Abstract
A new method (named a “jumping spider”) is introduced for
the optimization of slow biotechnological processes. The more
traditional sequential experimentation (i.e., gradient search, simplex,
etc.) is not well suited for slow dynamic processes, e.g., plant cell
culture and differentiation. Therefore, a more simultaneous approach is
proposed. A large number of initial experiments are performed, on the
basis of which several of the initial experiments are selected as
starting points. A search is then performed simultaneously from several
gradient directions and the optimum is estimated by a quadratic
approximation. In simulations, the spider generally climbs up the slopes
quickly and the final estimator yields good maximum point estimates
even on a complex topography. The spider may even approach more than one
local maximum point simultaneously. As a model application, the average
xylitol conversion rate of Candida guilliermondii was optimized
in relation to cultivation volume (oxygen availability) and the
concentration of nitrogen and phosphorus in the medium. A threefold
increase in xylitol production was obtained with three experimental
steps.
Original language | English |
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Pages (from-to) | 1301 - 1310 |
Number of pages | 10 |
Journal | Biotechnology and Bioengineering |
Volume | 42 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1993 |
MoE publication type | A1 Journal article-refereed |