View source for Mean Squared Displacement


{{Article|Czondor Choquet|2012|PNAS - [http://www.ncbi.nlm.nih.gov/pubmed/22331885 PDF]|22331885|Unified quantitative model of AMPA receptor trafficking at synapses}}
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{{Fig|[[File:UnifiedFig1.png|500px]]|(A) Schematic diagramof themodel. Kinetic parameters include Dout (extrasynaptic diffusion), Din (synaptic diffusion), DPSD (diffusion coefficient of the PSD), kon (AMPAR/ scaffold binding rate), koff (AMPAR/scaffold dissociation rate), and kendo (endocytosis rate). (B) Simulated trajectory (50 s). The synapse is in green, the PSD in red, and dendrite borders in gray. Geometric parameters are: a (synapse spacing), b (synapse width), c (PSD width), w (dendrite width). (C) High-magnification 30-s trajectory of an AMPAR-bound Qdot on the surface of a DIV 9 neuron. and simulated (plain curves) AMPAR median diffusion coefficients obtained at different neuronal ages (DIV 4–15) or varying synaptic spacing (0.75–30 μm), respectively, were plotted against synapse density. The simulated curves were computed for different kon values, keeping koff: 0.1 s−1. (F) Interquartile distributions of AMPAR diffusion coefficients from experiments (black) and simulations (green). Neuroligin-1 expression, which doubled the number of Homer1c-positive puncta, was mimicked by a decrease in synapse spacing (a: 1 μm). Overexpressing PSD-95 was modeled by enhancing kon (2.5 s−1) to mimic an increase in PSD binding sites, plus an increase in PSD size (c: 0.4 μm).}}

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Finding the MSD of the simulation is ([http://en.wikipedia.org/wiki/Mean_squared_displacement a trivial task]) using a Matlab toolbox (see video below). The target extrasynaptic MSD was '''0.1 <sup>µm&sup2;</sup>&frasl;<sub>s</sub>''' (from Choquet {{Fig|[[File:ChoquetDiffusionRate1.png]]}}). Scaling the model PSD size and distance between synapses to real-world values based on the target MSD is not a trivial task, and may actually be related only indirectly. Just because a particle moves quickly, does not mean it moves from A to B quickly - it depends on how much stuff there is to collide with. Linear estimates have shown that glycine receptor movements along dendrites at a speed of 1–2 µm/min (.016-.008 µm/s) {{Fig|[[File:Choquet MSD2.png]]}}

First, the randomly generated step-size was scaled to produce an MSD of '''0.1 <sup>units&sup2;</sup>&frasl;<sub>step</sub>''' using the Matlab Brownian motion toolbox. Second, the dimensions of the model need to be scaled. It was found that an XY random step-size of µ{{=}}0.4 (&sigma;{{=}}.2) units produced an MSE &asymp; '''0.1 <sup>units&sup2;</sup>&frasl;<sub>step</sub>'''. Next, the arbitrary 0.4 units need to be given meaning...

Given movements along dendrites at a speed of 1–2 µm/min (.016-.008 µm/s) observation, we can use a derivation of the MSD equation to help scale the model. The root-mean-square distance after N unit steps, with a step length of (L) is: 

* d {{=}} L*sqrt(N)

In order to travel a distance d, N steps are required by this equation

* N {{=}} (d/L)²
* N {{=}} (1000 nm /400 nm)² {{=}} 6.25
* N {{=}} (2000 nm /400 nm)² {{=}} 25
* N {{=}} (4000 nm /400 nm)² {{=}} 100

* Lets say a particle is traveling linearly at a rate of 1 µm / 100 s
* And takes between 6-25 steps to cover 1-2 µm.  




[[File:ScaleModel.png]]


here are several good resources for explaining and calculating MSD:
*[http://mathworld.wolfram.com/RandomWalk2-Dimensional.html wolfram alpha]
*[http://www.compsoc.man.ac.uk/~lucky/Democritus/Theory/msd.html Democritus]
*[http://www.mathworks.com/matlabcentral/fileexchange/40692-mean-square-displacement-analysis-of-particles-trajectories Matlab toolbox]

{{ExpandBox|VIDEO|
<HTML><embed src="http://bradleymonk.com/media/MSD.mp4" height="500" width="640" autoplay="false"></HTML>
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{{ExpandBox|MEAN SQUARED DISPLACEMENT|

;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 {{Button|''x'' units&thinsp;&sup2;&frasl;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,&sigma;{{=}}.2)
*a step size of {{Button|1 unit/step}} will produce a brownian motion MSD of {{Button|~0.52 ±0.2 units&thinsp;&sup2;/s}}
*empirical observations show that reasonable values for MSD are: 
** PSD 0.01 µm&thinsp;&sup2;/s 
** synaptic 0.05 µm&thinsp;&sup2;/s 
** extrasynaptic 0.1 µm&thinsp;&sup2;/s

*given an MSD of {{Button|0.52 ±0.2 units&thinsp;&sup2;/s}} at the current parameters: 1 step {{=}} 1 unit (at µ{{=}}1,&sigma;{{=}}.2), the model will need to be scaled such that particles move at an extrasynaptic rate of 0.1 µm&thinsp;&sup2;/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&thinsp;&sup2;/s and the empirical MSD is .1 µm&thinsp;&sup2;/s


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to make '''0.1 <sup>units&sup2;</sup>&frasl;<sub>step</sub>''' &asymp; '''0.1 <sup>µm&sup2;</sup>&frasl;<sub>s</sub>'''. It was found that an XY random step-size of µ{{=}}0.4 (&sigma;{{=}}.2) units produced an MSE &asymp; '''0.1 <sup>units&sup2;</sup>&frasl;<sub>step</sub>'''. Then, the arbitrary 0.5 units were given meaning (converted to 0.5 µm) by scaling the model according to real-world values (see below) by making 1 unit {{=}} 1 µm; as a convention, a ''subunit'' will be 1/10th of a unit, thus 1 subunit {{=}} 0.1 µm). The PSD areas were set to 3-subunits (.3 µm) square, 20 subunits (2 µm) apart, within a rectangular field 20 subunits (2 µm) wide and 60 subunits (6 µm) long. Given these scaled dimensions where 10 subunits &asymp; 1 µm, a particle with an XY step-size of 0.5 units moving in a straight line, could theoretically go from PSD1 to PSD2 in 4 steps (obviously given the simulated particles are moving with Brownian motion, this lower-bound would be extremely rare). 

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{{Article|Michalet|2010|Phys Rev E Stat Nonlin Soft Matter Phys - [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3055791/ PDF]|PMC3055791|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.

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