Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

2. Theory
designed using a parameter estimation criterion (Chen and Asprey, 2003; Froment, 1975;
Montepiedra and Yeh, 1998).
However, also some suggestions were made to use one criterion for the dual problem
(Fedorov and Khabarov, 1986; Ford et al., 1989; Froment, 1975). Although they will not
be explained in detail here, some are mentioned shortly. For instance, Hill et al. (1968)
used a weighted combination of both criteria. Borth (1975) later published an entropy
criterion adapted from the model discriminating criterion from Box and Hill. Spezzaferri
(1988) used a multiplicative combination of two criteria and Pronzato (2000) suggested
the use of a penalty function for poor parameter estimation in a model discriminating
design criterion.
2.6. Optimization of Bioprocesses
Bioprocesses are optimized at different stages: strain development, development of
the production process and optimization of the industrial plant, which are, however,
closely interconnected (Roubos, 2002). Optimal process conditions, for instance, depend
strongly on the used strain.
Using modern genetic techniques, strain improvement and improvement of process
conditions and the medium are more and more parallel processes. In the current work,
however, the used strain, the type of bioreactor and to a large extent also the used media
are assumed to be selected already. Nevertheless, suggestions for strain improvement
may very well come from identification of limitations during improvement of the process
conditions or the medium using the techniques presented in the current work.
In order to optimize a process, first a performance function is needed. In industrial
processes, this can be a rather complex function depending on many interconnected
steps in the process (Yuan et al., 1997). Optimization of the biological reactions is
usually of high importance. The overall yield of product on substrate, the total volumetric
productivity, the purity of the product or the quality of a complex product
are some of the common criteria to be optimized with respect to the biological process
(Heinzle and Saner, 1991).
Two strategies of process optimization can be distinguished: model based optimization
and empirical optimization. In the latter case, search techniques such as simplex
methods, gradient searches, or genetic algorithms can be used with sequential experiments
in order to improve the process. For instance, WeusterBotz et al. (1997) have
used a genetic algorithm to optimize the trace element composition for cultivation of
a Corynebacterium glutamicum strain. The review by Schügerl (2001) contains other
examples of the use of empirical optimization strategies for optimization of bioprocesses.
For more complex processes with many control parameters, as is usually the case for
optimization of trajectories in fed-batch processes, such search techniques may prove
to get very laborious and converge slowly. When a proper process model is available,
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