This paper deals with robust identification of dynamic metabolic flux model parameters based on parametric sensitivity analysis. First, the parameters in the model are ranked based on a global parametric sensitivity analysis to assess whether a subset of the parameters can be eliminated from further analysis. Then, the remaining significant parameters are identified based on the maximization of an overall parametric sensitivity measure subject to set based constraints that are derived from available data. The proposed method is illustrated on a case study of a model describing the diauxic growth of Escherichia coli in a batch culture.