Fractional Differential Equation for the Analysis of
             Electrophysiological Recordings
                    Darí...
Domínguez DM, Marín M.

time series reveal slow velocities Liebovitch2; Liebovitch and Sullivan3;
Mandelbrot4.
         Mu...
Fractional Differential Equation for the Analysis of Electrophysiological Recordings -

The data sets which we used was pu...
Domínguez DM, Marín M.


                                               Voltage (mV)                 Hcell(1)             ...
Fractional Differential Equation for the Analysis of Electrophysiological Recordings -

4. Fractional Differential equatio...
Domínguez DM, Marín M.

         The other function dα(t) is not considered, because there are not data
from the experimen...
Fractional Differential Equation for the Analysis of Electrophysiological Recordings -

References

1. J Bassingthwaighte,...
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Fractional Differential Equation for the Analysis of Electrophysiological Recordings

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Fractional Differential Equation for the Analysis of Electrophysiological Recordings

  1. 1. Fractional Differential Equation for the Analysis of Electrophysiological Recordings Darío M. Domínguez1, Mariela Marín2 Mathematics Department, Universidad Militar Nueva Granada, Bogotá, 1 Colombia Biophysical Laboratory, Centro Internacional de Física, Bogotá, Colombia 2 Email: fracumng@umng.edu.co Abstract: The use of fractal analysis is a study for times series of macrophage’s macroscopic ion currents signals. Hurst coefficients (H) and fractional dimension were calculated on the time series in the zone between peak and steady state currents of the pulse assuming white noise. We show that H, is different from 0.5, indicating that the time series cannot be considered white noise. H is not only different but below 0.5 implying an anti persistent pattern. In addition, we show that fluctuation of the ion currents IH versus voltage curves fit an equation where IH (V) = f (V, α, m, d) for a voltage V, α associates with fractional calculus and m, d fit the model to the voltage domains studied. Fitting by α fractional confirms that the phenomenon has memory and we suggest that α values are associated with the complexity of the current. Keywords: Hurst, fractal, ion current, fractional, memory. 1. Introduction Time series recordings by electrophysiological techniques allow the detection of capacitive currents; currents passing through ion channels and cellular membrane potentials and they are use for functional characterization of electrical properties and ion channels of the cellular membrane. There are two basic ways of recording information from the cellular membrane with electrophysiological techniques. One in which only a small patch of the membrane is recorded (single channel recordings) and in which the current through one or few channels is observed (ion current). Another in which all the cellular membrane is recorded (whole cell recordings) and the currents through many ion channels are observed (macroscopic currents). In single channel, the data set is analyzed to determine energy barriers, velocity of transition between open and close states and ion channel conductance. The use of fractal statistical analysis is mainly in single channel currents, particularly on experimental data coming from excitatory plasma membranes as those found in neurons. This type of analysis assumes that the ion current recorded is a phenomenon that has memory, thus ion channel fluctuations can be assumed as a series of large number of sub states, switching between sub states may vary in time and may be linked and therefore previous states are important Bassingthwaighte1. Fractal analysis predicts that the apparent constant velocity during channel opening and closing will vary inversely against the scale of the time series observed, therefore short time series reveal fast velocities whereas long
