Soft-sensor development for monitoring the lysine fermentation process.

Monitoring cell growth and target production in working fermentors is important for stabilizing high level production. In this study, we developed a novel soft sensor for estimating the concentration of a target product (lysine), substrate (sucrose), and bacterial cell in commercially working fermentors using machine learning combined with available on-line process data. The lysine concentration was accurately estimated in both linear and nonlinear models; however, the nonlinear models were also suitable for estimating the concentrations of sucrose and bacterial cells. Data enhancement by time interpolation improved the model prediction accuracy and eliminated unnecessary fluctuations. Furthermore, the soft sensor developed based on the dataset of the same process parameters in multiple fermentor tanks successfully estimated the fermentation behavior of each tank. Machine learning-based soft sensors may represent a novel monitoring system for digital transformation in the field of biotechnological processes.

Data science-based modeling of the lysine fermentation process.

Mathematical modeling of the fermentation process is useful for understanding the influence of operating parameters on target production and control performance, depending on the situation, to stabilize the target production at a high-level. However, the previous approaches using physical modeling methods and traditional knowledge-based methods are difficult to apply on working fermentors at a commercial plant scale because they have unknown and unmeasured parameters involved in target production. This study focused on developing an ensemble learning model that can predict the amino acid fermentation process behavior based on observation values, which can be obtained from fermentation tanks and future control input. The results revealed the influence of each control input on lysine production during the culturing period. Furthermore, high-order stability, which achieved the target trajectory for lysine production, was realized using dynamic fermentation controls. Additionally, this study demonstrates that the fermentation behavior on a commercial plant scale is reproduced using the ensemble device. The ensemble learning model will provide novel control system with data-science based model of Industry 4.0 in the field of biotechnological processes.

Prediction of Rate-Limiting Reactions for Growth-Associated Production Using a Constraint-Based Approach.

Identification of a rate-limiting step in pathways is a key challenge in metabolic engineering. Although the prediction of rate-limiting steps using a kinetic model is a powerful approach, there are several technical hurdles for developing a kinetic model. In this study, an in silico screening algorithm of key enzyme for metabolic engineering is developed to identify the possible rate-limiting reactions for the growth-coupled target production using a stoichiometric model without any experimental data and kinetic parameters. In this method, for each reaction, an upper-bound flux constraint is imposed and the target production is predicted by linear programming. When the constraint decreases the target production at the optimal growth state, the reaction is thought to be a possible rate-limiting step. For validation, this method is applied to the production of succinate or 1,4-butanediol (1,4-BDO) in Escherichia coli, in which the experimental engineering for eliminating rate-limiting steps has been previously reported. In succinate production from glycerol, nine reactions including phosphoenolpyruvate carboxylase are predicted as the rate-limiting steps. In 1,4-BDO production from glucose, eight reactions including pyruvate dehydrogenase are predicted as the rate-limiting steps. These predictions include experimentally identified rate-limiting steps, which would contribute to metabolic engineering as a practical tool for screening candidates of rate-limiting reactions.

Magnesium starvation improves production of malonyl-CoA-derived metabolites in Escherichia coli.

Starvation of essential nutrients, such as nitrogen, sulfur, magnesium, and phosphorus, leads cells into stationary phase and potentially enhances target metabolite production because cells do not consume carbon for the biomass synthesis. The overall metabolic behavior changes depend on the type of nutrient starvation in Escherichia coli. In the present study, we determined the optimum nutrient starvation type for producing malonyl-CoA-derived metabolites such as 3-hydroxypropionic acid (3HP) and naringenin in E. coli. For 3HP production, high production titer (2.3 or 2.0 mM) and high specific production rate (0.14 or 0.28 mmol gCDW-1 h-1) was observed under sulfur or magnesium starvation, whereas almost no 3HP production was detected under nitrogen or phosphorus starvation. Metabolic profiling analysis revealed that the intracellular malonyl-CoA concentration was significantly increased under the 3HP producing conditions. This accumulation should contribute to the 3HP production because malonyl-CoA is a precursor of 3HP. Strong positive correlation (r = 0.95) between intracellular concentrations of ATP and malonyl-CoA indicates that the ATP level is important for malonyl-CoA synthesis due to the ATP requirement by acetyl-CoA carboxylase. For naringenin production, magnesium starvation led to the highest production titer (144 ±â€¯15 µM) and specific productivity (127 ±â€¯21 µmol gCDW-1). These results demonstrated that magnesium starvation is a useful approach to improve the metabolic state of strains engineered for the production of malonyl-CoA derivatives.

Application of adaptive laboratory evolution to overcome a flux limitation in an Escherichia coli production strain.

Gene deletion strategies using flux balance analysis (FBA) have improved the growth-coupled production of various compounds. However, the productivities were often below the expectation because the cells failed to adapt to these genetic perturbations. Here, we demonstrate the productivity of the succinate of the designed gene deletion strain was improved by adaptive laboratory evolution (ALE). Although FBA predicted deletions of adhE-pykAF-gldA-pflB lead to produce succinate from glycerol with a yield of 0.45 C-mol/C-mol, the knockout mutant did not produce only 0.08 C-mol/Cmol, experimentally. After the ALE experiments, the highest succinate yield of an evolved strain reached to the expected value. Genome sequencing analysis revealed all evolved strains possessed novel mutations in ppc of I829S or R849S. In vitro enzymatic assay and metabolic profiling analysis revealed that these mutations desensitizing an allosteric inhibition by L-aspartate and improved the flux through Ppc, while the activity of Ppc in the unevolved strain was tightly regulated by L-aspartate. These result demonstrated that the evolved strains achieved the improvement of succinate production by expanding the flux space of Ppc, realizing the predicted metabolic state by FBA.

Increased 3-hydroxypropionic acid production from glycerol, by modification of central metabolism in Escherichia coli.

BACKGROUND: 3-hydroxypropionic acid (3HP) is an important chemical precursor for the production of bioplastics. Microbial production of 3HP from glycerol has previously been developed through the optimization of culture conditions and the 3HP biosynthesis pathway. In this study, a novel strategy for improving 3HP production in Escherichia coli was investigated by the modification of central metabolism based on a genome-scale metabolic model and experimental validation. RESULTS: Metabolic simulation identified the double knockout of tpiA and zwf as a candidate for improving 3HP production. A 3HP-producing strain was constructed by the expression of glycerol dehydratase and aldehyde dehydrogenase. The double knockout of tpiA and zwf increased the percentage carbon-molar yield (C-mol%) of 3HP on consumed glycerol 4.4-fold (20.1 ± 9.2 C-mol%), compared to the parental strain. Increased extracellular methylglyoxal concentrations in the ΔtpiA Δzwf strain indicated that glycerol catabolism was occurring through the methylglyoxal pathway, which converts dihydroxyacetone phosphate to pyruvate, as predicted by the metabolic model. Since the ΔtpiA Δzwf strain produced abundant 1,3-propanediol as a major byproduct (37.7 ± 13.2 C-mol%), yqhD, which encodes an enzyme involved in the production of 1,3-propanediol, was disrupted in the ΔtpiA Δzwf strain. The 3HP yield of the ΔtpiA Δzwf ΔyqhD strain (33.9 ± 1.2 C-mol%) was increased 1.7-fold further compared to the ΔtpiA Δzwf strain and by 7.4-fold compared to the parental strain. CONCLUSION: This study successfully increased 3HP production by 7.4-fold in the ΔtpiA Δzwf ΔyqhD E. coli strain by the modification of the central metabolism, based on metabolic simulation and experimental validation of engineered strains.