Actionable Forecasting as a Determinant of Biological Adaptation

TL;DR

Biological systems can adapt precisely by tracking a target that combines current conditions and their rate of change, using predictive mechanisms when sensing is unreliable.

Contribution

A new mathematical framework using dynamics-informed neural networks to model biological adaptation.

Findings

Biological systems can precisely track their optimal state using a non-anticipatory actionable target.

Predictive mechanisms like circadian rhythms help infer actionable targets when sensing is slow or unreliable.

Dynamics-informed neural networks efficiently capture biological adaptation in noisy environments.

Abstract

Organisms continuously adapt to changing environments to survive. Here, contrary to the prevailing view that predictive strategies are essential for perfect adaptation, it is shown that biological systems can precisely track their optimal state by adapting to a non‐anticipatory actionable target that integrates the current optimum with its rate of change. Predictive mechanisms, such as circadian rhythms, are beneficial for accurately inferring the actionable target when environmental sensing is slow or unreliable. A new mathematical framework is developed, showing that dynamics‐informed neural networks embodying these principles can efficiently capture biological adaptation even in noisy environments. These results provide fundamental insights into the interplay between forecasting, control, and inference in biological systems, redefining adaptation strategies and guiding the design of advanced adaptive biomolecular circuits.

A new framework reveals how biological systems can achieve precise adaptation by tracking an actionable target that combines the current optimal state with its rate of change. This approach, implemented through dynamics‐informed neural networks, demonstrates that predictive mechanisms like circadian rhythms become beneficial when environmental sensing is unreliable, providing fundamental insights into biological adaptation strategies.

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