Mean Squared Displacement

Finding the MSD was not a trivial task.

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MEAN SQUARED DISPLACEMENT

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Brownian Motion Mean Squared Displacement

• The goal of this calculation is to relate the simulated particle diffusion to real world values, namely velocity.
• Particle velocity will be a function of MSD x units ²⁄s which scales on space (units) and time (s) parameters.
• Space and time in the model are defined arbitrarily as Step_Size and Step where each Step a particle moves a distance randomly chosen from a normal distribution (µ=1,σ=.2)
• a step size of 1 unit/step will produce a brownian motion MSD of ~0.52 ±0.2 units ²/s
• empirical observations show that reasonable values for MSD are:
  • PSD 0.01 µm ²/s
  • synaptic 0.05 µm ²/s
  • extrasynaptic 0.1 µm ²/s

• given an MSD of 0.52 ±0.2 units ²/s at the current parameters: 1 step = 1 unit (at µ=1,σ=.2), the model will need to be scaled such that particles move at an extrasynaptic rate of 0.1 µm ²/s.
• spines are on average 1 to 10 µm apart, if the model is comparing two spines 1 µm apart, they should be separated by 5 units of model space. This is because the current particle diffusion rate of the model is .5 µm ²/s and the empirical MSD is .1 µm ²/s

Michalet • 2010 • Phys Rev E Stat Nonlin Soft Matter Phys - PDF

Mean Square Displacement Analysis of Single-Particle Trajectories with Localization Error: Brownian Motion in Isotropic Medium

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We examine the capability of mean square displacement analysis to extract reliable values of the diffusion coefficient D of single particle undergoing Brownian motion in an isotropic medium in the presence of localization uncertainty. The theoretical results, supported by simulations, show that a simple unweighted least square fit of the MSD curve can provide the best estimate of D provided an optimal number of MSD points is used for the fit. We discuss the practical implications of these results for data analysis in single-particle tracking experiments.