Performance of objective functions and optimisation procedures for parameter estimation in system biology models

Relations between parameter estimates and non-identifiability. a Clustergram visualising the relations between the parameter estimates. b Scatter plot illustrating that the space occupied by the estimates is a low-dimensional manifold: here a 1D curve in 10D space; shown is a projection in 3D (blue dots), and 2D (grey dots). c Principal component analysis of the parameter estimates. Bars illustrate the number of principal components required to explain the variability of the parameter estimates. The colours indicate how much variance is explained by each principal component (PC). The data were brought onto the same scale by normalising each parameter with respect to the best estimate from GLSDC-DNS-LS (see Methods). The number of PCs required indicates the dimensionality of the estimated parameter space (linear approximation), thus indicating the degree of non-identifiabilit