University of Michigan researchers have developed artificial intelligence that can solve molecular Where’s Waldo puzzles, tracking behaviours from single molecules to animal migrations and potentially celestial bodies.
The AI foundation model, called META-SiM, can examine single-molecule behaviour in fluorescence microscopy data that would normally take researchers six months to analyse. Understanding single-molecule behaviour helps track how diseases begin and progress.
Current methods require researchers to tag molecules with fluorophores, excite them with lasers, then use powerful microscopes to follow behaviour over time. But identifying important behaviours requires sifting through vast amounts of data with incredible time, attention and luck.
META-SiM was trained on millions of simulated traces that imitate molecule behaviours in laboratories. Unlike task-specific AI models focusing on single problems, META-SiM operates as a foundation model trained on many different experiments and massive data amounts, allowing wide-ranging analyses.
Co-author Alexander Johnson-Buck, a research scientist, explained the challenge: “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. 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.”
Senior author Nils Walter, co-director of the Centre for RNA Biomedicine, said: “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. We could also track, say, the movement of wildebeests across Kenya and Tanzania, or even potentially celestial bodies moving across the universe.”
The research could help address genetic diseases. 60 per cent of human genetic diseases occur from malfunctions when genetic information is spliced together. META-SiM could find sporadic instances where mis-splicing occurs and suggest therapies.
Walter noted the speed improvement: “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.”
Jieming Li and Leyou Zhang, former graduate student researchers, led the work. The National Institutes of Health supported the study, published in Nature Methods.
