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University of Michigan researchers have developed a tool powered by artificial intelligence that can help them examine the behavior of a single molecule out of a sea of information in the blink of an eye—or at least overnight.

Understanding the behavior of single molecules is important: it can lead to knowledge of different cellular processes or track how diseases begin and progress. To track the behavior of single molecules, researchers tag the molecules with what's called a fluorophore. They excite these fluorophores with a laser, then use powerful microscopes to follow the behavior of the tagged molecules over time.

But identifying important behaviors of these tagged molecules requires sifting through the vast amounts of data this kind of microscopy often produces. This requires an incredible amount of time, attention and luck—and even then, researchers can miss important information.

To combat this, the U-M research team developed META-SiM. Unlike task-specific AI models which focus on a single problem, such as language translation, the researchers developed META-SiM as an AI foundation model.

Foundation models are large-scale AI models trained on many different kinds of experiments and analyses and a massive amount of data. This allows the tool to conduct a wide variety of analyses and scan through entire datasets to identify interesting behaviors that need further study.

The study, "Foundation model for efficient biological discovery in single-molecule time traces," is in Nature Methods. Jieming Li and Leyou Zhang, former U-M graduate student researchers, led the work.

While currently focused on the evolution of a signal strength over time, reflecting different states, down the road the researchers say META-SiM's algorithm can move beyond molecules and track other phenomena such as single particle diffusion, animal migration patterns or even the movement of asteroids through our solar system.

"The idea is to grow from single molecules to any larger scale. In principle, data have similarities to one another, and this AI algorithm is able to find out what those similarities are—as well as any deviations—no matter what scale you're working at," said senior study author Nils Walter, co-director of the Center for RNA Biomedicine.

"We could also track, say, the movement of wildebeests across Kenya and Tanzania, or even potentially celestial bodies moving across the universe."

The researchers developed META-SiM by training it on millions of simulated traces that imitate many types of behaviors that molecules display in the lab. But one real-world example of what META-SiM could track is a frequent cellular origin of human genetic diseases, Walter said.

Our body produces different types of proteins for different types of cells—skin, muscle, bone or eye and so on—and their function. One way it does this is by splicing pieces of genetic information from our DNA together in different ways. When fused together properly, this information, called exons, becomes a messenger RNA. This mRNA then expresses a protein tailored to a specific organ.

But 60% of human genetic diseases occur because of malfunctions that occur when this genetic information is spliced together. META-SiM could theoretically find sporadic instances where the mis-splicing occurs, and then suggest therapies to combat the mistake.

Co-author and U-M research scientist Alexander Johnson-Buck likens looking for the behavior of a single molecule to a complex game of "Where's Waldo?"—the children's book series in which the goal is to find one tiny person wearing a red hat, glasses and a red-and-white-striped sweatshirt among huge crowds of people, sometimes wearing similar clothes.

"Doing analysis on large data sets like our single molecule fluorescence microscopy data, is like doing a Where's Waldo? puzzle where you're trying to find Waldo," Johnson-Buck said. "Except maybe instead of a single page, it's hidden on dozens of pages or more, and maybe you don't know what Waldo looks like, and there might be multiple Waldos."

While META-SiM still cannot zero in on Waldo, what it can do is show scientists areas where Waldo might be hiding.

"It accelerates analysis and finds the key things that you would normally have to sift through the data for half a year or so to find basically overnight," Walter said.

According to Johnson-Buck, "you will still need an expert to interpret that discovery and to put it into context, but it makes the discovery aspect potentially a lot faster."