  2. 2. Domínguez DM, Marín M. time series reveal slow velocities Liebovitch2; Liebovitch and Sullivan3; Mandelbrot4. Much less, other works has made on models and with whole cell recordings (total currents). In this work we have applied a non-linear times series techniques from the data set provided by Biophysical Lab. (CIF Bogotá). Time series signal of ion currents from macrophage-like cells, were recorded using the whole cell configuration of the patch clamp technique Hamill5. Our main interest is to try to classify these series and to analyze if their dynamical behaviors are correlated in some way. First, a preliminary study carried out with the aim of characterizing those times series in terms of long-term memory (R/S analysis) and the fractal dimension calculus (D). Here we adjust the model through the solution of a differential equation of fractional order. 2. Time Series of the Electrophysiological recordings Outward currents of Macrophages: Cell membranes solve the problem of exchanging ions across them using integral membrane proteins. Among these proteins, ion channels are the most efficient, diffusing ions in favor of their electrochemical potential. Ion channels are ubiquitous in cells, are complex proteins that span the cell membrane, gate their aqueous pore in response to voltage differences, ligands or mechanical stimuli and are involved in functions related with action potential propagation, muscle contraction and cell signaling. In this work, we studied the time series of the macrophage’s ion current signals. Macroscopic outward currents Io through cellular membranes from cells of the immune system, using the whole cell configuration of the patch clamp technique by Hamill5. The time series are outward current elicited in response to voltage steps from -40 mV to 90 mV; in 20 mV increments (see Figure 1). Each data set contains 3800 points, which corresponds to ionic flow at this potential. Time series are the study to use fractal analysis Glöckle and Nonnenmacher6, Leibovitch7. Figure 1. Ionic currents present in control J774.1 cells. Outward current elicited in response to voltage steps from a holding potential of -60 mV. To values ranging from -40mV to 90 mV, in 20 mV increments.
  3. 3. Fractional Differential Equation for the Analysis of Electrophysiological Recordings - The data sets which we used was purchased by Biophyisical Lab (CIF). The data sets are recording by the electrophysiologial thecnique in whole cell configuration for detection of macroscopic currents. The Figure 1 represents the data sets after transfomation. Each data set cell (whlose cell recording) corresponds to 10 ion current siganls that contains 3800 points each one. 3. Data Analysis 3.1 R/S Analysis Rescaled Rang analysis (R/S analysis) is the tool to study long-term memory and fractality, first introduced by Hurst (1951) in hidrology and then Mandelbrot (1983) said that R/S analysis is more powerful tool in detecting long range dependence than the conventional analysis like correlation analysis, variance ratios and spectral analysis. In this method, one measure of culmulative deviations from the mean of the serie is changing with the time. It has found that, for some time series, the dependence of the R/S on the number of data point follows an empirical power law described as (R/S)=(R/S)0nH , where(R/S)0 is a constant, n is the time index for periods of different longth, and H is the Hurst exponent (R/S)n is defined as max1≤ r ≤ n A ( t , n ) − min1≤ r ≤ n A ( t , n ) (1) ⎛R⎞ ⎜ ⎟= ⎝ S ⎠n 1τ ∑ ( s (t ) − S n ) 2 n t =1 Where A(t,n) is the cumulative departure of the time seires s(t) from the time t +n A (t, n ) = ∑ ( s (i ) − s ) average over the time interval n : s 2 n n i =t The Hurst exponent, 0 ≤ H ≤ 1, is equal to 0.5 for random, white noise series, <0.5 for rough anticorrelated series, and >0.5 for positively correlated series. In this work, the recordings obtained were converted to Microsoft Excel 4.0 and then transferred