Synthesis, characterization and pharmacological potential of green synthesized copper nanoparticles Abstract The phenomenal and astonishing properties and their different application in the field of pharmaceutical made copper nanoparticles (Cu-NPs) to be in the spotlight of the researcher's focus. In the present study, copper nanoparticles were biologically synthesized with the aqueous extract of the flower Millettia pinnata, and their corresponding characteristics were studied using UV–visible spectroscopy, XRD, FT-IR, SEM, TEM, and SAED analysis. Copper acetate was reduced to copper nanoparticles and is confirmed by UV–visible spectrophotometer analysis. The maximum absorption occurring at 384 nm at the visible spectrum of UV rays confirms the surface plasmon resonance of the nanoparticles. The result of the FTIR spectroscopy analysis of the nanoparticles complements the involvement of organic mioties of the flower extract in the synthesis. The synthesized particles were extremely durable, spherical with the average particle size in the range of 23 ± 1.10 nm. The Cu-NPs exhibited greater inhibition on DPPH radical and nitric oxide scavenging activities. The biologically synthesized Cu-NPs was receptive to the Gram-negative and Gram-positive bacteria as well. The Cu-NPs exhibited strong anti-inflammatory activity using albumin denaturation and membrane stabilization. The present study is the first effort done to synthesize of Cu-NPs from the extract of M. pinnata flower. Consequently, to authenticate the results and to establish the antioxidant, antibacterial, an anti-diabetic and anti-inflammatory agent, in vivo studies are made in the molecular level.

Bioleaching of silicon in electrolytic manganese residue using single and mixed silicate bacteria Abstract Electrolytic manganese residue (EMR) is a type of industrial solid waste with a high silicon content. The silicon in EMR can be used as an essential nutrient for plant growth, but most of the silicon is found in silicate minerals with very low water solubility, that is, it is inactive silicon and cannot be absorbed and used by plants directly. Thus, developing a highly effective and environmentally friendly process for the activation of silicon in EMR is important both for reusing solid waste and environmental sustainability. The aim of this study was to investigate the desilication of EMR using cultures of Paenibacillus mucilaginosus (PM) and Bacillus circulans (BC). The results showed that the two types of silicate bacteria and a mixed strain of them were all able to extract silicon from EMR with a high efficiency, but the desilication performance of the mixed PM and BC was the best. Fourier transform infrared spectroscopy indicated that silicate bacteria can induce a suitable micro-environment near the EMR particles and release Si into the solution through their metabolism. X-ray diffraction analysis confirmed that layered crystal minerals, such as muscovite and diopside, were more likely to be destroyed by the bacterial action than quartz, which has a frame structure. Scanning electron microscopy–energy dispersive spectrometry proved that the silicate structures were destroyed and that Si in the residue was decreased, indicating the dissolution of silicon under the action of these microorganisms. This study suggests that bioleaching may be a promising method for the activation of silicon in EMR. Graphical abstract

FeedER: a feedback-regulated enzyme-based slow-release system for fed-batch cultivation in microtiter plates Abstract With the advent of modern genetic engineering methods, microcultivation systems have become increasingly important tools for accelerated strain phenotyping and bioprocess engineering. While these systems offer sophisticated capabilities to screen batch processes, they lack the ability to realize fed-batch processes, which are used more frequently in industrial bioprocessing. In this study, a novel approach to realize a feedback-regulated enzyme-based slow-release system (FeedER), allowing exponential fed-batch for microscale cultivations, was realized by extending our existing Mini Pilot Plant technology with a customized process control system. By continuously comparing the experimental growth rates with predefined set points, the automated dosage of Amyloglucosidase enzyme for the cleavage of dextrin polymers into d-glucose monomers is triggered. As a prerequisite for stable fed-batch operation, a constant pH is maintained by automated addition of ammonium hydroxide. We show the successful application of FeedER to study fed-batch growth of different industrial model organisms including Corynebacterium glutamicum, Pichia pastoris, and Escherichia coli. Moreover, the comparative analysis of a C. glutamicum GFP producer strain, cultivated under microscale batch and fed-batch conditions, revealed two times higher product yields under slow growing fed-batch operation. In summary, FeedER enables to run 48 parallel fed-batch experiments in an automated and miniaturized manner, and thereby accelerates industrial bioprocess development at the screening stage.

