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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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A Bayesian inference scheme to extract diffusivity and potential fields from confined single-molecule trajectories.

Silvan Türkcan1, Antigoni Alexandrou, Jean-Baptiste Masson

  • 1Laboratoire d'Optique et Biosciences, Ecole Polytechnique, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale U696, Palaiseau, France.

Biophysical Journal
|June 9, 2012
PubMed
Summary

This study introduces a Bayesian inference method to analyze single-molecule trajectories, revealing the confining potential of receptor motion. The new technique accurately extracts molecular dynamics information, outperforming traditional methods.

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Area of Science:

  • Biophysics
  • Statistical Mechanics
  • Computational Biology

Background:

  • Current single-molecule trajectory analysis methods utilize limited data.
  • Receptor motion is often restricted by confining potentials, particularly when targeted by toxins.
  • Understanding these potentials is crucial for molecular dynamics.

Purpose of the Study:

  • To develop and validate a Bayesian inference scheme for extracting 2D confining potentials from single-molecule trajectories.
  • To model receptor motion using the overdamped Langevin equation.
  • To compare the new method's performance against classical techniques like mean-square-displacement analysis.

Main Methods:

  • Bayesian inference applied to single-molecule trajectories of confined receptors.
  • Modeling molecular motion via the overdamped Langevin equation.
  • Inference performed on polynomial expansions of the potential and discretized diffusivities.

Main Results:

  • The Bayesian method efficiently extracts confining potentials and provides uncertainty estimates.
  • Numerical simulations confirm convergence of inferred values for forces, potential, and diffusivity.
  • The technique outperforms mean-square-displacement analysis, especially when forces are present.
  • Inferred potentials more accurately represent input potentials than those from Boltzmann statistics.

Conclusions:

  • Bayesian inference offers a more comprehensive analysis of single-molecule trajectory data.
  • This method accurately characterizes confining potentials and molecular dynamics.
  • It provides a superior alternative to traditional methods for analyzing complex molecular systems.