to Benoit, 1.3, Fractal Analysis System (TruSoft Int'l Inc., St. Petersburg, FL, USA) to estimate H. We consider three controls cells for the analysis. Each cell had 10 times series for current elicited in response to voltage steps from a holding potential of -60 mV. The results for these cells, are summarised on table 1. As it can see, the time series between I peak (Ipeak) was the peak current determined at 42 ms and stacionay current (Iss), was the mean current at the end of the pulse that was 790-830 ms for outwards currents, normally assumed to be white noise. We obtained values different and bellow of 0.5 for all times studied, suggesting that the time series does not follow a random pattern but that the phenomena has memory, wich indicates that the series are antipersistent. This result indicates that the fluctuations of the time series studied correlated negatively.
  4. 4. Domínguez DM, Marín M. Voltage (mV) Hcell(1) Hcell(2) Hcell(3) -90 0.347 0.336 0.3 -70 0.221 0.169 0.224 -50 0.197 0.176 0.185 -30 0.233 0.2 0.187 -10 0.275 0.233 0.213 10 0.285 0.265 0.246 30 0.273 0.251 0.269 50 0.286 0.279 0.273 70 0.294 0.293 0.287 90 0.336 0.288 0.316 Table 1. Estmated H, using Benoit Software for three cells in the differents potentials. 3.2 Fractal Dimension The IOut recordings (Figure 1), show fluctuations that appear to be self- similar. Calculation of fractal dimension as well as of H by rescaled range analysis were performed in the interval between times to Ipeak and Iss where signals are usually assumed to be white noise (42−790 ms for IOut), corresponding to a non-stationary process. Once the H were calculated, the fractal dimension is defined as D=2-H, (the results not showed). If the fractal dimension is D=1.5, or the Hurst coeficient is H=0.5, and the correlation is 0 then the local flows are completely random. The value of H is associated with the extent of the fluctuation in the flow in this area, associated with the gating of the channels and then IH is defined as the value of coefficient Hurst at intervals to their respective voltage. Once the IH values are calculated, we plot them against Vm (see Figure 2). The curves obtained have the same pattern as that found when the ratio Iss to the apparent Ipeak was plotted versus voltage (not showed). 0,5 0,5 0,4 0,4 0,3 0,3 IH IH 0,2 0,2 0,1 0,1 0,0 0,0 -100 -80 -60 -40 -20 0 20 40 60 80 100 -100 -80 -60 -40 -20 0 20 40 60 80 100 V (mV) 0,5 V (mV) 0,4 0,3 IH 0,2 0,1 0,0 -100 -80 -60 -40 -20 0 20 40 60 80 100 V (mV) Figure 2. Fluctuation currents IH vs. voltage (mV). Outward currents for three control cells.
  5. 5. Fractional Differential Equation for the Analysis of Electrophysiological Recordings - 4. Fractional Differential equation: IH as a function of voltage Definition: The Hurst coefficient is like the fluctuation current in the time series. So, IH(V) as H is a value different from 0.5 indicates a process with memory (Figure 3). Therefore, the curves obtained were fitted with a model that also has memory, in other words a model derived from fractional calculus Glockle and Nonnenmacher6; Carpentieri and Mainardi8; Bassingthwaighte et al.1; Picozzi and West9; Vargas et al.10; Vargas et al.11; Metzler and Klafter12. The model with the best fit gives the following equation: ⎛ π ⎞⎞ ⎛ ⎛ π ⎞⎞ ⎛ ⎜ ( mV + d ) cos ⎜ ⎟ ⎟ 2 I H (V ) = cos ⎜ ( mV + d ) sin ⎜ ⎟ ⎟ + 0.31 ⎝α ⎠⎠ ⎝ (2) e α ⎝α ⎠⎠ ⎝ This fractional equation (2) fits the IH as a function of voltage for IO (Figure 3). The curve which fit is significant by chi2 analysis (p-value <0.01). The parameters m and d in this equation are scaling parameters. V is voltage, and α is a parameter that related with diffusion. For IO, at voltages between - 90 to 90 mV, the found values were: m was 0.024 d was 4.5 and α was 1.36. The IH vs voltage curve is a function of the theoretical solution of the differential fractional equation Dα I H (V ) = I H (V ) (3) 13 When H<0.5, one has α = 2 / ( 2 H 0 + 1) Darses and Saussereau . The solution of the fractional differential equation is I H (V ) = − J α I H (V ) (4) For the Laplace transform Sα L ( Ι H (V ) ) = (5) 1+ Sα For the Laplace inverse transform ) ( Sα Sα 1 1 1 iπ − iπ ∫ e 1 + S α dS = 2π i Ha e 1 + S α dS + α te α + te α ∫ I= SV SV 2π i Br = dα ( t ) + gα ( t ) thus 1 ⎧ t cos⎜ α ⎟ ⎛ it sin ⎜ α ⎟ − it sin ⎜ α ⎟ ⎞ ⎫ ⎛π ⎞ ⎛π ⎞ ⎛π ⎞ ⎪ ⎪ gα ( t ) = ⎨e + e ⎝ ⎠ ⎟⎬ (6) ⎜e ⎝⎠ ⎝⎠ ⎜ ⎟ α⎪ ⎠⎪ ⎝ ⎩ ⎭ We obtain ⎛π ⎞ ⎛ π ⎞⎞ ⎛ 2 (7) t cos ⎜ ⎟ gα ( t ) = ⎝α ⎠ cos ⎜ t sin ⎜ ⎟ ⎟ e α ⎝α ⎠⎠ ⎝
