Open access peer-reviewed chapter

Diffusion Theory for Cell Membrane Fluorescence Microscopy

Written By

Minchul Kang

Submitted: 18 November 2019 Reviewed: 20 February 2020 Published: 30 September 2020

DOI: 10.5772/intechopen.91845

From the Edited Volume

Fluorescence Methods for Investigation of Living Cells and Microorganisms

Edited by Natalia Grigoryeva

Chapter metrics overview

657 Chapter Downloads

View Full Metrics


Since the discovery of fluorescent proteins and the development of DNA recombinant techniques, various fluorescence methods have significantly improved our understanding of cell biology at a molecular level. In particular, thanks, in large part, to technological advances in these fields, fluorescence techniques such as fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), and single-particle tracking (SPT) have become standard tools in studying cell membrane structure as well as the diffusion and interaction of biomolecules in the cell membrane. In this chapter, we will review some topics of the diffusion theory from both deterministic and probabilistic approaches, which are relevant to cell membrane fluorescence microscopy. Additionally, we will derive some basic equations for FARP and FCS based on the diffusion theory.


  • diffusion theory
  • fluorescence recovery after photobleaching
  • fluorescence correlation spectroscopy
  • cell membranes

1. Introduction

Diffusion is an idealization of the random motion of one or more particles in space. Since diffusion is a dominant way for biological organisms to transport various molecules to desirable locations for cell signaling, the role of diffusion within biological systems is critical [1, 2, 3]. Therefore, to quantify the diffusion coefficient, a measure of diffusion rates, is essential to understand both the physiology and pathology of cells in terms of cell signaling time scales [1, 2, 3]. Moreover, the diffusion coefficients of proteins may also provide information on the landscape of the membrane environment where diffusion occurs [4, 5, 6]. However, quantifying the diffusion especially in live cell membranes is still challenging although a couple of tools are available including fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) [7, 8]. Diffusion is quantified by a diffusion coefficient, D, which characterizes the proportionality in a linear relationship between mean squared displacement (MSD, x2) of a Brownian particle and time [9, 10]. To determine the diffusion coefficients of biomolecules of interests, mathematical models for the diffusion process are compared with experimental data in FRAP and FCS analysis. In this chapter, we bridge the gap between experimental and theoretical aspects of FRAP and FCS by reviewing mathematical theories for FRAP and FCS.


2. Diffusion equation

2.1 Diffusion equation from the deterministic point of view

In 1855, Fick [11] published two cornerstone papers on diffusion, in which he proposed the fundamental laws describing the transport of mass due to the concentration gradient and an associated mathematical model. According to Fick’s first law, the diffusive flux (J) is proportional to the concentration gradient of diffusants (du/dx) with a proportionality constant called a diffusion coefficient, D. In one-dimensional spatial dimension (R1), Fick’s law can be represented as


where Jxt is the diffusion flux and uxt is the concentration of diffusants at the location x at time t. The diffusion coefficient can be calculated by the Stokes-Einstein equation [12, 13]:


where kB is Boltzmann’s constant, T is the absolute temperature, η is the dynamic viscosity, and r is the radius of the spherical particle. Assuming the conservation of mass in an infinitesimal interval xx+Δx, we obtain


where uxtΔx is the total number of molecules in the interval xx+Δx and JxtJx+Δxt is the difference of influx and efflux in and out of the interval (i.e., net change in the total number of molecules in the interval) as shown in Figure 1.

Figure 1.

The change in the number of molecules in an intestinal interval due to diffusion.

By combining Eqs. (1) and (3) and by taking the limit in Δx0, we have Fick’s second law that describes the diffusion process in a form of partial differential equation:


Eq. (4) is often referred to as the one-dimensional diffusion equation or heat equation. Similarly, two-dimensional (R2) and three-dimensional (R3) can be derived as


In a more compact form, the diffusion equations are written using the Laplace operator, Δ:


where Δu=2ux2 in R1, Δu=2ux2+2uy2 in R2, and Δu=2ux2+2uy2+2uz2 in R3.

Importantly, the diffusion equation satisfies the following important properties:

  1. Property 1: Translation invariance. If uxt is a solution of the heat equation, then for any fixed number x0, the function uxx0t is also a solution.

  2. Property 2: Derivatives of solutions. If uxt is a solution of the heat equation, then the partial derivatives of u also satisfy the heat equation.

