Abstract:Using tomato-peel extracted cutin, an economically viable substrate, cutinase production by Pseudomonas cepacia was studied at different initial substrate concentrations (2-20 g L -1 ). The highest volumetric enzyme activity was observed at 10 g L -1 of cutin, which was inhibited at further higher concentrations. Various 3-, 4-and 5-parametric Monod-variant models were chosen to analyze the inhibition kinetics. The model parameters as well as goodness of fit were estimated using non-linear regression analysis.… Show more
“…The consumption of substrate plays an important role in both the growth of the cells and product formation, therefore the optimum concentration of substrate aids in the higher productivity of the desired product. In addition, the further application of a mathematical model helps in predicting the effect of environmental perturbation on the process and it provides information that can help maxime the objective function . Kinetic models are improved with an increase in complexity, therefore the Akaike information criterion (AIC) and the extra sum of squares F‐test are used to predict more consistent models from the experimental data …”
In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis).In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN-g production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN-g concentration at 50 g L À1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4-parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R 2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F-test and Akaike information criterion (AIC). The Plackett-Burman multifactorial design identified sorbitol, glycine, Na 2 HPO 4 , and MgSO 4 .7H 2 O as the parameters significantly influencing the hIFN-g production. Further, the Box-Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN-GA) was employed for the precise optimization of medium components. With ANN-GA a maximum hIFN-g yield of 2.1 AE0.3 mg L À1 in shake flask level and 3.5 AE0.1 mg L À1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.
“…The consumption of substrate plays an important role in both the growth of the cells and product formation, therefore the optimum concentration of substrate aids in the higher productivity of the desired product. In addition, the further application of a mathematical model helps in predicting the effect of environmental perturbation on the process and it provides information that can help maxime the objective function . Kinetic models are improved with an increase in complexity, therefore the Akaike information criterion (AIC) and the extra sum of squares F‐test are used to predict more consistent models from the experimental data …”
In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis).In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN-g production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN-g concentration at 50 g L À1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4-parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R 2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F-test and Akaike information criterion (AIC). The Plackett-Burman multifactorial design identified sorbitol, glycine, Na 2 HPO 4 , and MgSO 4 .7H 2 O as the parameters significantly influencing the hIFN-g production. Further, the Box-Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN-GA) was employed for the precise optimization of medium components. With ANN-GA a maximum hIFN-g yield of 2.1 AE0.3 mg L À1 in shake flask level and 3.5 AE0.1 mg L À1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.
“…Agarry and Solomon in 2008 studied the degradation of phenol by the fluorescence of Pseudomonas and observed that the experimental data of μ max corresponded well to the Haldane model. Dutta et al in 2015 by studying the production of cutinase by Pseudomonas cepacia did not conclude on the ideal model. Tazda et al 2013 found that the Yano and Andrews model best showed μ max when biodegradation of malathion.…”
Section: Resultsmentioning
confidence: 98%
“…These curves have a bell-like appearance and have two phases. A phase where the specific growth rates and maximal enzymatic activity increase with the formate concentration (1.59–3.17 mM) and a phase of decline of the specific growth rate and maximum enzymatic activity from 4.76 mM (Dutta et al 2015; Agarry et al 2010; Dey and Mukherjee 2010) proposed that this bell-like appearance at high substrate concentrations reveals inhibition by the substrate.Fig. 6Influence of the initial ammonium concentration on the maximum enzymatic activity for Yarrowia lipolytica and Pichia guilliermondii strains…”
Section: Resultsmentioning
confidence: 99%
“…However, low purifying efficiencies have always been observed, mostly due to variations in the concentration of pollutants (NH 3 ) which become inhibitors even at low concentration (Vadivelu et al 2007). In this case, mathematical modeling is useful for understanding the behavior of biological processes and predicting the concentrations of components in the system (Dutta et al 2015; Tazda et al 2013) to avoid inhibition and increase treatment efficiency. In this study, two strains of yeast ( Yarrowia lipolytica and Pichia guilliermondii ) were isolated from rubber effluents.…”
“…Also, the capability of the used models for simulating VFCW outflow concentrations of NH4 + and (NO2 -+NO3 -) was assessed by drawing the predicted concentrations as a function of observed VFCW outflow concentrations. The precision of the estimated parameters was assessed by the sensitivity analysis used by [39]. It comprised the effect of each estimate on the coefficient of determination, with only one estimate varied at a time, and a -50 to 50% variation range.…”
Section: Model Calibration and Simulationmentioning
This work shows simulation results for subsurface vertical flow constructed wetland (VFCW) using a series CSTR model. The VFCW considered received the outflow from a domestic wastewater treatment plant. In addition, it was planted with Cyperus sp. and filter media was unsaturated. The model was based on an unsteady state mass balance for ammonia, nitrites, and nitrates, using one to three series CSTRs. Nitrogen transformation mechanisms considered were ammonification, nitrification, plant uptake and denitrification. The following effects were evaluated: the number of reacting CSTRs from one to three; the occurrence of the reaction in second and third CSTRs for the case that three CSTRs hold; the use of either equal or different values of reaction rate parameters between CSTRs; and the discretization of the reaction rate parameters. The inflow and outflow measurements of ammonium, nitrites, and nitrates were used for model calibration. The estimated parameters included the reaction rate coefficients and reactor water volume. The coefficient of determination (R 2 ) evidenced a satisfactory capability of simulating outlet pollutant concentrations. Two and three reacting CSTRs achieved similar R 2 value (0.54-0.55), whereas one reacting CSTR achieved an R 2 of 0.39, and three CSTRs with reaction only in the first tank achieved an R 2 of 0.42. Discretization of the nitrification rate for the case of two reacting CSTRs led to an R 2 of 0.94. The parameter sensitivity analysis revealed a significant effect of model parameters on the R 2 value.
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