A fundamental problem in model identification is to investigate whether unknown parameters in a given model structure potentially can be uniquely recovered from experimental data. This issue of global or structural identifiability is essential during nonlinear first principles model development where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models. The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters in dependence of the given measurements and input perturbations. (c) 2008 Elsevier Ltd. All rights reserved.