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Table 2 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, A B] T y= [D, A C] T y =[D, B C] T y =[D, A B 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 identifiable −0.72 0.44 non-identifiable 0.41 identifiable −0.86 1.17 identifiable −0.70 0.36
log10k 12 0.301 non-identifiable 0.86 non-identifiable non-identifiable 0.82 non-identifiable 0.81
log10k 21 0.398 identifiable −0.16 0.60 identifiable 0.07 0.63 identifiable −0.01 1.45 identifiable 0.12 0.59
log10k 22 0.004 non-identifiable 3 non-identifiable 0.44 non-identifiable 0.39 non-identifiable 0.39
log10k 23 −0.301 non-identifiable non-identifiable 0.31 non-identifiable 0.29 non-identifiable 0.23
log10d 0.004 non-identifiable 0.42 non-identifiable 0.40 non-identifiable 0.39 non-identifiable 0.37
  1. This table illustrates the potential impact of additional readouts to the initial setup y = D.