Actionable Forecasting as a Determinant of Biological Adaptation.

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Organisms must adapt to environmental changes for survival.
• Current models often emphasize predictive strategies for adaptation.
• The role of non-anticipatory mechanisms in precise biological tracking is less understood.

Purpose of the Study:

• To investigate alternative adaptation strategies beyond pure prediction.
• To demonstrate how biological systems can precisely track optimal states.
• To develop a framework for understanding adaptation in dynamic and noisy environments.

Main Methods:

• Developed a new mathematical framework for biological adaptation.
• Utilized dynamics-informed neural networks.
• Analyzed the integration of current optimum and its rate of change.

Main Results:

• Biological systems can track optimal states using non-anticipatory actionable targets.
• Predictive mechanisms enhance adaptation when environmental sensing is slow or unreliable.
• Dynamics-informed neural networks efficiently capture adaptation in noisy conditions.

Conclusions:

• Biological adaptation can be achieved through non-anticipatory control integrating current state and change rate.
• Predictive strategies are beneficial but not always essential for precise adaptation.
• Findings redefine adaptation strategies and inform the design of adaptive biomolecular circuits.