Method Development for Purification of γ-oryzanol from Hydrolyzed Rice Bran Acid Oil by Semi-preparative Chromatography.

The objective of this study was to develop a method for isolation and purification of γ-oryzanol from hydrolyzed rice bran acid oil (RBAO) using semi-preparative chromatography by first applying silica coated-thin layer chromatography (TLC) to determine the suitable mobile phase. Subsequently, column chromatography was carried out to determine the effects of purification conditions such as the amount of and particle sizes of the sample silica gel, and elution modes, on the percentage of γ-oryzanol yield and recovery. The results from the TLC suggested that 75:25 (v/v) hexane to ethyl acetate mixture was a suitable mobile phase. The semi-chromatographic results indicated that the column containing 10 g of 25-40 µm silica gel with isocratic elution gave the highest yield (84%) of purified γ-oryzanol (> 95% purity). Further application of a step-gradient elution with 85:15 (v/v), followed by 75:25 (v/v) hexane to ethyl acetate mixture increased chromatographic resolution (Rs), resulting in enhanced separation efficiency, which in turn led to a higher yield of purified γ-oryzanol of 90%.

Optimal bioprocess design through a gene regulatory network - Growth kinetic hybrid model: Towards replacing Monod kinetics.

Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, mixed-substrate utilisation is predominantly ignored despite its significance in enhancing bioprocess performance. Herein, bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, was achieved through application of a novel experimental-modelling gene regulatory network - growth kinetic (GRN-GK) hybrid framework. The GRN model described the TOL and ortho-cleavage pathways in Pseudomonas putida mt-2 and captured the transcriptional kinetics expression patterns of the promoters. The GRN model informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework's predictive capability and potential as a systematic optimal bioprocess design tool, was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of TOL Pr promoter expression resulting in 61% and 60% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides strong evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.