cornell and Vanderbilt universities and CFD Research Corporation, took a significant step in that direction when they reported last year that a program of theirs had been able to solve a complex biological problem. They focused on glycolysis, the metabolic process by which cells—yeast, in this case—break down sugars to produce energy. The team fed their algorithm with experimental data about yeast metabolism,

Adam, a robotic system, operates biological experiments on microtiter places.

 

along with theoretical models in the form of sets of equations that could fit the data.

The team seeded the program with approximately 1,000 equations, all of which had the correct mathematical syntax but were otherwise random.

The computer changed and recombined the equations and ranked the results according to which produced answers that fit the data—an evolutionary technique that has been used since the early 1990s. The key step, explains Hod Lipson, associate professor of computing and information science at Cornell, was to not only rank how the equations fit the data at any given point in the dataset, but also at points where competing models disagreed.

“If you carefully plan an experiment to be the one that causes the most disagreement between two theories, that’s the most efficient experiment you can do,” says Lipson. By finding the disagreements and measuring how much error the different equations produced at those points, the computer was able to refine the theoretical models to find the one set of equations that best fit the data. In some cases, Lipson says, the technique can even develop a full dynamical model starting from scratch, without any prior knowledge.

Lipson and then-doctoral student Michael Schmidt started developing Eureqa, the algorithm the work was