Methods for checking structural identifiability of nonlinear biosystems: A critical comparison

6 pages, 2 figures, 1 tableModel parametric identification is a critical yet often overlooked step for the modelling of biosystems. Modern experimental techniques can be used to obtain time-series data which may then be used to estimate model parameters. However, in many cases, a subset of model parameters may not be uniquely estimated, independently of the quantity and quality of data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model. This work presents a review and a critical comparison of methods to analyze the structural identifiability of non-linear models. Three examples, of increasing level of complexity, related to the modelling of biochemical networks, will be used to illustrate advantages and disadvantages of the available techniques. Results reveal that the generating series approach combined with the identifiability tableau is the most promising to analyze large scale highly nonlinear modelsN