Efficient Brownian dynamics simulation of DNA molecules with hydrodynamic interactions in linear flows

Area of Science:

• Computational Biology
• Biophysics
• Fluid Dynamics

Background:

• Coarse-grained molecular dynamics (MD) and Brownian dynamics (BD) simulations are crucial for studying biological systems with hydrodynamic interactions (HIs).
• Simulating the motion of long molecules like DNA in fluid flows presents computational challenges, particularly for long-time dynamics.

Purpose of the Study:

• To develop an efficient and stable algorithm for simulating the motion of single DNA molecules in linear flows.
• To enable larger time step sizes in BD simulations, facilitating long-time and large-scale studies.

Main Methods:

• An efficient algorithm combining an integrating factor for linear flow effects and the Metropolis method (MM) for enhanced BD simulation.
• Numerical simulations of λ-DNA motion in linear flows were performed.

Main Results:

• The proposed algorithm achieves significantly larger time step sizes compared to previous methods while maintaining simulation stability.
• Numerical results for λ-DNA show excellent agreement with experimental data and prior simulation studies.
• The method is parallelizable, scalable, and stable for large time steps, making long-time, large-scale BD simulations feasible.

Conclusions:

• The developed algorithm offers a more efficient and practical approach for long-time, large-scale BD simulations of complex biological systems.
• This method is applicable to studying membranes, long-chain molecules, and collections of molecules in fluids.
• The enhanced efficiency and scalability open new avenues for computational biophysics and molecular dynamics research.