A Monte Carlo algorithm to study polymer translocation through nanopores. I. Theory and numerical approach

Area of Science:

• Polymer physics
• Nanotechnology
• Computational biophysics

Background:

• Polymer translocation through nanopores is crucial for DNA sequencing and drug delivery.
• Understanding the physical parameters governing this process is essential for optimizing applications.
• Existing simulation methods often lack efficiency or comprehensive parameter integration.

Purpose of the Study:

• To introduce a novel, efficient one-dimensional lattice Monte Carlo algorithm for simulating polymer translocation.
• To incorporate entropic forces, external driving forces, and frictional effects into a unified model.
• To accurately calculate polymer escape probability and translocation time distributions.

Main Methods:

• Developed a one-dimensional lattice Monte Carlo algorithm.
• Modeled the polymer as a Brownian particle with position-dependent forces.
• Integrated entropic bias, external forces, and frictional effects.
• Employed a numerical approach for exact calculation of translocation dynamics.

Main Results:

• The algorithm efficiently simulates polymer translocation dynamics.
• It accurately captures the influence of entropic, external, and frictional forces.
• The method allows for exact calculation of escape probability and mean translocation time.
• The study derives scaling laws for polymer translocation regimes.

Conclusions:

• The proposed Monte Carlo algorithm offers a significant advancement in simulating polymer translocation.
• This efficient and accurate model provides insights into fundamental translocation mechanisms.
• The derived scaling laws will be crucial for understanding and controlling polymer behavior in nanopores.