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

TL;DR

This paper applies an information geometric stochastic thermodynamic approach to E. coli sensory adaptation, revealing how noise influences adaptation efficiency and identifying optimal noise levels for thermodynamic efficiency.

Contribution

It introduces a novel application of information geometry and stochastic thermodynamics to quantitatively analyze E. coli adaptation, linking adaptation speed, thermodynamic cost, and noise.

Findings

Efficiency decreases with external noise level

Efficiency remains robust to stimulation strength

Optimal noise level enhances thermodynamic adaptation efficiency

Abstract

Biological systems process information under noisy environment. Sensory adaptation model of E. coli is suitable for investigation because of its simplicity. To understand the adaptation processing quantitatively, stochastic thermodynamic approach has been attempted. Information processing can be assumed as state transition of a system that consists of signal transduction molecules using thermodynamic approach, and efficiency can be measured as thermodynamic cost. Recently, using information geometry and stochastic thermodynamics, a relationship between speed of the transition and the thermodynamic cost has been investigated for a chemical reaction model. Here, we introduce this approach to sensory adaptation model of E. coli, and examined a relationship between adaptation speed and the thermodynamic cost, and efficiency of the adaptation speed. For increasing external noise level in stimulation, the efficiency decreased, but the efficiency was highly robust to external stimulation strength. Moreover, we demonstrated that there is the best noise to achieve the adaptation in the aspect of thermodynamic efficiency. Our quantification method provides a framework to understand the adaptation speed and the thermodynamic cost for various biological systems.