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

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

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

ISSN: 1471-2105

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