The most efficient machines remember what has happened to them, and use that memory to predict what the future holds. That is the conclusion of a theoretical study1 by Susanne Still, a computer scientist at the University of Hawaii at Manoa and her colleagues, and it should apply equally to ‘machines’ ranging from molecular enzymes to computers. The finding could help to improve scientific models such as those used to study climate change.
“The idea that predictive capacity can be quantitatively connected to thermodynamic efficiency is particularly striking,” says Christopher Jarzynski, who studies statistical mechanics at the University of Maryland in College Park.
It might feel perfectly familiar for a computer simulation of weather, say, to construct a model of the environment and use it for prediction. But it seems peculiar to think of a biomolecule such as a motor protein doing the same thing.
Yet that is just what it does, say Still and her colleagues. A molecular motor works by undergoing changes in the conformation of the proteins that comprise it, and “the conformation it is in now is correlated with what states the environment passed through previously”, says Gavin Crooks, a biophysicist at the Lawrence Berkeley National Laboratory in Berkeley, California, and a co-author of the study, which was published last month in Physical Review Letters.
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