Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

dy/dµ max
1000
0
−1000
−2000
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0 5 10 15 20
time [h]
2.5. Experimental Design
Figure 2.1.: Jacobian calculated in two ways. Parameter sensitivities are shown for
the concentrations of biomass [OD600h], substrate [gh/L] and product [mMh] depending
on the maximal specific growth rate (µmax). Lines are calculated using explicit simulation
of the sensitivities in the model and the markers by re-simulation with changed
parameter values. Solid lines and × show the sensitivities of the simulated biomass concentrations,
dotted lines and ∗ of substrate concentrations and dashed lines and + of
product concentrations.
2.5. Experimental Design
Every experiment takes time, money and other resources. Therefore it is very important
to limit the number of experiments and get much relevant information from each
experiment. Experimental design stands for techniques which help to plan informative
experiments. Many different approaches have been developed, depending for instance
on the type of information needed and the kind of information available.
The experimental designs can first be divided in two groups which will be called
’statistical experimental design’ and ’model based experimental design’ here. The term
’model based experimental designs’ is used here for designs which use predictions of a
mathematical model in order to determine how an experiment should be performed,
whereas with ’statistical experimental designs’, techniques are meant, which do not
explicitly need these model predictions.
In the current work, model based designs are investigated and used and some of this
type of designs will be discussed in more detail in the following paragraphs.
Examples of statistical experimental designs are for example factorial designs such
as 2-level full factorial designs, where two values are chosen for each variable and all
combinations of these variable values are used. Many different statistical designs have
been used, depending for instance on the type of effects, influential factors and combi-
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