Biomedical Engineering Reference
In-Depth Information
70
80
70
60
60
50
40
30
20
10
50
40
30
20
10
0.01
0.02 0.03
MET-AMC concentration (mU)
0.04
0.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
1.45
1.5
A
B
Fractal dimension,
D
f
1.5
1.45
1.4
1.35
1.3
1.25
1.2
1.15
1.1
0.01
0.02
0.03
0.04
0.05
C
MET-AMC concentration (mU)
Figure 4.8
(a) Increase in the binding rate coefficient, k with an increase in the MET-AMC concentration
(in mU) in solution. (b) Increase in the binding rate coefficient, k with an increase in the fractal
dimension, D
f
. (c) Increase in the fractal dimension, D
f
with an increase in the MET-AMC
concentration (in mU) in solution.
The fit is reasonable. Only three data points are available. The availability of more data
points would lead to a more reliable fit. The fractal dimension,
D
f
, exhibits a weak depen-
dence on the MET-AMC concentration in solution as noted by the 0.1454 order of depen-
dence exhibited. Once again, the fractal dimension is based on a log scale, and even very
small changes in the fractal dimension on the biosensor surface lead to significant changes
in the degree of heterogeneity on the biosensor surface.
Forbes et al. (2007)
recently reported that the quantitative detection of amino acids is essen-
tial for drug discovery in the pharmaceutical industry. These authors point out that there is a
need for an amino-acid detection method that is high throughput, and is capable of detecting
a singe amino acid in the presence of other amino acids. They have recently presented a
HTS-compatible method for measuring the concentration of most naturally occurring amino
