Study shows bacteria 'could learn' how to predict the future

New research from Washington University in St. Louis suggests that bacteria could learn from the past to predict the future. Using computer simulations and a simple theoretical model, Mikhail Tikhonov and co-authors published a paper in eLife that shows how bacteria could adapt to a fluctuating environment by learning its statistical regularities -- for example, which nutrients tend to be correlated -- and do so faster than evolutionary trial-and-error would normally allow.

"Evolutionary 'learning' is commonplace. For example, many organisms have evolved a circadian clock to follow the 24-hour day and night cycle," said Tikhonov, assistant professor of physics in Arts & Sciences. "But evolution takes place over many generations. We show that bacteria could, in principle, do what we do: Learn correlations from recent experience and adapt their future behaviour accordingly, even within their lifetime," he added.

"Bacteria do not have brains, but we find that this kind of information processing can be achieved with a circuit that is not only simple but similar to the circuits that bacteria are already known to have," stated Tikhonov.

Bacteria need three essentials to predict future

However, there are three essential elements that must be present. For this type of learning to occur, bacteria must have more regulators than they appear to need, the regulators themselves must be self-activated, and the bacteria must operate under real-world "nonlinear" situations, which are often approximated in models.

This new perspective will help Tikhonov shed light on the ways that traditional biological theories limit the kinds of questions that researchers can ask about their findings. Some bacteria may use redundant regulators or means of modulating their distinct pathways, as suggested by a recent paper.

"The 'wasteful' redundancy and unnecessary regulators that we see in many laboratory experiments can be puzzling, but scientists usually study organisms in simplified settings or static environments. The benefits we describe would only manifest themselves in the more realistic, fluctuating conditions. Our hope is that an experimentalist might read our paper and recognize the key ingredients we describe in the system that they study," Tikhonov said. 

(With inputs from ANI)

Picture Credit: Unsplash/RepresentativeImage