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A comparative study on physicochemical hydrolysis methods, including acid-catalyzed, base-catalyzed, and subcritical water hydrolysis, for glycerides removal prior to subsequent γ-oryzanol purification from rice bran acid oil (RBAO), is presented. The effects of hydrolysis conditions on the residual levels of glycerides and γ-oryzanol in RBAO were first investigated for each method. It can be concluded that the acid-catalyzed hydrolysis was not a suitable hydrolysis method for this application as it provided a low glycerides conversion level of ca. 30% using 2 N H2SO4 at 80–100 °C, despite the long reaction time (> 60 min). For the base-catalyzed hydrolysis, 100% glycerides removal was achieved at the most suitable condition, which was suggested to be at 2.5 N NaOH, 90 °C and 5–10 min. At this condition, a relatively high residual level of γ-oryzanol of ca. 57–66% was obtained. For the subcritical water hydrolysis, the results suggested that γ-oryzanol was hydrolyzed more rapidly than glycerides, and the most suitable condition for subcritical water hydrolysis was at 220 °C for 10 min. At this condition, high glycerides removal of > 95% was achieved while the remaining amount of γ-oryzanol was still acceptable (50%).

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.

Cell productivity in fed-batch processes can be increased by cell cycle arrest through mild hypothermia. However, hypothermia can simultaneously reduce cell growth, which is regulated by the cell cycle. Consequently, the time point for the temperature shift is important and requires optimization while considering the cell cycle. An unstructured cell cycle model which includes the distribution of proliferating (G1, S, G2/M) and arrested cells (G0) has been proposed in order to predict the time point of temperature shift in fed-batch cultures. A model development and analysis framework that enables the evaluation of the required model parameters is described. The parameters are estimated from (fed)-batch cultivations, carried out at 37 °C and at 33 °C in order to characterize temperature dependency. The batch cultures are also used to evaluate substrate depletion and fed-batch cultures are used to study the impact of metabolite accumulation on the cell cycle. The reliability of the proposed framework for parameter estimation is validated using a mAb-producing hybridoma cell culture and the model predicts hypothermic transitions within the cell population at different shift time points. In conclusion, this framework can be used to optimize the time point of the temperature shift, which is commonly adjusted in industrial fed-batch processes in order to obtain a good balance between temperature induced growth limitation and cell cycle specific enhanced productivity.

Background Large scale mammalian cell culture systems, especially fed-batch systems, are currently utilised to manufacture monoclonal antibodies (MAbs) in order to meet the continuously growing global demand [1]. Nutrient deprivation and toxic metabolite accumulation commonly encountered in such systems influence the cell cycle and trigger apoptosis, resulting in shorter culture times and a lower final MAb titre. Control of the cell cycle has been previously studied in order to achieve higher titre through apoptosis inhibition by bcl-2 overexpression and cell cycle arrest in G1/G0 by p21 transfection. However, the above mentioned strategies have not always been successful; no improvement in titre was often observed though bcl-2 over-expression helped prolong the culture viability whereby the majority of cells were arrested at G1/G0 to avoid apoptosis [2-4]. Thus, a systematic insight of the dynamic relation between metabolic stress, cell cycle and apoptosis is still required. To this end, we aim to establish a novel map of the dynamic interplay between cell cycle and apoptosis at the genetic level, and provide a link with the culture conditions at the metabolic level.