A practical strategy is presented addressing the related issues of model parameter identifiability and estimability that was applied to a large-scale, dynamic, and highly nonlinear biological process model describing the metabolic behavior of mammalian cell cultures in a continuous bioreactor. The model used consists of 27 inputs, 32 outputs, and more than 350 parameters; is compartmental in nature; and represents the state of the art in terms of model complexity and fidelity. The strategy adopted falls under the scope of estimability and comprises of two parts: (a) a parameter perturbation study that singly perturbs parameters under a number of deterministically sampled model input vectors and consequently partitions them into those that yield significant changes in the outputs (the estimable parameter set) and those that do not and (b) subsequent evaluation of Monte Carlo estimates of global sensitivity indices of these two sets, which quantitatively assess the amount of parameter sensitivity contained both within and between the sets. Of the 357 parameters, 37 were found to be estimable to within at least +/-25% of their nominal parameter value and, under nominal experiment conditions, accounted for 48% of the model's sensitivity. The remaining 320 parameters accounted for just 4% of the model's sensitivity. As expected, significant interactions were found to exist between these two sets. Interactions of the estimable parameter set with the inestimable set accounted for 48% of the model's sensitivity.