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Related Experiment Video

Updated: Jun 8, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Statistically consistent coarse-grained simulations for critical phenomena in complex networks.

Hanshuang Chen¹, Zhonghuai Hou, Houwen Xin

¹Department of Chemical Physics, Hefei National Laboratory for Physical Sciences at Microscales, University of Science and Technology of China, Hefei, Anhui 230026, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 28, 2010

Summary

We developed a novel coarse-graining method for complex networks that speeds up dynamic evaluations while maintaining accuracy for both equilibrium and nonequilibrium systems. This approach accurately models phase transitions and fluctuations.

Related Experiment Videos

Last Updated: Jun 8, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

Complex networks analysis
Statistical physics modeling
Computational dynamics

Background:

Evaluating dynamics on complex networks is computationally intensive.
Existing coarse-graining methods may not preserve critical statistical and dynamical properties.

Purpose of the Study:

To introduce a degree-based coarse-graining approach for complex networks.
To ensure the method satisfies consistency conditions for equilibrium and nonequilibrium dynamics.
To accelerate the evaluation of dynamics on complex networks.

Main Methods:

Developed a degree-based coarse-graining technique.
Formulated and verified consistency conditions for statistical distributions and dynamical flows.
Applied the method to the Ising model and susceptible-infected-susceptible epidemic model under annealed network approximation.

Main Results:

The proposed coarse-graining method accelerates dynamic evaluations.
The method satisfies equilibrium statistical distribution and nonequilibrium dynamical flow consistency conditions.
Numerical simulations show good agreement in phase transitions and fluctuations between coarse-grained and original networks.

Conclusions:

The degree-based coarse-graining approach provides an efficient and accurate way to study dynamics on complex networks.
This method preserves essential properties of both equilibrium and nonequilibrium systems.
It offers a reliable tool for analyzing complex systems like epidemic spread and magnetic models.