| The present work describes the analysis of the diffusion-limited binding and dissociation kinetics of analyte in solution to a receptor immobilized on a biosensor surface. The binding and dissociation kinetics were analyzed within a fractal framework. Fractals are disordered systems. Biosensor surfaces, are heterogeneous in nature, and this heterogeneity on the surface is described using the fractal dimension. Both single- and dual-fractal analysis models were applied to describe the binding and dissociation kinetics. In this work the fractal analysis approach is used to examine the binding of (i) ligands and coactivators to estrogen receptors (ER) immobilized on SPR biosensors (Warnmark et al., 2001), (ii) binding of prostate specific antigen (PSA) to anti-PSA antibody immobilized on a microcantilever biosensor (Wu et al., 2001), and (iii) binding of calcein esters to mouse fibroblast cells immobilized in microwells (Taylor and Walt, 1999). Values for the binding and dissociation rate coefficients and fractal dimensions were obtained by performing a linear regression using Corel Quattro Pro, 8.0 (1997).; The analysis indicates that the binding and the dissociation rate coefficients are very sensitive to the degree of heterogeneity (surface roughness) that exists on the biosensor surface. The binding and dissociation rate coefficient(s) expressions developed as a function of the fractal dimension are of particular value because they provide a better means to control biosensor and immunosensor performance. Predictive expressions are also presented for the affinity, K D (=kd/ka) as a function of the ratio of the fractal dimensions, (Dfd/Dfa). The analysis adds further physical insights into biomolecular interactions on biosensor surfaces. These insights may be used to enhance performance characteristics of biosensors.; Fractal concepts have also been used to analyze biomedical signals. Biological and biomedical signals by their very nature are chaotic in nature. They give rise to fractal structures. The fractal theory was used analyze cardiac Holter recordings obtained from blood-related members of two families suspected of having genetic defects leading to the serious heart ailment Hypertrophic Cardiomyopathy (HCM). The commercially available Fractal Vision (1994) software is used to perform the analysis. Values of the fractal dimension were obtained from the temporal recordings. These values are indicative of the general well being or pathological condition of the heart.; The fractal analysis indicates the presence of diurnal patterns in the cardiac recordings. These cardiac recordings exhibited oscillations and wide variations in the fractal dimensions estimated for the all the subjects tested. An attempt was made to risk-stratify the individual family members into three categories ‘Affected’, ‘Normal’ and ‘Carrier’ based on the average fractal dimension value obtained over a 24- or 48-hour time interval. The results of our fractal analysis and risk-stratification were compared to the previously known clinical diagnosis results. The results were in very good agreement in the case of one family. Some discrepancies were observed in the results for the other family. More ECGs and families need to be further analyzed and risk-stratified to validate the method. However based on the initial results the fractal analysis via the fractal dimension exhibits potential as a supplementary diagnostic tool, and provides a very convenient, rapid and easy technique to analyze ECGs. |
