Model-Based Iterative Design of Dynamic Experiments in Modeling for a Recombinant Protein Production

MARIANO CRISTALDI; ERNESTO MARTÍNEZ; RICARDO GRAU

Braga, Portugal

Conferencia; CHEMPOR 2008 -10 th International Chemical and Biological Engineering Conference; 2008

European Federation of Chemical Engineering

Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. In this paper, a novel iterative methodology for model-based design of dynamic experiments in modeling for optimization is developed and successfully applied to the optimization of the production of interleukin-11 (IL-11) whose DNA sequence has been cloned in an Escherichia coli strain. At each iteration, the proposed methodology resorts to a library of models to increasingly bias operating conditions by selecting the ‘most informative’ model for designing the next dynamic experiment consisting of an improved operating policy and optimal sampling times. Model selection is done by computing an error measure which distinguishes between parametric and structural uncertainty to selectively bias data gathering towards optimal operation. Parametric uncertainty is iteratively reduced using Global Sensitivity Analysis (GSA) to pinpoint which parameters are key for estimating the objective function. Results obtained after just three iterations are very promising.