This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: In both biological and engineered systems, cells and sensors must make sense of noisy, changing signals to function. However, collecting and using this information consumes valuable energy.
A new study, published in Physical Review Letters, describes a model for how a sensor or cell might maximize information collection from its surroundings while controlling the overall energy cost, resulting in an optimized system. Cells must monitor signals from their surrounding environment, such as molecules binding to the cell's receptors. Real biological systems rarely observe every step in signaling processes and must adapt in real time.
Previous studies also established that cellular sensing faces trade-offs among energy use, speed and accuracy because of limited range, biochemical noise and energetic constraints. Engineered sensors face similar problems. The study authors write, "In all such cases, the system has to tune the strength of its coupling to the signal, whether it is implemented via chemical or mechanical interactions, balancing the information it can extract with the energetic cost of probing.
A crucial feature to establish robust functioning is that these operations have to be performed in real time, based solely on data that can be directly observed." In a previous model, the team found that imperfect observers can show surprising information-gathering behavior. For example, an imperfect observer was capable of gathering more information than an ideal one or, in other cases, gathered no information at all. The researchers say this underscores the central role of energetics in shaping sensing strategies.
Previous work has shown that energy dissipation can sometimes be inferred from the timing and fluctuations of observable trajectories without reconstructing forces, which is inherently difficult. The team uses this idea in its new model with a strategy for balancing information gain with energy dissipation using only observable time-series data. In the model, a sensor can adjust how strongly it produces a readout molecule based on observed signal fluctuations from a hidden chemical network.
The abstract sensor could observe the signal and readout, but not the hidden network itself. It then estimated information based on how often the observed signal and readout states occurred together. The approach was able to determine near-optimal sensor settings, balancing information acquisition and energy dissipation, from finite observation windows, provided the observations were long enough.
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