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This work proposes an innovative methodology to control high density fed-batch cultures of E. coli, based on measurements of the concentration of dissolved oxygen and on estimations of the cellular specific growth rate (µ), of the yield of biomass/limiting substrate (Y (xs)) and of the maintenance coefficient (m). The underlying idea is to allow cells to grow according to their metabolic capacity, without the constraints inherent to pre-set growth rates. Cellular concentration was assessed on-line through a capacitance probe. Three configurations of the control system were compared: (1) pre-set value for the three control parameters; (2) continuously updating µ; (3) updating µ, Y (xs) and m. Implementation of an efficient noise filter for the signal of the capacitance probe was essential for a good performance of the control system. The third control strategy, within the framework of an adaptive model-based control, led to the best results, with biomass productivity reaching 9.2 g(DCW)/L/h.

-High cell density cultivations of recombinant E. coli have been increasingly used for the production of heterologous proteins. However, it is a challenge to maintain these cultivations within the desired conditions, given that some variables such as dissolved oxygen concentration (DOC) and feed flow rate are difficult to control. This paper describes the software SUPERSYS_HCDC, a tool developed to supervise fed-batch cultures of rE. coli with biomass concentrations up to 150 g DCW /L and cell productivities up to 9 g DCW .L -1 .h -1 . The tool includes automatic control of the DOC by integrated action of the stirrer speed as well as of the air and oxygen flow rates; automatic start-up of the feed flow of fresh medium (system based on a neural network committee); and automatic slowdown of feeding when oxygen consumption exceeds the maximum capacity of the oxygen supply.

Background Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation. Results The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae , defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L −1 h −1 ), with a final ethanol concentration of 87 g L −1 and yield of 0.46 g ethanol g substrate −1 . The other fermentation strategies showed lower yields (close to 0.40 g ethanol g substrate −1 ) and ethanol productivity around 4.0 g L −1 h −1 . Conclusion The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

Streptococcus pneumoniae is the main cause of pneumonia, meningitis, and other conditions that kill thousands of children every year worldwide. The replacement of pneumococcal serotypes among the vaccinated population has evidenced the need for new vaccines with broader coverage and driven the research for protein-based vaccines. Pneumococcal surface protein A (PspA) protects S. pneumoniae from the bactericidal effect of human apolactoferrin and prevents complement deposition. Several studies indicate that PspA is a very promising target for novel vaccine formulations. Here we describe a production and purification process for an untagged recombinant fragment of PspA from clade 4 (PspA4Pro), which has been shown to be cross-reactive with several PspA variants. PspA4Pro was obtained using lactose as inducer in Phytone auto-induction batch or glycerol limited fed-batch in 5-L bioreactor. The purification process includes two novel steps: (i) clarification using a cationic detergent to precipitate contaminant proteins, nucleic acids, and other negatively charged molecules as the lipopolysaccharide, which is the major endotoxin; and (ii) cryoprecipitation that eliminates aggregates and contaminants, which precipitate at -20 °C and pH 4.0, leaving PspA4Pro in the supernatant. The final process consisted of cell rupture in a continuous high-pressure homogenizer, clarification, anion exchange chromatography, cryoprecipitation, and cation exchange chromatography. This process avoided costly tag removal steps and recovered 35.3 ± 2.5% of PspA4Pro with 97.8 ± 0.36% purity and reduced endotoxin concentration by >99.9%. Circular dichroism and lactoferrin binding assay showed that PspA4Pro secondary structure and biological activity were preserved after purification and remained stable in a wide range of temperatures and pH values.

In spite of the large number of reports on fed-batch cultivation of E. coli, alternative cultivation/induction strategies remain to be more deeply exploited. Among these strategies, it could be mentioned the use of complex media with combination of different carbon sources, novel induction procedures and feed flow rate control matching the actual cell growth rate. Here, four different carbon source combinations (glucose, glycerol, glucose + glycerol and auto-induction) in batch media formulation were compared. A balanced combination of glucose and glycerol in a complex medium formulation led to: fast growth in the batch-phase; reduced plasmid instability by preventing early expression leakage; and protein volumetric productivity of 0.40 g.L-1.h-1. Alternative induction strategies were also investigated. A mixture of lactose and glycerol as supplementary medium fully induced a high biomass population, reaching a good balance between specific protein production (0.148 gprot.gDCW-1) and volumetric productivity (0.32 g.L-1.h-1). The auto-induction protocol showed excellent results on specific protein production (0.158 gprot.gDCW-1) in simple batch cultivations. An automated feed control based on the on-line estimated growth rate was implemented, which allowed cells to grow at higher rates than those generally used to avoid metabolic overflow, without leading to acetate accumulation. Some of the protocols described here may provide a useful alternative to standard cultivation and recombinant protein production processes, depending on the performance index that is expected to be optimized. The protocols using glycerol as carbon source and induction by lactose feeding, or glycerol plus glucose in batch medium and induction by lactose pulse led to rSpaA production in the range of 6 g.L-1, in short fed-batch processes (16 to 20 h) with low accumulation of undesired side metabolites.

-The performance of an in-situ capacitance sensor for on-line monitoring of biomass concentration was evaluated for some of the most important microorganisms in the biotechnology industry: Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Bacillus megaterium. A total of 33 batch and fed-batch cultures were carried out in a bench-scale bioreactor and biomass formation trends were followed by dielectric measurements during the growth phase as well as the induction phase, for 5 recombinant E. coli strains. Permittivity measurements and viable cellular concentrations presented a linear correlation for all the studied conditions. In addition, the permittivity signal was further used for inference of the cellular growth rate. The estimated specific growth rates mirrored the main trends of the metabolic states of the different cells and they can be further used for setting-up control strategies in fed-batch cultures.

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.