  3. Property 3: Integrals and convolutions. If Φxt is a solution of the heat equation, then Φg (the convolution of S with g) is also a solution where Φgxt=Φxytgydy provided that this improper integral converges. The improper integral Φg is called the convolution of Φ and g.

  4. Property 4: Dilation. Suppose a>0 is a constant. If uxt is a solution of the heat equation, then the dilated function v(x,t)=uaxat is also a solution.

Based on these properties, we are now ready to solve the following initial value problem on xR1 for0t<:


where Hx is often referred to as the Heaviside function.

By Property 4, any solution (uxt) is unaffected by the dilation xax and tat for any aR1. Since xt is also unaffected by the dilations (xtaxat=xt), we look for a solution in the form of gαxt for some constant α. Notice also that gαxt is also invariant under these dilations: αaxat=gαxt. If we let p=αxt and choose α=14D, then by the chain rule, we have


which reduces to an ordinary differential equation g+2pg=0. This can be solved as.


for arbitrary constants C1 and C2. Because as t0+, p, for x>0


where we used a well-known identity (the error function integral):


On the other hand, since as t0, p, for x<0


which implies that C1=1π and C2=12. Putting together, we have a solution


Define Φxt=uxxt; then


By Property 2, derivatives of solutions, the function Φxt=14πκtex24κt is also a solution to the diffusion equation. Φxt is called the (one-dimensional) heat kernel or the fundamental solution of the heat equation. The graphs of the heat kernel for different t are shown in Figure 2.

Figure 2.

The heat kernel graphs for different t.

From Figure 2, we can see that the heat kernel Φxt has a “bell curve” graph of a normal distribution (Gaussian function) with2Dt as the standard deviation, which sometimes called the Gaussian root mean square width. Also, 14πt modulates the amplitude of the Gaussian curves, and the amplitude blows up to as t0+ and approaches 0 as t, i.e.:


Also, from the error function integration (Eq. (14))


Furthermore, it follows that (i) Φxyt satisfies the heat equation (Property 1: translation invariance) and (ii) Φϕxt=Φxytϕydy satisfies the heat equation (Property 3: integrals and convolutions).

From the definition (Φ=ux), by differentiating Eq. (7) with respect to x, we see that Φxt satisfies


Even though Hx is not differentiable due to discontinuity at x=0, we can redefine differentiation in a broad sense (weak derivative) and under this weak derivative definition:


where δx is called the Dirac delta function. The Dirac delta function satisfies a few important properties:

  1. limt0+Φxt=δx

  2. δxdx=1 and δxydx=1

  3. δxfxdx=f0 and δxyfxdx=fy

The third integration property is sometimes called the sifting property of the Dirac delta function. With these properties, we now can show (heuristically) uxt=Φϕxt satisfies the following diffusion equation:


To show Φϕxt satisfies the initial condition, we apply the sifting property of the Dirac delta function:


In other words, this result (Eq. (19)) indicates that for any initial value problem, the solution can easily be found as a convolution of the heat kernel and initial data.

2.2 Diffusion equation from the stochastic point of view

In many biological systems, passive transports are often described by Brownian motion or diffusion that is observed in random drifting of pollen grains suspended in a fluid. Suppose a Brownian particle located at the position x=0 when time t=0 has moved randomly on a straight line during time Δt. Since the movement of a Brownian particle is random, the location of the Brownian particle at t=Δt will be probabilistic. Especially, for smaller Δt elapsed, the Brownian particle will have a higher chance to be found near the starting location x=0 similar to a normal (or Gaussian) probability distribution with zero mean and a small standard deviation. For this reason, the Brownian motion is often described mathematically by random variables in time, which is called a stochastic process (time-dependent random variable).

If we let Xt be a stochastic process in R1 describing the position of a fluorescence molecule at time t, i.e., “Xt=x” means that the location of a fluorescence molecule at time t is x, then the probability of the Brownian particle located within the interval 0Δx at time t will be dependent on both Δx and the previous location:


assuming the initial location is the origin (X0=0). Bachelier [14] explicitly calculated this probability as


where D (μm/s2) is a diffusion coefficient. The probability density function (the integrand) is the fundamental solution of heat equation (Eq. (14)) that is the normal distribution with standard deviation σ=2Dt. Later, Einstein [12] showed that the probability density function of randomly moving particles (Brownian motion) satisfies the diffusion equation with a solution Φxt (Eq. (17)).

