Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model's validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering.
Two glucose dehydrogenase (E.C. 1.1.1.47) genes, gdh223 and gdh151, were cloned from Bacillus megaterium AS1.223 and AS1.151, and were inserted into pQE30 to construct the expression vectors, pQE30-gdh223 and pQE30-gdh151, respectively. The transformant Escherichia coli M15 with pQE30-gdh223 gave a much higher glucose dehydrogenase activity than that with the plasmid pQE30-gdh151. Thus it was used to optimize the expression of glucose dehydrogenase. An proximately tenfold increase in GDH activity was achieved by the optimization of culture and induction conditions, and the highest productivity of glucose dehydrogenase (58.7 U/ml) was attained. The recombinant glucose dehydrogenase produced by E. coli M15 (pQE30-gdh223) was then used to regenerate NADPH. NADPH was efficiently regenerated in vivo and in vitro when 0.1 M glucose was supplemented concomitantly in the reaction system. Finally, this coenzyme-regenerating system was coupled with a NADPH-dependent bioreduction for efficient synthesis of ethyl (R)-4-chloro-3-hydroxybutanoate from ethyl 4-chloro-3-oxobutanoate.
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