Bacteria Choose the Best Value for Money When Making Predictions
Predicting our environment can be a matter of life or death. Think, for example, of crossing a busy street. If you don't accurately predict where the cars will be when you're in the middle of the road, the consequences can be catastrophic. Remarkably, experiments have shown that even simple bacteria can make such predictions. For instance, they predict changes in the availability of nutrients. The better these bacteria are at predicting their environment, the higher their chances of survival. But just how good bacteria are at making these predictions, and what limits them in doing so, has remained an open question—until now.
Gathering Information from the Past to Predict the Future
To answer this question, researcher Age Tjalma studied how such organisms can best gather information from the past to predict the future. He then compared this with the strategies bacteria actually use.
In order to make a prediction, every system—whether human, bacterium, or robot—must rely on information from the past. First, this information needs to be stored. While humans use their brains for this, bacteria do so using proteins and energy in what are called biochemical networks. The amount of information that can be stored in these systems is limited, so the researchers expected that bacteria would store only the information that is most predictive of the future.
To Tjalma’s surprise, this turned out not to be the case. Further research revealed why: the most predictive information is also the most recent, but this information turns out to be the most expensive to store in terms of proteins and energy. To make the best possible prediction on a limited budget, bacteria instead gather a larger amount of less predictive but cheaper information. This information offers the best value for money.
Developing Innovative Medications
Tjalma's research provides deeper insight into how cells perceive their environment—a crucial step in understanding their eventual behavior. In the long run, this could contribute to the development of innovative medications that specifically target the communication systems of cells. At the same time, a better understanding of making predictions with limited resources could also aid in designing efficient autonomous systems, such as robots with minimal energy consumption.
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