Combined available nitrogen resources enhanced erythromycin production and preliminary exploration of metabolic flux analysis under nitrogen perturbations Abstract In the current study, the effect of different available nitrogen sources on erythromycin fermentation by Saccharopolyspora erythraea No. 8 is evaluated. Three different combinations of corn steep liquor and yeast powder were developed to investigate their impacts on erythromycin production. The results indicate that the optimal combination of available nitrogen sources was 10.0 g/L corn steep liquor and 4.0 g/L yeast power, generating a maximum yield of erythromycin of 13672 U/mL. To explore the effects of nitrogen perturbations on cell metabolism, metabolic flux analyses were performed and compared under different conditions. A high flux pentose phosphate pathway provided more NADPH for erythromycin synthesis via nitrogen optimization. Moreover, high n-propanol specific consumption rate enhanced erythromycin synthesis and n-propanol flowed into the central carbon metabolism by methylmalonyl-CoA node. These results indicate that the selection of an appropriate organic nitrogen source is essential for cell metabolism and erythromycin synthesis, and this is the first report of the successful application of available nitrogen source combinations in industrial erythromycin production.

Modeling of micropollutant removal in full-scale membrane bioreactors: calibration and operations to limit the emissions Abstract Micropollutants are a major concern for aquatic organisms and human health. Membrane bioreactors (MBRs) are an efficient wastewater treatment and water reuse solution, but their micropollutant removal performances are still not fully determined. Modeling micropollutant behavior in MBRs could help better understand and optimize the removal process. Here we provide detailed explanation on a model of micropollutant removal in MBRs that predicts biodegradation and sorption rates. Parameters were calibrated following an iterative two-step procedure developed in this work and using data from two full-scale plants. The calibrated set of parameters was then used (i) to determine the influence of MBR operating conditions such as the duration of aerobic time and the sludge concentration in bioreactor, on micropollutant removal, and (ii) to better understand micropollutant behavior and removal performances in MBRs in response to sudden changes in operating conditions (rain event, F:M ratio). These predictive simulations showed that increasing sludge concentration in bioreactor can decrease effluent concentrations of most of the micropollutants studied by up to 15%, and increasing the duration of aerobic time decreases effluent concentrations of few organic micropollutants tested by up to 15%. Rain events and F:M ratio can increase effluent concentrations of six out of nine micropollutants tested by more than 15%.

Efficient production of polysaccharide by Chaetomium globosum CGMCC 6882 through co-culture with host plant Gynostemma pentaphyllum Abstract Endophytic fungus, as a new kind of microbial resources and separated from plants, has attracted increasing attention due to its ability to synthesize the same or similar bioactive secondary metabolites as the host plants. Nevertheless, the effects of the symbiotic relationship between microorganisms and elicitors existed in host plant on metabolite production are not adequately understood. In the present work, the impacts of elicitors (ginseng saponin and puerarin) and symbiotic microorganisms on endophytic fungus Chaetomium globosum CGMCC 6882 synthesizing polysaccharide were evaluated. Results show that the polysaccharide titers increased from 2.36 to 3.88 g/L and 3.67 g/L with the addition of 16 μg/L ginseng saponin and puerarin, respectively. Moreover, the maximum polysaccharide titer reached 4.55 g/L when C. globosum CGMCC 6882 was co-cultured with UV-irradiated G. pentaphyllum. This work brings a significant contribution to the research and interpretation of the relationship between endophytic fungus and its host plant.

