Biomedical Engineering Reference
In-Depth Information
the biosensor surface with different biosensor performance parameters. More specifically, the
interest is in finding out how changes in the fractal dimension or the degree of heterogeneity on
the biosensor chip surface affect the different biosensor parameters of interest.
Unless specifically mentioned there is no nonselective adsorption of the analyte. In other
words, nonspecific binding is ignored. Nonselective adsorption would skew the results
obtained very significantly. In these types of systems, it is imperative to minimize this non-
selective adsorption. It is also recognized that, in some cases, this nonselective adsorption
may not be a significant component of the adsorbed material and that the rate of association,
which is of a temporal nature would depend on surface availability.
Fang et al. (2006)
recently reported that cell-based assays that help monitor the activities and
health of living cells are important in drug discovery and development. These cells-based
assays measure specific molecular cellular events (
Blake, 2001; Taylor et al., 2001; Abraham
et al., 2004
).
Fang et al. (2006)
emphasize the need for a cell-based assay to provide a non-
invasive and continuous record of cellular activity. Besides, a high sensitivity would be help-
ful. These authors have recently applied RWG biosensors to analyze cytoskeleton modulation
(
Fang et al., 2005a,b
), cell signaling mediated through epidermal growth factor (EGF) recep-
tor (
Fang et al., 2005b
), or a G-protein-coupled receptor (GPCR) bradykinin B
2
receptor
(
Fang et al., 2005c
). They point out that these studies have led to the development of
MRCAT (mass redistribution cell assay technology). In their latest publication
Fang et al.
(2006)
have introduced multiple optical readouts for cell sensing using RWG biosensors.
Theoretical analysis as well as experimental data is presented with emphasis on the sensi-
tivities of these optical readouts as the nature of the dynamic mass distribution values.
Fang et al. (2006)
analyzed the binding and dissociation of different concentrations of bradykinin
to a bradykinin B
2
receptor on a RWG biosensor to help characterize stimulation-mediated cell
responses including signaling.
Figure 9.1a
shows the binding and dissociation of 128 nM brady-
kinin concentration in solution to the bradykinin B
2
on a RWG biosensor surface. A dual-fractal
analysis is required to adequately describe the binding and the dissociation kinetics. The values
of (a) the binding rate coefficient,
k
, and the fractal dimension,
D
f
, for a single-fractal analysis,
(b) the binding rate coefficients,
k
1
and
k
2
, and the fractal dimensions,
D
f1
and
D
f2
, for a dual-
fractal analysis, and (c) the dissociation rate coefficient,
k
d
, and the fractal dimension,
D
fd
,for
a single-fractal analysis, and (d) the dissociation rate coefficients,
k
d1
and
k
d2
, and the fractal
dimensions,
D
fd1
and
D
fd2
, for a dual-fractal analysis are given in
Tables 9.1
and
9.2
.
It is of interest to note that for a dual-fractal analysis as the fractal dimension increases by a
factor of 1.965 from a value of
D
f1
equal to 1.402 to
D
f2
equal to 2.7544, the binding rate coef-
ficient increases by a factor of 12.98 from a value of
k
1
equal to 0.0199 to
k
2
equal to 0.3579.
Figure 9.1b
shows the binding and dissociation of 64 nM bradykinin concentration in solution
to the bradykinin B
2
on a RWG biosensor surface (
Fang et al., 2006
). Once again, a
