Biomedical Engineering Reference
In-Depth Information
directly to PAC and DNA coupled via biotin-streptavidin ( Krieg et al., 2006 ), (c) binding and
dissociation of trace amounts of TNT in m g/L to anti-TNT antibody immobilized on a proto-
type fluorescence-based detector system (KinExA Inline Biosensor, Sappidyne Instrument,
Inc.; Bromage et al., 2007 ), (d) binding of EBP to S. cerevisiae and the binding of 17 b -estra-
diol ( Baronian and Guruzada, 2007 ), binding of different units of restriction endonuclease
activity using a MB ( Ma et al., 2007 ), and (f) binding and dissociation of different rabbit
IgG concentrations (in m M) in solution to A10B scFv ( Tang et al., 2006 ).
Predictive relations are developed for the binding and dissociation rate coefficients and for
the fractal dimension in the binding phase. For example, for the binding of TNT in solution
to anti-TNT antibody immobilized on a KinExA biosensor ( Bromage et al., 2007 ), (a) the
binding rate coefficient, k for a single-fractal analysis exhibits close to a one-half (equal to
0.492) order of dependence on the fractal dimension D f , and the dissociation rate coefficient
k d exhibits a 4.65 order of dependence on the degree of heterogeneity that exists on the sens-
ing surface. In this case, the dissociation rate coefficient is much more sensitive to the degree
of heterogeneity that exists on the sensing surface than the binding rate coefficient. Also,
(b) the binding rate coefficient k for a single-fractal analysis exhibits close to a one and a half
(equal to 1.593) order of dependence on the endonuclease unit in solution for the real-
time monitoring of the DNA cleavage process catalyzed by RsaI endonuclease ( Ma et al.,
2007 ), and (c) the binding rate coefficient k for a single-fractal analysis exhibits a 4.753 order
of dependence on the fractal dimension D f or the degree of heterogeneity on the sensing
surface.
The predictive relationships developed and presented for the different analyte-receptor
reactions occurring on the different biosensor surfaces are useful as they may be used to
manipulate the different biosensor parameters (such as the binding and the dissociation rate
coefficients) in required or desired directions. As the biosensor systems analyzed were
selected at random, the fractal analysis technique may also be applied to other biosensor
systems. A particular advantage of the fractal analysis method is that it provides a
quantitative measure of the degree of heterogeneity that exists on the biosensor surface.
This provides one with an extra variable that biosensorists may use to help enhance or mod-
ify the different and relevant biosensor parameters in desired directions. At times, this
may require some ingenuity in the sense that changing one particular experimental variable
may or may not affect the biosensor performance parameter(s) in different directions. For
example, increasing the sensitivity may decrease the stability or increase the detection
time. If one is fortuitous enough, then perhaps a change in an experimental variable may
affect two (or more) biosensor performance parameters simultaneously in required or
desired directions. For this to occur, it behooves one to know as much as one can about
the biosensor system being analyzed. The fractal analysis presented is one step in that
direction.
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