A simple model of a sequence-reading diffusion: non-self-averaging and self-averaging properties

Abstract Motivated by a question about the sensitivity of knots’ diffusive motion to the actual sequence of nucleotides placed on a given DNA, here we study a simple model of a sequence-reading diffusion on a stretched chain with a frozen sequence of ‘letters’ A and B , having different interaction energies. The chain contains a single distortion—a hernia—which brings two consecutive letters at its bottom together such that they interact. Due to interactions with the solvent, the hernia performs a random hopping motion along the chain with the transition rates dependent on its actual position. Our two focal questions are (a) the dependence of various transport properties on the letters’ interaction energy and (b) whether these properties are self-averaging with respect to different realizations of sequences. We show that the current through a finite interval, the resistance of this interval and the splitting probabilities on this interval lack self-averaging. On the contrary, the mean first-passage time through a finite interval with N sites and the diffusion coefficient in a periodic chain are self-averaging in the limit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo accent="false" stretchy="false">→</mml:mo> <mml:mi mathvariant="normal">∞</mml:mi> </mml:mrow> </mml:math> . Concurrently, two latter properties exhibit strong sample-to-sample fluctuations for finite N , as evidenced by numerical simulations.

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Publication Details

Journal
New Journal of Physics
Published
2025-04-01
DOI
https://doi.org/10.1088/1367-2630/adcd94
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

A simple model of a sequence-reading diffusion: non-self-averaging and self-averaging properties

Gleb Oshanin, Luca Peliti, Enzo Marinari, Silvio Kalaj
New Journal of Physics
Neural Networks and Applications
article

A simple model of a sequence-reading diffusion: non-self-averaging and self-averaging properties

Gleb Oshanin, Luca Peliti, Enzo Marinari, Silvio Kalaj
article en

Abstract

Abstract Motivated by a question about the sensitivity of knots’ diffusive motion to the actual sequence of nucleotides placed on a given DNA, here we study a simple model of a sequence-reading diffusion on a stretched chain with a frozen sequence of ‘letters’ A and B , having different interaction energies. The chain contains a single distortion—a hernia—which brings two consecutive letters at its bottom together such that they interact. Due to interactions with the solvent, the hernia performs a random hopping motion along the chain with the transition rates dependent on its actual position. Our two focal questions are (a) the dependence of various transport properties on the letters’ interaction energy and (b) whether these properties are self-averaging with respect to different realizations of sequences. We show that the current through a finite interval, the resistance of this interval and the splitting probabilities on this interval lack self-averaging. On the contrary, the mean first-passage time through a finite interval with N sites and the diffusion coefficient in a periodic chain are self-averaging in the limit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo accent="false" stretchy="false">→</mml:mo> <mml:mi mathvariant="normal">∞</mml:mi> </mml:mrow> </mml:math> . Concurrently, two latter properties exhibit strong sample-to-sample fluctuations for finite N , as evidenced by numerical simulations.

New Journal of PhysicsVol. 27(4)
Centre National de la Recherche Scientifique (FR), Asia Pacific Center for Theoretical Physics (KR), Sorbonne Université (FR), Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche (IT), Laboratoire de Physique Théorique de la Matière Condensée (FR), Istituto Nazionale di Fisica Nucleare, Sezione di Roma I (IT), Sapienza University of Rome (IT)
Openalex Percentile: Top 97%
Neural Networks and Applications
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