On the identifiability of metabolic network models S. Berthoumieux, M. Brilli, H. de Jong, D. Kahn, E. Cinquemani Journal of Mathematical Biology, 67(67):17951832, 2013. A major problem for the identification of metabolic network models is parameter identifiability, that is, the possibility to unambiguously infer the parameter from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudolinear models of metabolism, socalled linlog models. Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use Singular Value Decomposition (SVD) to detect identifiability problems and reduce the model to an identifiable approximation by a Principal Component Analysis (PCA) approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a stateoftheart dataset on central carbon metabolism in Escherichia coli yields the surprising result that only 4 out of 31 reactions, and 37 out of 100 parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of largescale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudolinear models, such as generalized massaction (powerlaw) models.Moreover, it provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.
