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

Table 1 Profile likelihood based identifiability analysis and confidence intervals of the in silico model example in log-space

Parameter	\(\hat {\theta }_{i}\)	y = D			y =[ D , A ] ^T			y = [D, B] ^T			y = [D, C] ^T
		Identifiability	Lower CI	Upper CI	Identifiability	Lower CI	Upper CI	Identifiability	Lower CI	Upper CI	Identifiability	Lower CI	Upper CI
log10k ₁₁	0.043	non-identifiable	−∞	∞	non-identifiable	−∞	0.65	non-identifiable	−1.08	∞	non-identifiable	−∞	∞
log10k ₁₂	0.301	non-identifiable	−∞	∞	non-identifiable	−∞	∞	non-identifiable	−∞	1.07	non-identifiable	−∞	∞
log10k ₂₁	0.398	non-identifiable	−∞	∞	non-identifiable	−∞	0.65	non-identifiable	−∞	∞	non-identifiable	−0.03	∞
log10k ₂₂	0.004	non-identifiable	−∞	∞	non-identifiable	−∞	∞	non-identifiable	−∞	∞	non-identifiable	−∞	0.45
log10k ₂₃	−0.301	non-identifiable	−∞	∞	non-identifiable	−∞	∞	non-identifiable	−∞	∞	non-identifiable	−∞	0.33
log10d	0.004	non-identifiable	−∞	0.42	non-identifiable	−∞	0.42	non-identifiable	−∞	0.42	non-identifiable	−∞	0.4

This table illustrates the potential impact of one additional readout to the initial setup y = D.

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