Biomedical Engineering Reference
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Table 9a Ranking and SRC value of the model parameters for each model output, for a time
point during the exponential growth phase on glucose
t = 17.2 h
Glucose
Ethanol
Oxygen
Biomass
SRC
Rank
SRC
Rank
SRC
Rank
SRC
Rank
r
G
;
max
-0.0089
2
0.7423
1
-0.0858
6
0.1111
4
r
O
;
max
0.0006
8
-0.1591
3
-0.5768
2
-0.0129
10
r
E
;
max
0.0002
11
-0.0837
5
0.0210
10
-0.0400
7
Y
Oxid
xg
-0.0107
1
-0.0777
6
-0.7884
1
0.9746
1
Y
Red
xg
0.0060
3
0.0467
8
-0.1599
4
0.1697
2
Y
Xe
0.0008
7
0.0070
10
0.0452
7
-0.0643
5
K
G
0.0058
4
-0.4819
2
-0.0301
8
0.0328
8
K
O
0.0042
5
0.0279
9
0.1142
5
-0.1664
3
K
E
-0.0005
9
0.0756
7
0.0027
11
-0.0103
11
K
i
0.0013
6
-0.1324
4
0.0273
9
-0.0420
6
k
L
a
-0.0005
10
-0.0070
11
0.2380
3
0.0170
9
Table 9b Ranking and SRC value of the model parameters for each model output, for a time
point during the exponential growth phase on ethanol
t = 27.2 h
Glucose
Ethanol
Oxygen
Biomass
SRC
Rank
SRC
Rank
SRC
Rank
SRC
Rank
r
G
;
max
-0.0253
11
-0.0003
11
0.0356
8
r
O
;
max
-0.1310
3
-0.2484
3
0.0409
5
r
E
;
max
-0.1700
2
-0.2534
2
-0.0208
9
Y
Oxid
xg
-0.9504
1
-0.6101
1
0.9812
1
Y
Red
xg
-0.0653 7 0.0936 4 0.1157 3
Y
xe
0.0281 10 0.0105 9 -0.0357 6
K
G
-0.1195 4 -0.0710 6 0.0997 4
K
O
0.1150 5 0.0748 5 -0.1279 2
K
E
0.0394 8 -0.0010 10 -0.0120 11
K
i
-0.1110 6 0.0596 7 -0.0356 7
k
L
an -0.0284 9 0.0560 8 0.0202 10
Values corresponding to the prediction of glucose are not shown, as the linear regression was
found not to be suitable for this time point and model output (R
2
\ 0.7)
3 Discussion
A mechanistic model of glucose oxidation by Saccharomyces cerevisiae has been
taken as an example and has been analyzed rigorously with a number of methods.
The chosen case study is purposely kept relatively simple in order to better illu-
minate how the different methods work and what kind of information is gained in
each step. In practice, the presented analysis methods are generic and can be
applied to a wide range of process models to assess their reliability. Each step of
the analysis has been commented in detail already. However, one thing that cannot
be emphasized enough is the importance of collecting proper datasets: biological
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