New York, May 5 (IANS) Researchers have developed an artificial intelligence (AI) system that can help robots to conduct automated scientific experiments — as many as 10,000 per day — potentially driving a drastic leap forward in the pace of discovery in areas from medicine to agriculture to environmental science.
Reported in the journal Nature Microbiology, the novel AI platform, dubbed BacterAI, mapped the metabolism of two microbes associated with oral health — with no baseline information to start with. Bacteria consume some combination of the 20 amino acids needed to support life, but each species requires specific nutrients to grow.
The team from the University of Michigan wanted to know what amino acids are needed by the beneficial microbes in our mouths so they can promote their growth.
“We know almost nothing about most of the bacteria that influence our health. Understanding how bacteria grow is the first step toward reengineering our microbiome,” said Paul Jensen, Assistant Professor of biomedical engineering at the varsity.
Figuring out the combination of amino acids that bacteria like is tricky, however. Those 20 amino acids yield more than a million possible combinations, just based on whether each amino acid is present or not.
Yet BacterAI was able to discover the amino acid requirements for the growth of both Streptococcus gordonii and Streptococcus sanguinis.
To find the right formula for each species, BacterAI tested hundreds of combinations of amino acids per day, honing its focus and changing combinations each morning based on the previous day’s results. Within nine days, it was producing accurate predictions 90 per cent of the time.
Unlike conventional approaches that feed labelled data sets into a machine-learning model, BacterAI creates its own data set through a series of experiments.
By analysing the results of previous trials, it comes up with predictions of what new experiments might give it the most information. As a result, it figured out most of the rules for feeding bacteria with fewer than 4,000 experiments.
Little to no research has been conducted on roughly 90 per cent of bacteria, and the amount of time and resources needed to learn even basic scientific information about them using conventional methods is daunting. Automated experimentation can drastically speed up these discoveries. The team ran up to 10,000 experiments in a single day.
But the applications go beyond microbiology. Researchers in any field can set up questions as puzzles for AI to solve through this kind of trial and error.