Quantifying dissipation using fluctuating currents

TL;DR

This paper explores methods to quantify entropy production in nonequilibrium systems, using probability currents and thermodynamic uncertainty relations to measure dissipation from time-series data.

Contribution

It introduces three strategies to quantify entropy production, including bounds from statistical fluctuations, improving analysis of nonequilibrium biological systems.

Findings

Probability currents can be used to estimate entropy production.

Thermodynamic uncertainty relations provide bounds on dissipation.

High-dimensional data pose challenges, mitigated by fluctuation-based bounds.

Abstract

Systems coupled to multiple thermodynamic reservoirs can exhibit nonequilibrium dynamics, breaking detailed balance to generate currents. To power these currents, the entropy of the reservoirs increases. The rate of entropy production, or dissipation, is a measure of the statistical irreversibility of the nonequilibrium process. By measuring this irreversibility in several biological systems, recent experiments have detected that particular systems are not in equilibrium. Here we discuss three strategies to replace binary classification (equilibrium versus nonequilibrium) with a quantification of the entropy production rate. To illustrate, we generate time-series data for the evolution of an analytically tractable bead-spring model. Probability currents can be inferred and utilized to indirectly quantify the entropy production rate, but this approach requires prohibitive amounts of data in high-dimensional systems. This curse of dimensionality can be partially mitigated by using the thermodynamic uncertainty relation to bound the entropy production rate using statistical fluctuations in the probability currents.