Non-equilibrium scaling behaviour in driven soft biological assemblies

TL;DR

This study investigates how non-equilibrium activity in soft biological networks can be quantified through probe measurements, revealing power-law scaling of cycling frequencies and their relation to entropy production.

Contribution

It introduces a stochastic model linking enzymatic activity to non-equilibrium dynamics and demonstrates how probe-based measurements can reveal scaling laws and entropy production.

Findings

Cycling frequencies scale as a power law with probe distance.

Scaling behavior relates to entropy production rate.

Non-invasive measurements can infer internal activity levels.

Abstract

Measuring and quantifying non-equilibrium dynamics in active biological systems is a major challenge, because of their intrinsic stochastic nature and the limited number of variables accessible in any real experiment. We investigate what non-equilibrium information can be extracted from non-invasive measurements using a stochastic model of soft elastic networks with a heterogeneous distribution of activities, representing enzymatic force generation. In particular, we use this model to study how the non-equilibrium activity, detected by tracking two probes in the network, scales as a function of the distance between the probes. We quantify the non-equilibrium dynamics through the cycling frequencies, a simple measure of circulating currents in the phase space of the probes. We find that these cycling frequencies exhibit power-law scaling behavior with the distance between probes. In addition, we show that this scaling behavior governs the entropy production rate that can be recovered from the two traced probes. Our results provide insight in to how internal enzymatic driving generates non-equilibrium dynamics on different scales in soft biological assemblies.