Mauro et al. (1996) utilized fluorometric sensing to detect polymerase chain-reaction-amplified DNA using a DNA capture protein immobilized on a fiberoptic biosensor. The authors used amplified DNA labeled with the fluorophore tetramethylrhodamine and the AP-1 consensus nucleotide sequence recognized by GCN4. This DNA was noncovalently bound to IgG-modified fibers. Wanting to see if they could reuse the fiber, the authors performed regeneration studies. They focused their attention on conditions that would permit the release of the bound DNA while leaving the IgG-PG-GCN4 assembly in a functional state. Figure 5.17 shows the curves obtained using Eqs. (5.1) (single-fractal analysis) and (5.2) (dual-fractal analysis) for ten consecutive runs. The points are the experimental results obtained by Mauro et al. (1996). A dual-fractal analysis was required since the single-fractal analysis did not provide an adequate fit for the binding curves.
Table 5.7 shows the values of the binding rate coefficient, k, and the fractal dimension, Df, obtained using Sigmaplot (1993) to fit the data. Since a dual-fractal analysis was used to model the binding curves, the results obtained from the single-fractal analysis will not be analyzed further. The Df, values reported for each of the ten runs were all equal to zero. This is due to the sigmoidal shape or concave nature of the curve (toward the origin) at very low values of time, t.
Figures 5.18a and 5.18b show the fluctuations in the binding rate coefficient, k2, and in the fractal dimension, Df2, respectively, as the run number increases from one to ten. No pattern is easily discernible from the data presented in Figs. 5.18a and 5.18b. Table 5.7 and Fig. 5.18c indicate that an increase in Df2 leads to a linear increase in k2, but there is scatter in the data. An increase in Df2 by about 16.9%—from a value of 1.9612 to 2.2938—leads to a 85.4% increase in k2—from a value of 91.122 to 169. For the regeneration runs, k2 may be given by k2 = (8.229+ 1.096)df23997±0'9319. (5.20)
Equation (5.20) predicts the k2 values presented in Table 5.7 reasonably well. There is some deviation in the data. Note the high exponent dependence of k2 on D{2. This underscores that k2 is sensitive to the surface roughness or the
Run |
k |
Df |
h |
h2 |
Df, |
Df, |
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