Sensitivity analysis and reduction of a dynamic model of a bioproduction of fructo-oligosaccharides Abstract Starting from a relatively detailed model of a bioprocess producing fructo-oligosaccharides, a set of experimental data collected in batch and fed-batch experiments is exploited to estimate the unknown model parameters. The original model includes the growth of the fungus Aureobasidium pullulans which produces the enzymes responsible for the hydrolysis and transfructosylation reactions, and as such contains 25 kinetic parameters and 16 pseudo-stoichiometric coefficients, which are not uniquely identifiable with the data at hand. The aim of this study is, therefore, to show how sensitivity analysis and quantitative indicators based on the Fisher information matrix can be used to reduce the detailed model to a practically identifiable model. Parametric sensitivity analysis can indeed be used to progressively simplify the model to a representation involving 15 kinetic parameters and 8 pseudo-stoichiometric coefficients. The reduced model provides satisfactory prediction and can be convincingly cross validated.

Substrates’ and products’ inhibition in fructanase production by a new Kluyveromyces marxianus CF15 from Agave tequilana fructan in a batch reactor Abstract This study focuses on fructanase production in a batch reactor by a new strain isolated from agave juice (K. marxianus var. drosophilarum) employing different Agave tequilana fructan (ATF) concentrations as substrate. The experimental data suggest that the fructanase production may be inhibited or repressed by high substrate (50 g/L) and ethanol (20.7 g/L) concentrations present in culture medium. To further analyze these phenomena an unstructured kinetic mathematical model taking into account substrate and products inhibition was proposed and fitted. The mathematical model considers six reaction kinetics and the ethanol evaporation, and predicts satisfactorily the biomass, fructan, glucose, fructose, ethanol, and fructanase behavior for different raw material initial concentrations. The proposed model is the first to satisfactorily describe the production of fructanase from branched ATF with a new strain of K. marxianus.

Model-based control for a demand-driven biogas production to cover residual load rises Abstract The development of systems for energy storage and demand-driven energy production will be essential to enable the switch from fossil to renewable energy sources in future. To cover the residual load rises, a rigorous dynamic process model based on the Anaerobic Digestion Model No. 1 (ADM1) was applied to analyse the flexible operation of biogas plants. For this, the model was optimised and an operational concept for a demand-driven energy production was worked out. Different substrates were analysed, both by batch fermentation and Weende analysis with van Soest method, to determine the input data of the model. The lab results show that the substrates have got different degradation kinetics and biogas potentials. Finally, the ADM1 was extended with a feeding algorithm which is based on a PI controller. Essential feeding times and quantities of available substrates were calculated so that a biogas plant can cover a defined energy demand. The results prove that a flexible operation of biogas plants with a feeding strategy is possible.

A bootstrap-aggregated hybrid semi-parametric modeling framework for bioprocess development Abstract Hybrid semi-parametric modeling, combining mechanistic and machine-learning methods, has proven to be a powerful method for process development. This paper proposes bootstrap aggregation to increase the predictive power of hybrid semi-parametric models when the process data are obtained by statistical design of experiments. A fed-batch Escherichia coli optimization problem is addressed, in which three factors (biomass growth setpoint, temperature, and biomass concentration at induction) were designed statistically to identify optimal cell growth and recombinant protein expression conditions. Synthetic data sets were generated applying three distinct design methods, namely, Box–Behnken, central composite, and Doehlert design. Bootstrap-aggregated hybrid models were developed for the three designs and compared against the respective non-aggregated versions. It is shown that bootstrap aggregation significantly decreases the prediction mean squared error of new batch experiments for all three designs. The number of (best) models to aggregate is a key calibration parameter that needs to be fine-tuned in each problem. The Doehlert design was slightly better than the other designs in the identification of the process optimum. Finally, the availability of several predictions allowed computing error bounds for the different parts of the model, which provides an additional insight into the variation of predictions within the model components.