Systems Biology (Methods in Molecular Biology)

3.3 Sloppiness In order to detect the level of sloppiness of the parameters involved
in the Tyson’ model we applied the procedure designed by Guten-
kunst et al. [6] only for the nonzero parameters. Results are shown
in Fig.2.

3.4 Inverse Model The procedure of inverse problems have been developed adding a
white noise on parameters. The key is, of course, the simultaneous
fitting of all ten datasets. Results are reported in Table3 with
related statistics.

Table 2

Structurally identifiable parameters and parameter combinations

Parameter Note

k6 Is uniquely identifiable

k7 Is uniquely identifiable

k9 Is uniquely identifiable

k8notP Is uniquely identifiable

k2 Is uniquely identifiable

k3 Is uniquely identifiable

k1aa Is uniquely identifiable

k4 Unidentifiable

k4prime Unidentifiable

k5notP Unidentifiable

25

20

15

10

5

0

–5

–10

k6 k8notP k9 k3 k1aa k7 k4 k4prime

Fig. 2Parameters confidence intervals in a semilog box plot

80 Rodolfo Guzzi et al.