Universal bounds on entropy production inferred from observed statistics

TL;DR

This paper introduces a new method to estimate a lower bound on entropy production in nonequilibrium systems using limited observed data, applicable to biological and molecular systems.

Contribution

It presents a hierarchical inference scheme for tight lower bounds on entropy production based on partial transition and waiting time statistics, including simplified network assumptions.

Findings

Hierarchical bounds improve EPR estimation accuracy.

Method applies to systems with hidden states and lumped observations.

Lower bounds are obtainable with simplified network models.

Abstract

Nonequilibrium processes break time-reversal symmetry and generate entropy. Living systems are driven out-of-equilibrium at the microscopic level of molecular motors that exploit chemical potential gradients to transduce free energy to mechanical work, while dissipating energy. The amount of energy dissipation, or the entropy production rate (EPR), sets thermodynamic constraints on cellular processes. Practically, calculating the total EPR in experimental systems is challenging due to the limited spatiotemporal resolution and the lack of complete information on every degree of freedom. Here, we propose a new inference approach for a tight lower bound on the total EPR given partial information, based on an optimization scheme that uses the observed transitions and waiting times statistics. We introduce hierarchical bounds relying on the first- and second-order transitions, and the moments of the observed waiting time distributions, and apply our approach to two generic systems of a hidden network and a molecular motor, with lumped states. Finally, we show that a lower bound on the total EPR can be obtained even when assuming a simpler network topology of the full system.