This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found based on data that are assumed to be bounded within sets whereas, in the second approach, the limiting constraints are found based on parametric sensitivity analysis. The ability of these approaches to finding the active limiting constraints was verified through their application to two case studies: an in‐silico (simulated) data‐based study describing the growth of E. coli and an experimental data‐based study for Bordetella pertussis (B. pertussis) . © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:26–36, 2017