If gy is the probability of a Brownian particle to be found at location y when t=0, i.e., PX0=y=gy, then the distribution of the Brownian particles can be determined by solving an initial value problem:


which has the solution


as in Eq. (19).

2.3 Mean squared displacement

The spreading rate of diffusing particles is quantified by a diffusion coefficient, D, which characterizes a linear relationship between mean squared displacement (x2) of a Brownian particle and time, where MSD is defined as


For a diffusion process, MSD increases linearly in time with the rate of the diffusion coefficient:


where n is the spatial dimension (Rn) for a diffusion process. To derive this relation in 1D (R), we consider tx2


where we used Eq. (17). Notice that by the product rule


By solving for Dx22x2Φxtdx


Next, by integration by parts


Finally, by putting all together


for R1.


3. Fluorescence recovery after photobleaching

3.1 Principles of FRAP

Fluorescence recovery after photobleaching is a fluorescence-based biophysical tool developed in the 1970s to investigate the diffusion process in membranes of live cells. Discovery of the green fluorescent protein (GFP) and the invention of commercial confocal laser scanning microscopes (CLSMs) have broadened the accessibility of FRAP for many researchers in the field, and the applications of FRAP have become widely extended to the study of intracellular protein dynamics [15, 16, 17, 18]. Over the four decades, there have been considerable advances in microscope technology. However, the basic principle of FRAP remains the same. In FRAP, fluorescently tagged molecules in a small region of interest (ROI) are irreversibly photobleached using a high-intensity laser source for a short period of time, and then the exchange of fluorescence and photobleached molecules in and out of the bleached region is monitored using low-intensity laser excitation to track fluorescence recovery (Figure 3A). Due to the artifacts such as the diffusion during the photobleaching step (Figure 3B) and the photofading during the imaging step, FRAP data requires some corrections (Figure 3C). The diffusion during the photobleaching step can be corrected by using the experimentally measured postbleach profile as an initial condition for the FRAP model [19, 20, 21]. On the other hand, the photofading during the imaging step can be corrected by diving the raw FRAP data (FDatat) by the fluorescence intensity from the whole image (FDatat) (Figure 3D) [19, 20, 21]. Since different transport and reaction mechanisms may affect the curvature and the mobile fraction of a FRAP curve in various manners, kinetic parameters for underlying mechanisms can be obtained by comparing the FRAP curve to the corresponding theoretical FRAP models. For example, D can be measured by comparing a diffusion FRAP model with FRAP data for the best fitting D [19, 20].

Figure 3.

Example of FRAP data. (A) Representative images from a FRAP experiment on Alexa488-CTxB. (B) A postbleach profile from the image for t=0shows a wider spreading radius (effective radius; re) than the bleaching spot radius (nominal radius; rn) due to diffusion during photobleaching. (C) Mean fluorescence intensity (N = 13) from the bleaching ROI (,FDatat), whole image (•, FWholet), and background () from a FRAP experiment of Alexa488-CTxB. The image in the inset shows the locations where FDatat () and background () were measured. (D) In FRAP analysis, prebleach steady-state, postbleach initial, and postbleach steady-state fluorescence intensities are typically denoted as Fi, F0, and Fi. These parameters can be used to calculate the mobile fraction (Mf) and the immobile fraction (1Mf) from the corrected FRAP data for photofading (FDatat/FWholet) as indicated in the boxed equation.

3.2 Derivation of diffusion FRAP equation in R1

Quantitative FRAP analysis requires a mathematical description of fluorescence recovery for a given underlying transport/reaction kinetics through two different modes of CLSMs: photobleaching and photo-illumination. Although CLSMs use scanning laser for both photobleaching and photo-illumination, it has been reported for small bleaching spot size (we call this as the nominal radius of the laser); the scanning profile of CLSMs on a confocal plane is well approximated by a Gaussian function:


where rn is the nominal radius, i.e., radius of a bleaching ROI (the half-width at e2 laser intensity). Irn can be regarded as a photobleaching mode of CLSMs with a maximal laser intensity I0. A bell-shaped profile of Irnx defines total laser intensity I0 with Irnxdx=I0 resulting from the error function integral (Eq. (11)). Since the high-intensity mode of laser (Irnx) causes photobleaching of fluorophores, for illumination, laser intensity has to be attenuated to a lower laser intensity level. Therefore, for an attenuation factor ϵ1, a photo-illumination mode of CLSMs can be described as ϵIrnx. If we let uxt be the density of fluorophores (or fluorescent proteins) at a location x at time t, then fluorescence intensity at the position x at time t will be proportional to both the illumination laser intensity (ϵIrnx) and fluorophore density (uxt). Assuming the linear proportionality, fxt, the fluorescence intensity at a location xy at time t can be described as


