Hop is an important source of secondary metabolites, such as flavonoids. Some of these are pharmacologically active. Nevertheless, the concentration of some classes as flavonoids in wild-type plants is rather low. To enhance the production in hop, it would be interesting to modify the regulation of genes in the flavonoid biosynthetic pathway. For this purpose, the regulatory factor PAP1/AtMYB75 from Arabidopsis thaliana L. was introduced into hop plants cv. Tettnanger by Agrobacterium-mediated genetic transformation. Twenty kanamycin-resistant transgenic plants were obtained. It was shown that PAP1/AtMYB75 was stably incorporated and expressed in the hop genome. In comparison to the wild-type plants, the color of female flowers and cones of transgenic plants was reddish to pink. Chemical analysis revealed higher levels of anthocyanins, rutin, isoquercitin, kaempferol-glucoside, kaempferol-glucoside-malonate, desmethylxanthohumol, xanthohumol, α-acids and β-acids in transgenic plants compared to wild-type plants.
In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional-integral-derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.
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