A sequential algorithm with a built in tension-propagation mechanism for modeling the chain-like bodies dynamics

TL;DR

This paper introduces a stochastic, sequential algorithm for modeling chain-like body dynamics based on tension propagation, validated through polymer translocation simulations and revealing new distributional insights.

Contribution

The paper presents a novel tension-propagation based stochastic algorithm for chain dynamics, improving memory efficiency and providing new distributional understanding of translocation times.

Findings

Translocation time follows a Moyal distribution.

The algorithm efficiently models chain movements with tension release.

Validation against literature data confirms accuracy.

Abstract

In the paper a novel stochastic algorithm designed to study of chain-like bodies dynamics is introduced. This algorithm models chain movements induced by the tension propagation and its main idea relies on the sequentialization of each movement into a sequence of virtual steps made by chain's segments. In this spirit, any accepted chain's new position is achieved by a move that is initiated by a shift of one segment picked randomly according to a problem-specific probability distribution and then followed by a cascade of some other segments' position rearrangements. The rearrangement process terminates when the tension in the chain induced by the initial shift is released. A considerable gain in the volume of allocated memory is achieved because the virtual steps lead to new conformations that are very likely to be acceptable by nature. We validate the algorithm by comparing passage times for polymer translocation through a pore obtained within this algorithm with their counterparts reported in the literature. In this paper we focus on a fluctuating-bond model of self-avoiding polymers on 2D square lattice. Based on the large data sets received in our simulations we have found that the transolaction time is distributed according to the Moyal probability distribution. This novel finding enables us to identify the theoretical form of various distributions of translocation time reported in literature by expressing them very accurately with the help of this two-parameter family of probability distributions