where the proportionality constant, q, is referred to as a quantum yield or quantum efficiency. When a CLSM system is used to photobleach fluorophores, its postbleach profile is not exactly the same as the laser profile in most cases due to diffusion occurring during the photobleaching step. Assuming the first-order photobleaching process with a photobleaching rate α, a governing equation for a photobleaching process of freely diffusing fluorescent proteins can be described as a reaction–diffusion equation:


where u0 is the prebleach steady-state fluorescence intensity, which is regarded as a constant. Although the solution to Eq. (34) is hard to find, it is empirically proven [22] that a confocal postbleach profile can be described as a simple Gaussian function (constant minus Gaussian):


Note that different underlying kinetics for u yield a different FRAP equation. For free diffusion kinetics, the evolution of uxt can be described as the diffusion equation subject to the initial condition from a postbleach profile right after photobleaching.


where Dμm2/s is a diffusion coefficient and the Laplacian, Δ=2x2, in R1. The solution of the diffusion equation can be found as (Eq. (19))


by Eq. (11) (error function integration).

The total fluorescence intensity from the region of interest can be found by integrating this local fluorescence intensity over the ROI:


which is called a FRAP equation. To simplify Eq. (38) by using Eq. (37)


where Fi=CiI0 is the prebleach fluorescence intensity due to fluorophore density Ci. If we let x¯=x+θχ where θ=4Dt (dx¯=θdχ), then the integral term in Eq. (39) becomes




where γ=rn/re and τD=re2/4D. If we consider the immobile fraction (Figure 3D), the FRAP equation for mobile fluorophores is found as


for the mobile fraction, M is defined as (Figure 3D)


where Fi, F0, and F are prebleach steady-state fluorescence intensity, postbleach initial fluorescence intensity (F0), and postbleach steady-state fluorescence intensity, respectively. The calculations for the 1D FRAP model can easily be extended to higher-dimensional cases. For example, a diffusion FRAP equation in 2D (R2) and 3D (R3) is found as


4. Fluorescence correlation spectroscopy

4.1 Principles of fluorescence correlation spectroscopy

Fluorescence correlation spectroscopy is a standard bioengineering and biophysics technique for the study of molecular movements and interactions [23, 24, 25]. For FCS experiments, a laser beam is focused and stationed at a region of interest in the specimen (usually live cells). The illumination region formed by the focused laser is called a confocal volume, which is generally in the femtoliter range. As fluorescence molecules cross the confocal volume by diffusion or other transporting mechanisms, they emit fluorescence photons responding to the illumination laser (Figure 4A), and the fluctuations in the fluorescence signal, Ft, is monitored as a function of time which is called raw FCS data. Since different FCS measurements from different cells can be quite different depending on the fluorescent protein expression level, the raw FCS data is first standardized by

Figure 4.

Principles of fluorescence correlation spectroscopy analysis. (A) For FCS analysis for free diffusion, a static laser beam is focused on a specific region of interest. As the fluorescence molecules diffuse in and out of a certain domain, commonly called confocal volume (∼1 femtoliter), fluorescence intensities from the confocal volume fluctuate, yielding fluorescence time series. (B) The fluorescence time series data are processed into an autocorrelation curve by taking the average of the original time series data and the shifted time series data by τ to get an autocorrelation function (ACF) in τ. The ACF from the FCS data is next fitted to theoretical autocorrelation functions (ACFs) to determine underlying kinetic parameters, such as a diffusion coefficient.


where Ft is the fluorescence fluctuation in the confocal volume and Ft=1T0TFtdt is the time average of the fluorescence fluctuation during observation time T. Notice that the mean of standardized data (ΔFt/Ft) is zero. Next, the autocorrelation function of the standardized data is calculated by multiplying the standardized data, ΔFt/Ft, and the shifted standardized data by τ, ΔFt+τ/Ft, and then taking the average over time:


Notice that the autocorrelation has the maximum when τ=0 and converges to 0 as τ increases as ΔFt/Ft and ΔFt+τ/Ft become independent for a large τ.


