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Unraveling Dynamic Transitions in Time-Resolved Biomolecular Motions by A Dressed Diffusion Model.

Kaicheng ZhuHaibin Su
Published in: The journal of physical chemistry. A (2019)
Recent experimental data reveal the complexity of diffusion dynamics beyond the scope of classical Brownian dynamics. The particles exhibit diverse diffusive motions from the anomalous toward classical diffusion over a wide range of temporal scales. Here a dressed diffusion model is developed to account for non-Brownian phenomena. By coupling the particle dynamics with a local field, the dressed diffusion model generalizes the Langevin equation through coupled damping kernels and generates the salient feature of time-dependent diffusion dynamics reported in the experimental measurements of biomolecules. The dressed diffusion model provides one quantitative aspect for future endeavors in this rapid-growing field.
Keyphrases
  • machine learning
  • gene expression
  • high resolution
  • mass spectrometry
  • dna methylation
  • artificial intelligence
  • sensitive detection