Stochastic thermodynamic limit on E. coli adaptation by information geometric approach

Area of Science:

• Biophysics
• Systems Biology
• Statistical Mechanics

Background:

• Biological systems operate in noisy environments, necessitating robust information processing mechanisms.
• Sensory adaptation, exemplified by the bacterium *Escherichia coli* (*E. coli*), is a fundamental process for survival and response.
• Stochastic thermodynamics offers a quantitative framework to analyze information processing in biological systems.

Purpose of the Study:

• To investigate the relationship between adaptation speed and thermodynamic cost in the *E. coli* sensory adaptation model.
• To explore the efficiency of adaptation speed under varying noise levels and stimulation strengths.
• To establish a quantitative framework for understanding adaptation dynamics in biological systems.

Main Methods:

• Application of information geometry and stochastic thermodynamics to the *E. coli* sensory adaptation model.
• Modeling information processing as state transitions of signal transduction molecules.
• Quantification of adaptation efficiency and thermodynamic cost.

Main Results:

• Adaptation efficiency decreases with increasing external noise levels.
• The system exhibits high robustness in adaptation efficiency against variations in external stimulation strength.
• A specific noise level was identified as optimal for achieving the highest thermodynamic efficiency in adaptation.

Conclusions:

• The study provides a quantitative method to analyze adaptation speed and thermodynamic cost in biological systems.
• Noise plays a critical role in the efficiency of biological information processing, with an optimal level existing for adaptation.
• The findings offer a generalized framework applicable to diverse biological adaptation mechanisms.