An autocorrelation curve carries two crucial information. Since a large molecule will move slower than a light molecule, therefore the correlation decays at a longer time scale. On the other hand, the correlation amplitude is inversely proportional to the concentration of fluorophores due to the denominator for standardization. The information on the diffusion coefficient and concentration of fluorophores can be determined, once a mathematical model for Gτ is developed.

Stationarity and ergodicity of the diffusion process play a pivotal role to derive an FCS equation in a closed, yet simple, form. A continuous-time dynamical system such as Brownian motion is called ergodic when all the accessible microstates such as the locations of a Brownian particle are equally probable over a long period, i.e., the statistical properties from the time average at a position are same as the ensemble (spatial) average at any moment. On the other hand, a stationary process is a stochastic process whose probability distribution and parameters are invariant by shifts in time. Stationary and ergodic properties of a diffusion process were proven mathematically [26].

If we let nxt be the fluorescence molecule density per unit area, the temporal average of nxt at a location x0 and the spatial (ensemble) average of nxt can be defined as


Under stationarity and ergodicity of a diffusion process, we assume


where nxt0 can be though as a snapshot of all the positions of Brownian particles at any fixed time t0.

4.2 Derivation of diffusion FCS equation

For the fluorescence molecule density per unit area, nxt, if we let fxt be the fluorescence intensities due to photons from fluorescent proteins at the location x at the time t, then fxt is proportional to nxt. On the other hand, since more fluorescence photons can be generated under the higher laser intensity, fxt is also proportional to the laser intensity, Ix. Therefore, fxt satisfies


where Q is a proportionality constant for the product of the absorption cross section by the fluorescence quantum yield and the efficiency of fluorescence, and Ix is a function describing a Gaussian laser profile:


where ω is the half-width of the beam at e2, which measures the size of a confocal volume (V).

A bell-shaped profile of Ix defines a unit confocal volume (V) with V=Ixdx=1, resulting from the error function integral (Eq. (11)). Therefore, the fluorescent intensity (or the number of photons, Ft) from the confocal volume is determined by


where we used the fact that the Gaussian laser profile defines the confocal volume in the last equality to switch the integration domain from V to .

Lastly, we will also assume the spatial and temporal independence of fluorescence intensities:


This assumption hypothesizes that fluorescence intensities from different locations are not correlated but independent.

In FCS, to analyze the fluorescence fluctuations from the confocal volume (V), an autocorrelation function (ACF) of the variations in Ft is considered. The variations in the number of photons from the mean number of photons in a confocal volume (ΔF) are calculated by ΔFt=FtFt where Ft and Ft are the fluorescence intensity in the confocal volume at time t and the mean fluorescence in the confocal volume, respectively. Therefore, by Eq. (52)


where Δnxt=nxtnt, we used the identities Ixdx=1.

Next, the autocorrelation function of the standardized fluorescence fluctuations, ΔF/Ft, is computed by


where we used Eq. (50).

Notice that nxt satisfies the diffusion equation (Eq. (19)). Therefore, Δnxt+τ also satisfies a diffusion equation in τ and x with initial time at t (τ=0):


Consequently, the solution Δnxt+τ is found as (Eq. (21))


Next, we use the ergodicity of a diffusion process to derive some essential properties of the double integral. Because diffusion is an ergodic process, the time average can be replaced by the ensemble average.


where σ2=Δnx02 is the variance of nx0, or the mean square fluctuations of the fluorescence molecules, and δxx¯ is the Dirac delta function defined as Eq. (18). In Eq. (58), the stationary and ergodic assumptions were used in the third line to convert the time average to the spatial average at t=0.


By plugging Eq. (58) back into Eq. (55)


If we substitute y=x+4η (dy=4), then


where we used the fact


Now, we can evaluate the inner integral in Eq. (46) using a substitution z=x+η for x


where we used Eq. (11). Back to Eq. (61)


by the error function integration (Eq. (11)), where τD=ω2/4D, which is a diffusion time.

