Mesoscopic non-equilibrium measures can reveal intrinsic features of the active driving

Federica Mura, Grzegorz Gradziuk, Chase P. Broedersz

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Biological assemblies such as chromosomes, membranes, and the cytoskeleton are driven out of equilibrium at the nanoscale by enzymatic activity and molecular motors. Similar non-equilibrium dynamics can be realized in synthetic systems, such as chemically fueled colloidal particles. Characterizing the stochastic non-equilibrium dynamics of such active soft assemblies still remains a challenge. Recently, new non-invasive approaches have been proposed to determine the non-equilibrium behavior, which are based on detecting broken detailed balance in the stochastic trajectories of several coordinates of the system. Inspired by the method of two-point microrheology, in which the equilibrium fluctuations of a pair of probe particles reveal the viscoelastic response of an equilibrium system, here, we investigate whether we can extend such an approach to non-equilibrium assemblies: can one extract information on the nature of the active driving in a system from the analysis of a two-point non-equilibrium measure? We address this question theoretically in the context of a class of elastic systems, driven out of equilibrium by a spatially heterogeneous stochastic internal driving. We consider several scenarios for the spatial features of the internal driving that may be relevant in biological and synthetic systems, and investigate how such features of the active noise may be reflected in the long-range scaling behavior of two-point non-equilibrium measures.
Original languageEnglish
Pages (from-to)8067-8076
Number of pages10
JournalSoft Matter
Volume15
Issue number40
DOIs
Publication statusPublished - 2019
Externally publishedYes

Funding

We thank F. Gnesotto, S. Ceolin, B. Remlein, and G. Torregrosa Cortes for many stimulating discussions. This work was supported by the German Excellence Initiative via the program NanoSystems Initiative Munich (NIM), and the Graduate School of Quantitative Biosciences Munich (QBM), and was funded by the Deutsche Forschungsgemeinshaft (DFG, German Research Foundation) – 418389167.

FundersFunder number
Deutsche Forschungsgemeinshaft
German Excellence Initiative
Graduate School of Quantitative Biosciences Munich
Nanosystems Initiative Munich
Deutsche Forschungsgemeinschaft418389167

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