Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions

Table 3 Profile likelihood based identifiability analysis and confidence intervals of the in silico model example in log-space

Parameter	\(\hat {\theta }_{i}\)	\(y=\left [\text {A B C D, C}_{k_{22}}\right ]^{\text {T}}\)			\(y=\left [\text {A B C D, C}_{k_{21}}\right ]^{\text {T}}\)			\(y=\left [\text {A B C D, D}_{k_{21}}\right ]^{\text {T}}\)
		Identifiability	Lower CI	Upper CI	Identifiability	Lower CI	Upper CI	Identifiability	Lower CI	Upper CI
log10k ₁₁	0.043	identifiable	−0.62	0.29	identifiable	−0.70	0.36	identifiable	−0.25	0.23
log10k ₁₂	0.301	non-identifiable	−∞	0.73	non-identifiable	−∞	0.81	identifiable	−0.02	0.63
log10k ₂₁	0.398	identifiable	0.24	0.55	identifiable	0.12	0.59	identifiable	0.23	0.54
log10k ₂₂	0.004	identifiable	−0.23	0.21	non-identifiable	−∞	0.39	identifiable	−0.33	0.24
log10k ₂₃	−0.301	non-identifiable	−∞	0.02	non-identifiable	−∞	0.23	non-identifiable	−∞	0.17
log10d	0.004	non-identifiable	−∞	0.28	non-identifiable	−∞	0.37	non-identifiable	−∞	0.32

This table illustrates the effect of an additional inhibition and readout selection. The additional readout and inhibition is indicated by the 5th letter and subscripted parameter, which corresponds to the inhibited reaction.

ISSN: 1471-2105