Single Mathematical Parameter for Evaluation of the Microorganisms' Growth as the Objective Function in the Optimization by the DOE Techniques

Growth kinetics of Lactococcus lactis and Lactobacillus casei in liquid culture medium containing as prebiotics inulin or fructose

BACKGROUND

Predictive microbiology is a tool that allows us to evaluate the behavior of the concentration of biomass and estimated cells under extrinsic conditions, providing scientific and industrial benefits. In the present study, the growth of L. lactis and L. casei combined with inulin and fructose was modeled using the Gompertz sigmoidal growth functions and plotted using data obtained from batch culture in relation to biomass and cell concentration expressed as estimates in ln N (OD600nm and cells mL-1 ) as a function of time.

RESULTS

The results of the kinetic modeling indicated that (T1) A1B1 = L. lactis + fructose and (T4) A2B2 = L. casei + inulin presented the best function coefficients and best fits in most cases compared to the rest. The specific growth rate of the maximum acceleration was from 0.364 to 0.473 h-1 and 0.100 to 0.129 h-1 , the concentration of bacterial cells (A) was from 0.556 to 0.713 and 0.425 to 0.548 respectively and the time where (μ) occurred with a greater magnitude (L) fluctuated between 0.854 and 0.802 and when this time in (L) is very fast, it presents values of ≤0.072 to ≤0.092. Its coefficient of determination and/or multiple regression (R2 ) obtained in the two adjustments was 0.97.

CONCLUSION

It was possible to predict the influence of the carbon source on the behavior of maximum growth rates, higher consumption due to nutrient affinity and shorter growth time. © 2023 Society of Chemical Industry.

The Influence of Hydrodynamic Conditions in a Laboratory-Scale Bioreactor on Pseudomonas aeruginosa Metabolite Production

Hydrodynamic conditions are critical in bioprocessing because they influence oxygen availability for cultured cells. Processes in typical laboratory bioreactors need optimization of these conditions using mixing and aeration control to obtain high production of the desired bioproduct. It could be done by experiments supported by computational fluid dynamics (CFD) modeling. In this work, we characterized parameters such as mixing time, power consumption and mass transfer in a 2 L bioreactor. Based on the obtained results, we chose a set of nine process parameters to test the hydrodynamic impact on a selected bioprocess (mixing in the range of 0-160 rpm and aeration in the range of 0-250 ccm). Therefore, we conducted experiments with P. aeruginosa culture and assessed how various hydrodynamic conditions influenced biomass, pyocyanin and rhamnolipid production. We found that a relatively high mass transfer of oxygen (k_La = 0.0013 s^-1) connected with intensive mixing (160 rpm) leads to the highest output of pyocyanin production. In contrast, rhamnolipid production reached maximal efficiency under moderate oxygen mass transfer (k_La = 0.0005 s^-1) and less intense mixing (in the range of 0-60 rpm). The results indicate that manipulating hydrodynamics inside the bioreactor allows control of the process and may lead to a change in the metabolites produced by bacterial cells.

Basic physiology of Pseudomonas aeruginosa contacted with carbon nanocomposites

Experiments describing properties of nanomaterials on bacteria are frequently limited to the disk diffusion method or other end-point methods indicating viability or survival rate in plate count assay. Such experimental design does not show the dynamic changes in bacterial physiology, mainly when performed on reference microorganisms (Escherichia coli and Staphylococcus aureus). Testing other microorganisms, such as Pseudomonas aeruginosa, could provide novel insights into the microbial response to nanomaterials. Therefore, we aimed to test selected carbon nanomaterials and their components in a series of experiments describing the basic physiology of P. aeruginosa. Concentrations ranging from 15.625 to 1000 µg/mL were tested. The optical density of cultures, pigment production, respiration, growth curve analysis, and biofilming were tested. The results confirmed variability in the response of P. aeruginosa to tested nanostructures, depending on their concentration. The co-incubation with the nanostructures (in concentration 125 µg/mL) could inhibit the population growth (in most cases) or promote it in the case of graphene oxide. Furthermore, a specific concentration of a given nanomaterial could cause contradictory effects leading to stimulation or inhibition of pigmentation, an optical density of the cultures, or biofilm formation. We have found that particularly nanomaterials containing TiO2 could induce pigmentation in P. aeruginosa, which indicates the possibility of increased virulence. On the other hand, nanocomposites containing cobalt nanoparticles had the highest anti-bacterial potential when cobalt was displayed on the surface. Our approach revealed changes in respiration and growth dynamics that can be used to search for nanomaterials’ application in biotechnology.