Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2401.09952

Thermodynamic precision of a chain of motors: the difference between phase and noise correlation

Giulio Costantini·Andrea Puglisi
View original source ↗Read source document ↗Export metadata ↓

Abstract

Inspired by recent experiments on fluctuations of the flagellar beating in sperms and C. reinhardtii, we investigate the precision of phase fluctuations in a system of nearest-neighbour-coupled molecular motors. We model the system as a Kuramoto chain of oscillators with coupling constant $k$ and noisy driving. The precision $p$ is a Fano-factor-like observable which obeys the Thermodynamic Uncertainty Relation (TUR), that is an upper bound related to dissipation. We first consider independent motor noises with diffusivity $D$: in this case the precision goes as $k/D$, coherently with the behavior of spatial order. The minimum observed precision is that of the uncoupled oscillator $p_{unc}$, the maximum observed one is $Np_{unc}$, saturating the TUR bound. Then we consider driving noises which are spatially correlated, as it may happen in the presence of some direct coupling between adjacent motors. Such a spatial correlation in the noise does not reduce evidently the degree of spatial correlation in the chain, but sensibly reduces the maximum attainable precision $p$, coherently with experimental observations. The limiting behaviors of the precision, in the two opposite cases of negligible interaction and strong interaction, are well reproduced by the precision of the single chain site $p_{unc}$ and the precision of the center of mass of the chain $N_{eff} p_{unc}$ with $N_{eff}<N$: both do not depend on the degree of interaction in the chain, but $N_{eff}$ decreases with the correlation length of the motor noises.

Explore related subjects

cond-mat.stat-mechcond-mat.soft

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giulio Costantini, Andrea Puglisi. 2024-01-18. Thermodynamic precision of a chain of motors: the difference between phase and noise correlation. https://doi.org/10.1088/1742-5468%2Fad244b

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech↗
arXiv2026

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech↗
arXiv2026

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech↗