  6. 6. Domínguez DM, Marín M. The other function dα(t) is not considered, because there are not data from the experiments. 0,5 0,5 0,4 0,4 0,3 0,3 IH IH 0,2 0,2 0,1 0,1 0,0 0,0 -100 -80 -60 -40 -20 0 20 40 60 80 100 -100 -80 -60 -40 -20 0 20 40 60 80 100 V (mV) V (mV) 0,5 0,4 0,3 IH 0,2 0,1 0,0 -100 -80 -60 -40 -20 0 20 40 60 80 100 V (mV) Figure 3. Experimental data fit by gα 5.Conclusions In this paper we first examined a process of gating memory because the Rescaled Range Analysis (R/S analysis) shows that there is an antipersistent through the calculation of the Hurst coeficient, H<0.5 in all time series, indicating that is a memory process. In addition, the fractal analysis shows that on any scale of the process the gating is not random. The fractional analysis suggests that the underlying process in the macroscopic scale of gating is a complex process and does not follow the laws of simply random. Then there is a determinism in gating of ionic currents when the process begins. Finally, determining the behavior of the model, made possible to study how the macrophages acts when it is infected, which will be our future study. Acknowledgment This work was supported by Grant: 1123240520182 from the Programa Ciencias Básicas, COLCIENCIAS. Universidad Militar Nueva Granada and Centro Internacional de Física, Bogotá, Colombia.
  7. 7. Fractional Differential Equation for the Analysis of Electrophysiological Recordings - References 1. J Bassingthwaighte, LS Liebovitch and B West. Fractal Physiology (Oxford University, 1994). 2. LS Liebovitch, J Fischbarg, JP Koniarek, I Tododrova, M Wang. Fractal Model of Ion-Channel Kinetics. Biochim Biophys Acta. 896, 173 (1987). 3. LS Liebovitch and JM Sullivan. Fractal Analysis of a voltage-dependent potassium channel from cultured mouse hippocampal neurons. Biophys J. 52, 979 (1987). 4. B Mandelbrot. The Fractal Geometry of Nature (Freeman, New York, 1983) 5. O Hamill, A Marty, E Neher, B Sakmann, F Sigworth. Improved Patch-Clamp Techniques for high-reslution current recording from cells and cell-free membrane patches. Pflügers Arch. Eur. J. Physiol. 391, 391 (1981). 6. W Glöckle and T Nonnenmacher. A fractional Calculus approach to self- similar protein dyanmicas. Biophys. J. 68, 46 (1995). 7. LS Liebovitch. Ion Channel Kinetics. In Fractal Geometry in Biological Systems, an Analytical Approach, edited by P. M. Iannaccone and M. Khokha (CRC Press, Boca Raton, FL, 1996). 8. A Carpentieri and F Mainardi. Fractals and Fractional Calculus in Continuum Mechanics. SpringerWien NewYork, 1997 9. S Picozzi, B West. Fractional Langevin Model of Memory in Financial Markets. Phys Rev E Stat Nonlin Soft Matter Phys 66: 046118, 2002. 10. WL Vargas, LE Palacio, DM Domínguez. Anomalous transport of particle tracers in multidimensional cellular flows. Phys Rev E Stat Nonlin Soft Matter Phys 67. 026314, 2003 11. WL Vargas, JC Murcia, LE Palacio, DM Domínguez. Fractional diffusion model for force distribution in static granular media. Phys Rev E Stat Nonlin Soft Matter Phys 68: 0213022003, 2003. 12. R Metzler, J Klafter. The restaurant at the end of the random walk: Recent developments in the description of anomalous transport by fractional dynamics. J Phys A: Math Gen 37: R161-R208, 2004. 13. S Darses, B Saussereau. Time reversal for drifted fractional Brownian motion with Hurst index H>1/2. Electronic Journal of Probability. Vol. 12:1181-1211, 2007

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