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In 2017, Roger Guimerà and Marta Sales-Pardo discovered a cause of cell division, the process driving the growth of living beings. But they couldn’t immediately reveal how they learned the answer. The researchers hadn’t spotted the crucial pattern in their data themselves. Rather, an unpublished invention of theirs — a digital assistant they called the “machine scientist” — had handed it to them. When writing up the result, Guimerà recalls thinking, “We can’t just say we fed it to an algorithm and this is the answer. No reviewer is going to accept that.”

The duo, who are partners in life as well as research, had teamed up with the biophysicist Xavier Trepat of the Institute for Bioengineering of Catalonia, a former classmate, to identify which factors might trigger cell division. Many biologists believed that division ensues when a cell simply exceeds a certain size, but Trepat suspected there was more to the story. His group specialized in deciphering the nanoscale imprints that herds of cells leave on a soft surface as they jostle for position. Trepat’s team had amassed an exhaustive data set chronicling shapes, forces, and a dozen other cellular characteristics. But testing all the ways these attributes might influence cell division would have taken a lifetime.

Instead, they collaborated with Guimerà and Sales-Pardo to feed the data to the machine scientist. Within minutes it returned a concise equation that predicted when a cell would divide 10 times more accurately than an equation that used only a cell’s size or any other single characteristic. What matters, according to the machine scientist, is the size multiplied by how hard a cell is getting squeezed by its neighbors — a quantity that has units of energy.

“It was able to pick up something that we were not,” said Trepat, who, along with Guimerà, is a member of ICREA, the Catalan Institution for Research and Advanced Studies.

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