Stochastic modeling in nanoscale biophysics: Subdiffusion within proteins

By Samuel C. Kou. 2008

203 citations recorded by the source corpus.

Advances in nanotechnology have allowed scientists to study biological processes on an unprecedented nanoscale molecule-by-molecule basis, opening the door to addressing many important biological problems. A phenomenon observed in recent nanoscale single-molecule biophysics experiments is subdiffusion, which largely departs from the classical Brownian diffusion theory. In this paper, by incorporating fractional Gaussian noise into the generalized Langevin equation, we formulate a model to describe subdiffusion. We conduct a detailed analysis of the model, including (i) a spectral analysis of the stochastic integro-differential equations introduced in the model and (ii) a microscopic derivation of the model from a system of interacting particles. In addition to its analytical tractability and clear physical underpinning, the model is capable of explaining data collected in fluorescence studies on single protein molecules. Excellent agreement between the model prediction and the single-molecule experimental data is seen.

Subfields: Atomic and Molecular Physics, and Optics, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b1d6e3d6538d4b943d609dd2499478b5658cf279

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

  1. Paul C. Bressloff
  2. Jay M. Newby
  3. Bangti Jin

In Modeling and Simulation

  1. Raytcho D. Lazarov
  2. Zhi Zhou
  3. Paul C. Bressloff
  4. Jay M. Newby
  5. Bangti Jin
  6. William Rundell
  7. Zhidong Zhang

In Statistical and Nonlinear Physics

  1. Paul C. Bressloff
  2. Jay M. Newby
  3. Karl J. Friston
  4. Brent Doiron
  5. Robert Rosenbaum

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