Cintia Regina Sargo

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations

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.

Salmonella typhimurium and Escherichia coli dissimilarity: closely related bacteria with distinct metabolic profiles

Live attenuated strains of Salmonella typhimurium have been extensively investigated as vaccines for a number of infectious diseases. However, there is still little information available concerning aspects of their metabolism. S. typhimurium and Escherichia coli show a high degree of similarity in terms of their genome contents and metabolic networks. However, this work presents experimental evidence showing that significant differences exist in their abilities to direct carbon fluxes to biomass and energy production. It is important to study the metabolism of Salmonella to elucidate the formation of acetate and other metabolites involved in optimizing the production of biomass, essential for the development of recombinant vaccines. The metabolism of Salmonella under aerobic conditions was assessed using continuous cultures performed at dilution rates ranging from 0.1 to 0.67 h−1, with glucose as main substrate. Acetate assimilation and glucose metabolism under anaerobic conditions were also investigated using batch cultures. Chemostat cultivations showed deviation of carbon towards acetate formation, starting at dilution rates above 0.1 h−1. This differed from previous findings for E. coli, where acetate accumulation was only detected at dilution rates exceeding 0.4 h−1, and was due to the lower rate of acetate assimilation by S. typhimurium under aerobic conditions. Under anaerobic conditions, both microorganisms mainly produced ethanol, acetate, and formate. A genome‐scale metabolic model, reconstructed for Salmonella based on an E. coli model, provided a poor description of the mixed fermentation pattern observed during Salmonella cultures, reinforcing the different patterns of carbon utilization exhibited by these closely related bacteria.