Impact of Acetic Acid Supplementation in Polyhydroxyalkanoates Production by Cupriavidus necator Using Mixture-Process Design and Artificial Neural Network.

The trend in bioplastic application has increased over the years where polyhydroxyalkanoates (PHAs) have emerged as a potential candidate with the advantage of being bio-origin, biodegradable, and biocompatible. The present study aims to understand the effect of acetic acid concentration (in combination with sucrose) as a mixture variable and its time of addition (process variable) on PHA production by Cupriavidus necator. The addition of acetic acid at a concentration of 1 g l-1 showed a positive influence on biomass and PHA yield; however, the further increase had a reversal effect. The addition of acetic acid at the time of incubation showed a higher PHA yield, whereas maximum biomass was achieved when acetic acid was added after 48 h. Genetic algorithm (GA) optimized artificial neural network (ANN) was used to model PHA concentration from mixture-process design data. Fitness of the GA-ANN model (R2: 0.935) was superior when compared to the polynomial model (R2: 0.301) from mixture design. Optimization of the ANN model projected 2.691 g l-1 PHA from 7.245 g l-1 acetic acid, 12.756 g l-1 sucrose, and the addition of acetic acid at the time of incubation. Sensitivity analysis indicates the inhibitory effect of all the predictors at higher levels. ANN model can be further used to optimize the variables while extending the bioprocess to fed-batch operation.

Dynamic Model Selection and Optimal Batch Design for Polyhydroxyalkanoate (PHA) Production by Cupriavidus necator.

Mathematical modelling of microbial polyhydroxyalkanoates (PHAs) production is essential to develop optimal bioprocess design. Though the use of mathematical models in PHA production has increased over the years, the selection of kinetics and model identification strategies from experimental data remains largely heuristic. In this study, PHA production from Cupriavidus necator utilizing sucrose and urea was modelled using a parametric discretization approach. Product formation kinetics and relevant parameters were established from urea-free experimental sets, followed by the selection of growth models from a batch containing both sucrose and urea. Logistic growth and Luedeking-Piret model for PHA production was selected based on regression coefficient (R2: 0.941), adjusted R2 (0.930) and AICc values (-42.764). Model fitness was further assessed through cross-validation, confidence interval and sensitivity analysis of the parameters. Model-based optimal batch startup policy, incorporating multi-objective desirability, suggests an accumulation of 2.030 g l-1 of PHA at the end of 120 h. The modelling framework applied in this study can be used not only to avoid over-parameterization and identifiability issues but can also be adopted to design optimal batch startup policies.

Inconsistency of PCA-based water quality index - Does it reflect the quality?

The formalization of a stable water quality index (WQI) from measured hydrogeochemical parameters is essential for the identification and classification of water resources. In the principal component analysis (PCA) based WQI approach, the parameter weight is derived using either PC loading or rotated factor loading from a large number of samples pooled for WQI measurement. The PCA-based approach is paradoxical, as the calculated WQI rating of a sample would rather be dependent on the size, and composition of the population. Though this issue is well anticipated, no attempt has been made to regularize or measure the extent of WQI disagreement. In the present study, the WQI of 106 groundwater samples analyzed for 12 different hydrochemical parameters were modelled using PC loading or rotated factor loading (referred to as PCQ-1, PCQ-2, respectively) approach. Analysis reveals PCQ-1 to be positively biased in 78 % of samples and rating disagreements were evident in 9.43 % of samples. WQI of the data set was estimated using repeated (1000) random non-overlapping 2 to 5-fold data partitioning (containing 21 to 83 samples in each fold) adopting either an in-sample (test set) or out-sample (train set) modelling approach. The mean of WQI deviations in repeated resampling from the reference (i.e., using the entire dataset) has been positive in most of the samples using the PCQ-1 model, irrespective of the fold partition size. The median root mean square deviation values of the data set increased with the number of fold partitioning for in-sample calibration for both PCQ-1 and PCQ-2 approaches. The exclusion of a single water quality parameter from the PCA model can cause up to a 60 % deviation of the WQI score in some water samples. The cross-validation and Monte Carlo resampling approach can serve as a framework to test the stability of PCA-based WQI.

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production-A state-of-the-art review.

The rate and extent of microbial polyhydroxyalkanoates (PHAs) production rely on the availability of substrates, growth of microbial biomass, and intracellular accumulation of polymer under nitrogen-limited conditions. The dynamics of PHAs production captured through various structured or unstructured models can be extended to design an optimal feeding strategy for process intensification. Large variability in process assumptions, choices of kinetics, and model complexity is expected depending on substrate(s), microbial metabolism, and discretization of the process under consideration. This communication attempts to review the estimation of stoichiometric yield coefficients, metabolic modelling, and choices of unstructured kinetics in microbial PHA production. Implementational irregularities in parameter estimation and quality check in modelling exercises have also been reviewed. It is observed that the scope of the majority of the "modelling" studies is confined to the estimation of stoichiometric parameters with limited utility. In dynamic models, microbial growth is often described using either Monod or logistic variants, while PHAs production adopts a Luedeking-Piret expression with or without substrate inhibition. Though model selection, regression with experimental data, parameter estimation, and model validation are integral parts of the exercise, very few provide sufficient coverage on all those aspects. Application of the model to control or optimize the bioprocess has rarely been attempted.

Process optimization, metabolic engineering interventions and commercialization of microbial polyhydroxyalkanoates production - A state-of-the art review.

BACKGROUND: Microbial polyhydroxyalkanoates (PHAs) produced using renewable resources could be the best alternative for conventional plastics. Despite their incredible potential, commercial production of PHAs remains very low. Nevertheless, sincere attempts have been made by researchers to improve the yield and economic viability of PHA production by utilizing low-cost agricultural or industrial wastes. In this context, the use of efficient microbial culture or consortia, adoption of experimental design to trace ideal growth conditions, nutritional requirements, and intervention of metabolic engineering tools have gained significant attention. PURPOSE AND SCOPE: This review has been structured to highlight the important microbial sources for PHA production, use of conventional and non-conventional substrates, product optimization using experimental design, metabolic engineering strategies, and global players in the commercialization of PHA in the past two decades. The challenges about PHA recovery and analysis have also been discussed which possess indirect hurdle while expanding the horizon of PHA-based bioplastics. SUMMARY: Selection of appropriate microorganism and substrate plays a vital role in improving the productivity and characteristics of PHAs. Experimental design-based bioprocess, use of metabolic engineering tools, and optimal product recovery techniques are invaluable in this dimension. CONCLUSION: Optimization strategies, which are being explored in isolation, need to be logically integrated for the successful commercialization of microbial PHAs.

Adsorption, Bioavailability and Microbial Toxicity of Diclofenac in Agricultural Soil.

Persistence and environmental implication of pharmaceuticals in agricultural soil is determined depending on adsorption, bioavailability and toxicity. This study aims to assess adsorption/partitioning behaviour of diclofenac (DCF) and its impact on microbial activity in four agricultural soils, differing in pH, organic carbon content, and cation exchange capacity. Results from batch studies suggests that soil/water partition coefficients of DCF are essentially nonlinear, i.e. depends on drug amount (p = 0.001), and positively correlated with soil organic carbon (p = 0.008). The adsorption data can effectively be modelled using Freundlich isotherm (regression coefficients between 0.84 and 0.90). In soil incubation studies, DCF could not be detected after 6 days of spiking (20 µg/g) in all soil types, including abiotic control. This suggests an interplay of combined biotic/abiotic process in DCF removal. Though microbial activity (based on tetrazolium reduction) declined with incubation time, but was not correlated with DCF exposure, particularly in soils rich in organic carbon.