If fluorescence molecules undergo Brownian motion, then the number of photons in a confocal volume changes in time due to random movements of fluorescence molecules in and out of the confocal volume. In FCS analysis, the number of photons (or fluorescence molecules) from a confocal volume at any moment t is assumed to follow a Poisson distribution, in which the probability for k fluorescence molecules (or photons) to be found in the confocal volume is


where λ=Ft is the average number of fluorescence molecules (or photons) in the confocal volume. This assumption is reasonable for a diffusion process since the arrival process of infinitely many identical independent diffusion processes was shown to be a Poisson process [27]. Importantly, the mean (or expectation) and variance of a Poisson random variable are known to be equal


Since we assumed that Ft follows the Poisson statistics that has equal variance and mean


by Eq. (66). On the other hand, by Eq. (64)


which indicates that


By replacing the bulk parameters in Eq. (47) with 1/Ft


As we saw, with a Poisson distribution assumption on Ft, we can readily determine the average density of fluorescence molecules as well as the average number of fluorescence molecules in the confocal volume. Similar to FRAP equations, FCS equations in higher spatial dimensions can be found by similar calculations


where τDxy=ωxy2/4D and τDz=ωz2/4D with ωxy = the half-width of the beam at e2 in x/y direction and ωz = the half-width of the beam at e2 in z direction.


5. Conclusion

Diffusion plays a crucial role within biological systems in many different temporal and spatial scales from various perspectives. It is a dominant way for biological organisms to transport multiple molecules to desirable locations for cell signaling. However, to quantify the molecular diffusion, especially in live cells, is still challenging although a couple of tools are available, including fluorescence recovery after photobleaching and fluorescence correlation spectroscopy. Although FRAP and FCS were originally developed to study biological diffusion processes, they are now being applied not only to a diffusion process but also to a broad range of biochemical processes, including binding kinetics and anomalous diffusion. Since the derivation of FRAP and FCS equations for many biochemical processes shares many common steps with the diffusion FRAP and FCS equations, it is essential to understand the mathematical theory behind the diffusion FRAP /FCS equation [18, 22, 25, 28, 29, 30, 31, 32]. In this study, we provide a simple and straightforward derivation of FRAP/FCS equation for free diffusion based on calculus-level mathematics, so that FRAP/FCS equations and its applications are accessible to a broad audience. Although the applications of these FRAP and FCS equations to cell membrane biophysics from experimental perspectives can be a very important topic, it is beyond the scope of this chapter and therefore will not be covered here. These topics are well documented in various references, and interested readers are referred to [20, 31, 33], and references therein. We hope that this tutorial is understandable as well as gives readers a solid theoretical foundation for FRAP and FCS, bridging the gap between experimental and theoretical aspects of FRAP and FCS.


