Biophysicists have taken another small step forward in the quest for an automated method to infer models describing a system’s dynamics – a so-called robot scientist. Nature Communications published the finding – a practical algorithm for inferring laws of nature from time-series data of dynamical systems.
“Our algorithm is a small step,” says Ilya Nemenman, lead author of the study and a professor of physics and biology at Emory University. “It could be described as a toy version of a robot scientist, but even so it may have practical applications. For the first time, we’ve taught a computer how to efficiently search for the laws that underlie arbitrary, natural dynamical systems, including complex, non-linear biological systems.”
Nemenman’s co-author on the paper is Bryan Daniels, a biophysicist at the University of Wisconsin.
Everything that is changing around us and within us – from the relatively simple motion of celestial bodies, to weather and complex biological processes – is a dynamical system. A large part of science is guessing the laws of nature that underlie such systems, summarizing them in mathematical equations that can be used to make predictions, and then testing those equations and predictions through experiments.
“The long-term dream is to harness large-scale computing to make the guesses for us and speed up the process of discovery,” Nemenman says.