Hardware Reference
In-Depth Information
2.5
Simulation Results
In this section, we evaluate the re-synthesis approach for error-recovery on rep-
resentative bioassays that are especially prone to fluidic errors. The completion
times for the two sensing schemes are compared; the re-synthesis results derived by
the greedy algorithm and the PRSA-based global optimization algorithm are also
presented.
2.5.1
Preparation of Plasmid DNA
First, we simulate the bioassay that is called “sample preparation of plasmid DNA
by alkalinelysis with SDS” [
17
,
24
]. During sample preparation, a mixture of three
reagents is required. The three reagents are:
• R
1
: Alkaline lysis Solution I [50 mM Glucose,25 mM Tris-HCl (pH 8.0), 10 mM
EDTA (pH 8.0)].
• R
2
: Alkaline lysis Solution II [0.2 N NaOH, 1 % SDS (w/v)].
• R
3
: Alkaline lysis Solution III (5 M sodium acetate, glacial acetic acid).
The required concentration of the mixture is 0.22 % of R
1
,0.44%ofR
2
,and
0.34 % of R
3
, which can be approximated as
28
128
56
128
of R
2
,and
44
128
of R
3
.
Figure
2.16
shows the sequencing graph to obtain the required concentration by
mixing R
1
, R
2
,andR
3
. This bioassay is mapped to a 10
10 electrode array and
all the electrodes at the boundary of the array are used as storage cells.
When errors are detected, the error-recovery capability of the cyberphysical
microfluidic system can be evaluated on the basis of the bioassay completion time.
The errors are randomly injected into the chip during the execution of the bioassay
and compare the completion time of the two sensing schemes. The results are
showninFig.
2.17
. Here the completion time is derived from the greedy algorithm
introduced in Sect.
2.4
. The results are the average of the values derived from
repeating the experiments ten times. For this case (no error-recovery), the final
of R
1
,
Fig. 2.16
Sequencing graph
for sample preparation of
plasmid DNA
