The increasing availability of metabolic network models and data poses new challenges in what concerns optimization. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In the first part of this paper three control optimization methods, Direct Optimization and Bi-level optimization using two different inner-optimization procedures, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network. The results obtained show that the bi-level optimization provides a good approximation to networks with incomplete kinetic information. The process of formulating Metabolic Network models and the estimation of its parameters is complex and there is no defined framework to obtain valid solutions. On the second part of this paper, a procedure to estimate parameters using data sets from different experiments is presented. The procedure is illustrated by a case study on the effect of Nisin on Mannitol production by Lactococcus lactis. The obtained results are encouraging, providing a consistent estimate of the model parameters.

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