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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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Lutein is a high‐value bioproduct synthesized by microalga Desmodesmus sp. In the current study two aspects of this process are thoroughly investigated: identifying the complex effects of light intensity and nitrate concentration on biomass growth and lutein synthesis, and constructing an accurate kinetic model capable of simulating the entire bioprocess dynamic performance, neither of which has been previously addressed. Three original contributions are presented here. First, it is found that completely opposite to a nitrogen‐limiting culture, under nitrogen‐sufficient conditions a higher lutein content is caused by a higher light intensity and lower nitrate concentration. Second, contrary to lutein content, total lutein production always increases with the increasing nitrate concentration. Third, through experimental verification, the proposed kinetic model is characterized by high accuracy and predictability, indicating its competence for future process design, control, and optimization. Based on the model, optimal light intensities for lutein production and microalgae growth are identified. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2546–2554, 2017

L-tryptophan is an essential amino acid widely used in food and pharmaceutical industries. However, its production via Escherichia coli fermentation suffers severely from both low glucose conversion efficiency and acetic acid inhibition, and to date effective process control methods have rarely been explored to facilitate its industrial scale production. To resolve these challenges, in the current research an engineered strain of E. coli was used to overproduce L-tryptophan. To achieve this, a novel dynamic control strategy which incorporates an optimized anthranilic acid feeding into a dissolved oxygen-stat (DO-stat) glucose feeding framework was proposed for the first time. Three original contributions were observed. Firstly, compared to previous DO control methods, the current strategy was able to inhibit completely the production of acetic acid, and its glucose to L-tryptophan yield reached 0.211 g/g, 62.3% higher than the previously reported. Secondly, a rigorous kinetic model was constructed to simulate the underlying biochemical process and identify the effect of anthranilic acid on both glucose conversion and L-tryptophan synthesis. Finally, a thorough investigation was conducted to testify the capability of both the kinetic model and the novel control strategy for process scale-up. It was found that the model possesses great predictive power, and the presented strategy achieved the highest glucose to L-tryptophan yield (0.224 g/g) ever reported in large scale processes, which approaches the theoretical maximum yield of 0.227 g/g. This research, therefore, paves the way to significantly enhance the profitability of the investigated bioprocess.