Balancing information and dissipation with partially observed fluctuating signals

TL;DR

This paper presents a chemical model for biological sensors that adaptively balance information acquisition and energy dissipation using limited observational data, applicable even with finite-time and resolution constraints.

Contribution

It introduces a novel strategy for sensors to optimize information gathering and dissipation balancing based on counting statistics, applicable under realistic biological constraints.

Findings

Strategies effectively estimate hidden signals from observed trajectories.

Sensors adapt production to balance information and dissipation.

Effective even with finite-time measurements and regulatory mechanisms.

Abstract

Biological systems sense and extract information from fluctuating signals while operating under energetic constraints and limited resolution. We introduce a general chemical model in which a sensor, coupled to a signaling pathway activated by hidden signals, can allosterically tune the production of a readout molecule. We propose viable strategies for the sensor to estimate, and eventually balance, information gathering on the hidden process and the associated dissipative cost relying solely on counting statistics of observed trajectories. We show that these strategies can be successfully implemented to adapt the readout production even with finite-time measurements and limited dynamic resolution, and remain effective in the presence of inhibitory regulatory mechanisms. Our study provides a plausible mechanism to actively balance information and dissipation, paving the way for an implementable design principle underpinning biological and biochemical adaptation.