Brownian motion describes the stochastic diffusion of particles as they travel through n-dimensional spaces filled with other particles and physical barriers. Here the term particle is a generic term that can be generalized to describe the motion of molecule (e.g. H₂O) or proteins (e.g. NMDA receptors); note however that stochastic diffusion can also apply to things like the price index of a stock (see random walk) or the propagation heat energy across a surface. Brownian motion is among the simplest continuous-time stochastic processes, and a limit of various probabilistic processes (see random walk). As such, Brownian motion is highly generalizable to many applications, and is directly related to the universality of the normal distribution. In some sense, stochastic diffusion is a pure actuation of the basic statistical properties of probability distributions - it is distribution sampling translated into movement.

SPECIFICATION - Physical experiments suggest that Brownian motion has

continuous increments
increments of a particle over disjoint time intervals are independent events
each increment is assumed to result from collisions with many molecules
each increment is assumed to have a normal probability distribution
- (Central Limit Theorem of probability theory: the sum of a large number of independent identically distributed random variables is approximately normal)
the mean increment is zero as there is no preferred direction
the position of a particle spreads out with time
the variance of the increment is proportional to the length of time that Brownian motion has been observed

The probability density of a normally distributed random variable with mean μ and standard deviation σ is given by:

<math>

f(x, \mu, \sigma) = \frac{1}{\sigma \sqrt{2\pi} } e^{ - \frac{1}{2} (\frac{x-\mu}{\sigma})^2 } </math>

Mathematically, the random process called Brownian motion is denoted here as B(t) and defined for times t ≥ 0; the probability density of Brownian particles at the end of time period [0, t] is obtained by substituting μ = 0 and σ = √t, giving:

<math>

\frac{1}{\sqrt{t2\pi} } e^{ - \frac{1}{2} (\frac{x}{\sqrt{t}})^2 } </math>

where x denotes the value of random variable B(t). The probability distribution of the increment B(t + u) − B(t) is:

<math>

P[B(t+u) - B(t) \leq a] = \int_{x=-\infty}^{a} \frac{1}{\sqrt{u2\pi} } e^{ - \frac{1}{2} (\frac{x}{\sqrt{u}})^2 } dx </math>

Common Diffusion Terms & Parameters

I've found it most helpful to learn about the terms and parameters used to characterize stochastic diffusion as they apply to diffusion phenomena along a flat 2D surface. These parameters include:

D : diffusion rate coefficient

<math>

D = {L^{2} \over 2d \cdot t} </math>

L : step length

d : dimensions

t : time

D (in units: µm²/s) is the mean diffusion rate per unit time (velocity), often in µm²/s for biological motion on a molecular level (n.b. µm²/s are the units for particle diffusion on a surface/membrane; the surface can actually be curved or ruffled such that the surface fills a 3D space, however the diffusion equations for those instances are slightly more complex. Units for 3D space are naturally: µm³/s). This refers to how fast, on average, the particle moves along its surface trajectories. This value is often of ultimate interest, that is, the goal of single-particle tracking studies is often to define the average diffusion rate of a molecule and report it as a Diffusion Coefficient; once D is known, this Diffusion Coefficient can then be easily implemented in computer simulations of Brownian motion. However under most circumstances, the diffusion rate cannot be observed directly in empirical experiments - this would require the ability to visualize all the microscopic particles and collisions that dictate the particle's movement on a nanosecond timescale. In the animation on the right, the diffusion rate can actually be quantified directly; but what is often seen when observing particle diffusion through a microscope would more closely resemble this:

 <source src="http://www.bradleymonk.com/w/images/e/e9/Stochastic_Diffusion.mp4" type="video/mp4">

</video> </html>

Instead, D is often calculated from the mean squared diffusion (MSD) path of the particle, defined below.

MSD : mean squared displacement

<math>

MSD = { 2d \cdot D} </math>

d : dimensions

D : diffusion coefficient

MSD (in units: µm²) is the mean square displacement of a particle over some time period. If D (the diffusion coefficient) is unknown, MSD can be computed using the following equation:

Brownian Motion

SPECIFICATION - Physical experiments suggest that Brownian motion has

Common Diffusion Terms & Parameters

D : diffusion rate coefficient

MSD : mean squared displacement

K : standard deviation of the normal distribution for D

L : mean step length

λ : mean 1D step length component of L

ε : step length scalar coefficient

Brownian Motion Matlab Code

Stochastic Diffusion

Introduction

Simulating Single Particle Trajectories

Open Source 'plasticity' Project

One Dimensional Brownian Motion

Distribution of Displacements

Convert displacements to position

Two dimensional particle simulation

Compute the Displacement Squared

Theoretical Value of D

A more realistic particle -- Getting the Units Right

Displacement Squared Plot

Estimating D from the Simulated Data

Uncertainty in the Estimate

Systematic Error -- Bulk Flow in the Solvent

Displacement Squared in the Presence of Bulk Flow

Simulating Multiple Particles

Using a For Loop to Generate Multiple Data Sets

SimulateParticle Function

Look at the Results

Displacement Squared

Estimated Value of D

Fancy Statistics and Plots

A More Perfect Distribution

Sampling Uncertainty

What Happens when you Square a Random Variable?

100 Years of BIO Lab Data in 1 Second

Planning Your Time in the Lab

Auto Correlation - A Closer Look at the Data

Cross Correlation

Matlab Tricks

A Correlated Trajectory

notes & articles

See Also

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