Proteins remember the past to predict the future
October 5, 2012


Motor Proteins (credit: Ccl005/Wikimedia Commons)

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 study 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, Nature News reports. The finding could
help to improve scientific models such as those used to study climate

Information that provides clues about the future state of the
environment is useful, because it enables the machine to ‘prepare’ —
to adapt to future circumstances, and thus to work as efficiently as
possible. “My thinking is inspired by dance, and sports in general,
where if I want to move more efficiently then I need to predict well,”
says Still.

Alternatively, think of a vehicle fitted with a smart
driver-assistance system that uses sensors to anticipate its imminent
environment and react accordingly — for example, by recording whether
the terrain is wet or dry, and thus predicting how best to brake for
safety and fuel efficiency.

That sort of predictive function costs only a tiny amount of
processing energy compared with the total energy consumption of a car.

But for a biomolecule it can be very costly to store information, so
its memory needs to be highly selective. Environments are full of
random noise, and there is no gain in the machine ‘remembering’ all
the details. “Some information just isn’t useful for making
predictions,” says Crooks.

Because biochemical motors and pumps have indeed evolved to be
efficient, says Still, “they must therefore be doing something clever
— something tied to the cognitive ability we pride ourselves with: the
capacity to construct concise representations of the world we have
encountered, which allow us to say something about things yet to

This balance, and the search for concision, is precisely what
scientific models have to negotiate. If you are trying to devise a
computer model of a complex system, in principle there is no end to
the information that it might incorporate. But in doing that you risk
simply constructing a one-to-one map of the real world — not really a
model at all, just a mass of data, many of which might be irrelevant
to prediction.

Efficient models should achieve good predictive power without
remembering everything. “This is the same as saying that a model
should not be overly complicated — that is, Occam’s razor,” says
Still. She hopes that knowledge of this connection between energy
dissipation, prediction and memory might help researchers to improve
algorithms that minimize the complexity of their models.

Susanne Still, David A. Sivak, Anthony J. Bell, Gavin E. Crooks,
Thermodynamics of Prediction, Physical Review Letters, 2012, DOI:
Susanne Still, David A. Sivak, Anthony J. Bell, Gavin E. Crooks, The
thermodynamics of Prediction,

You received this message because you are subscribed to the Google Groups 
"Everything List" group.
To post to this group, send email to
To unsubscribe from this group, send email to
For more options, visit this group at

Reply via email to