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Table 3 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 \(\hat {\theta }_{i}\) \(y=\left [\text {A B C D, C}_{k_{22}}\right ]^{\text {T}}\) \(y=\left [\text {A B C D, C}_{k_{21}}\right ]^{\text {T}}\) \(y=\left [\text {A B C D, D}_{k_{21}}\right ]^{\text {T}}\)
   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
  1. 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.