  1. 1. Murray J. Mathematical Biology: I. An Introduction (Interdisciplinary Applied Mathematics). 3rd ed. New York: Springer; 2007
  2. 2. Murray J. Mathematical Biology II: Spatial Models and Biomedical Applications (Interdisciplinary Applied Mathematics). 3rd ed. New York: Springer; 2011
  3. 3. Okubo A, Levin S. Diffusion and Ecological Problems, Modern Perspectives. 2nd ed. New York: Springer; 2002
  4. 4. Ritchie K, Spector J. Single molecule studies of molecular diffusion in cellular membranes: Determining membrane structure. Biopolymers. 2007;87(2–3):95-101
  5. 5. Skaug MJ, Faller R, Longo ML. Correlating anomalous diffusion with lipid bilayer membrane structure using single molecule tracking and atomic force microscopy. The Journal of Chemical Physics. 2011;134(21):215101
  6. 6. Das BB, Park SH, Opella SJ. Membrane protein structure from rotational diffusion. Biochimica et Biophysica Acta. 2015;1848(1 Pt B):229-245. DOI: 10.1016/j.bbamem.2014.04.002
  7. 7. Axelrod D, Koppel DE, Schlessinger J, Elson E, Webb WW. Mobility measurement by analysis of fluorescence photobleaching recovery kinetics. Biophysical Journal. 1976;16(9):1055-1069
  8. 8. Elson EL, Magde D. Fluorescence correlation spectroscopy. I. Conceptual basis and theory. Biopolymers. 1974;13:1-27
  9. 9. Crank J. The Mathematics of Diffusion (Oxford Science Publications). 2nd ed. USA: Oxford University Press; 1980
  10. 10. Carslaw HS, Jaeger JC. Conduction of Heat in Solids (Oxford Science Publications). 2nd ed. USA: Oxford University Press; 1986
  11. 11. Fick A. On liquid diffusion. Annalen Der Physik und Chemie Ergänzung. 1885;94:59
  12. 12. Einstein A. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Annalen der Physik. 1905;322(8):549-560
  13. 13. von Smoluchowski M. Zur kinetischen theorie der brownschen molekularbewegung und der suspensionen. Annalen der Physik. 1906;326(14):756-780
  14. 14. Bachelier L. Théorie de la spéculation. Annales Scientifiques de l’École Normale Supérieure. 1900;17(3):21-86
  15. 15. Carrero G, Crawford E, Hendzel MJ, de Vries G. Characterizing fluorescence recovery curves for nuclear proteins undergoing binding events. Bulletin of Mathematical Biology. 2004;66:1515-1545
  16. 16. Sprague BL, Pego RL, Stavreva DA, McNally JG. Analysis of binding reactions by fluorescence recovery after photobleaching. Biophysical Journal. 2004;86(6):3473-3495
  17. 17. Houtsmuller AB. Fluorescence recovery after photobleaching: Application to nuclear proteins. Advances in Biochemical Engineering/Biotechnology. 2005;95:177-199
  18. 18. Kang M, Day CA, DiBenedetto E, Kenworthy AK. A quantitative approach to analyze binding diffusion kinetics by confocal FRAP. Biophysical Journal. 2010;99(9):2737-2747
  19. 19. Kang M, Kenworthy A. Complex applications of simple FRAP on membranes. In: Faller R et al., editors. Biomembrane Frontiers. New York, USA: Humana Press; 2009
  20. 20. Day CA, Kraft LJ, Kang M, Kenworthy AK. Analysis of protein and lipid dynamics using confocal fluorescence recovery after photobleaching (FRAP). Current Protocols in Cytometry. 2012. Chapter 2; Unit 2.19
  21. 21. Kang M, Andreani M, Kenworthy AK. Normalizations, scaling, and photo-fading corrections for FRAP data analysis and their implications. PLoS One. 2015;10(5):e0127966
  22. 22. Kang M, Day CA, Drake K, Kenworthy AK, DiBenedetto E. A generalization of theory for two-dimensional fluorescence recovery after photobleaching applicable to confocal laser scanning microscopes. Biophysical Journal. 2009;97(5):1501-1511
  23. 23. Elson EL. Fluorescence correlation spectroscopy: Past, present, future. Biophysical Journal. 2011;101(12):2855-2870
  24. 24. Medina MA, Schwille P. Fluorescence correlation spectroscopy for the detection and study of single molecules in biology. BioEssays. 2002;24(8):758-764
  25. 25. Lee K, Astudillo N, Kang M. A simple derivation of diffusion fluorescence correlation spectroscopy equations. Journal of Fluorescence. 2020. DOI: 10.1007/s10895-019-02476-z [Epub ahead of print]
  26. 26. Derman C. Ergodic property of the Brownian motion process. Proceedings of the National Academy of Sciences of the United States of America. 1954;40(12):1155-1158
  27. 27. Nadler B, Schuss Z. The stationary arrival process of independent diffusers from a continuum to an absorbing boundary is Poissonian. SIAM Journal on Applied Mathematics. 2001;62(2):433-447
  28. 28. Kang M, Kenworthy A. A closed-form analytic expression for FRAP formula for the binding diffusion model. Biophysical Journal. 2008;95(2):L13-L15
  29. 29. Kang M, DiBenedetto E, Kenworthy A. Proposed correction to Feder’s anomalous diffusion FRAP equations. Biophysical Journal. 2011;100(3):791-792
  30. 30. Kang M, Day CA, Drake K, Kenworthy A, DiBenedetto E. Simplified equation to extract diffusion coefficients from confocal FRAP data. Traffic. 2012;13(12):1589-1600
  31. 31. Kang M, Andreani M, Kenworthy A. Validation of normalizations, scaling, and photofading corrections for FRAP data analysis. PLoS One. 2015;10(5):e0127966
  32. 32. Kang M, Day CA, Drake K, Kenworthy A. A novel computational framework for D(t) from fluorescence recovery after photobleaching data reveals various anomalous diffusion types in live cell membranes. Traffic. 2019;20(11):867-880
  33. 33. Chiantia S, Ries J, Schwille P. Fluorescence correlation spectroscopy in membrane structure elucidation. Biochimica et Biophysica Acta. 2009;1788(1):225-233

Written By

Minchul Kang

Submitted: 18 November 2019 Reviewed: 20 February 2020 Published: 30 September 2020