Data and knowledge based experimental design for fermentation process optimization

Model-based Characterization of the Parameters of Dissimilatory Sulfate Reduction Under the Effect of Different Initial Density of Desulfovibrio piger Vib-7 Bacterial Cells

The objective of this study was to design a model of dissimilatory sulfate reduction process using the Verhulst function, with a particular focus on the kinetics of bacterial growth, sulfate and lactate consumption, and accumulation of hydrogen sulfide and acetate. The effect of the initial density (0.12±0.011, 0.25±0.024, 0.5±0.048 and 1.0±0.096 mg cells/ml of medium) of the sulfate-reducing bacteria Desulfovibrio piger Vib-7 on the growth and dissimilatory sulfate reduction was studied. The exponential growth phase of the D. piger Vib-7 was observed for 72 hours of cultivation at the (0.12 and 0.25 mg/ml) initial concentration of bacterial cells. Sulfate and lactate were consumed incompletely during this time. The increase in the initial concentration of cells to 0.5 and 1 mg/ml led to a shortening of the exponential bacterial growth phase and a shift to the stationary phase of the growth. In the case of 0.5 mg/ml seeding, the stationary growth phase was observed in the 36(th) hour of cultivation. The increase in the initial concentration of cells to 1 mg/ml led to the beginning of the stationary growth phase in 24th hours of cultivation. Under these conditions, sulfate and lactate were consumed completely in the 48th hour of cultivation. The kinetic analysis of the curves of bacterial growth and the process of dissimilatory sulfate reduction by D. piger Vib-7 was carried out.

Developing a scalable model of recombinant protein yield from Pichia pastoris: the influence of culture conditions, biomass and induction regime

Background

The optimisation and scale-up of process conditions leading to high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences. Typical experiments rely on varying selected parameters through repeated rounds of trial-and-error optimisation. To rationalise this, several groups have recently adopted the 'design of experiments' (DoE) approach frequently used in industry. Studies have focused on parameters such as medium composition, nutrient feed rates and induction of expression in shake flasks or bioreactors, as well as oxygen transfer rates in micro-well plates. In this study we wanted to generate a predictive model that described small-scale screens and to test its scalability to bioreactors.

Results

Here we demonstrate how the use of a DoE approach in a multi-well mini-bioreactor permitted the rapid establishment of high yielding production phase conditions that could be transferred to a 7 L bioreactor. Using green fluorescent protein secreted from Pichia pastoris, we derived a predictive model of protein yield as a function of the three most commonly-varied process parameters: temperature, pH and the percentage of dissolved oxygen in the culture medium. Importantly, when yield was normalised to culture volume and density, the model was scalable from mL to L working volumes. By increasing pre-induction biomass accumulation, model-predicted yields were further improved. Yield improvement was most significant, however, on varying the fed-batch induction regime to minimise methanol accumulation so that the productivity of the culture increased throughout the whole induction period. These findings suggest the importance of matching the rate of protein production with the host metabolism.

Conclusion

We demonstrate how a rational, stepwise approach to recombinant protein production screens can reduce process development time.

Experimental design for optimal parameter estimation of an enzyme kinetic process based on the analysis of the Fisher information matrix

The investigation of enzyme kinetics is increasingly important, especially for finding active substances and understanding intracellular behaviors. Therefore, the determination of an enzyme's kinetic parameters is crucial. For this a systematic experimental design procedure is necessary to avoid wasting time and resources. The parameter estimation error of a Michaelis-Menten enzyme kinetic process is analysed analytically to reduce the search area as well as numerically to specify the optimum for parameter estimation. From analytical analysis of the Fisher information matrix the fact is obtained, that an enzyme feed will not improve the estimation process, but substrate feeding is favorable with small volume flow. Unconstrained and constrained process conditions are considered. If substrate fed-batch process design is used instead of pure batch experiments the improvements of the Cramer-Rao lower bound of the variance of parameter estimation error reduces to 82% for mu(max) and to 60% for K(m) of the batch values in average.

Optimization and scale up of industrial fermentation processes

To increase product yields and to ensure consistent product quality, key issues of industrial fermentations, process optimization and scale up are aimed at maintaining optimum and homogenous reaction conditions minimizing microbial stress exposure and enhancing metabolic accuracy. For each individual product, process and facility, suitable strategies have to be elaborated by a comprehensive and detailed process characterization, identification of the most relevant process parameters influencing product yield and quality and their establishment as scale-up parameters to be kept constant as far as possible. Physical variables, which can only be restrictedly kept constant as single parameters, may be combined with other pertinent parameters to appropriate mathematical groups or dimensionless terms. Process characterization is preferably based on real-time or near real-time data collected by in situ and on-line measurements and may be facilitated by supportive approaches and tools like neural network based chemometric data analysis and modelling, clarification of the mixing and stream conditions through computational fluid dynamics and scale-down simulations. However, as fermentation facilities usually are not strictly designed according to scale-up criteria and the process conditions in the culture vessels thus may differ significantly and since any strategy and model can only insufficiently consider and reflect the highly complex interdependence and mutual interaction of fermentation parameters, successful scale up in most cases is not the result of a conclusive and straight-lined experimental strategy, but rather will be the outcome of a separate process development and optimization on each scale. This article gives an overview on the problems typically coming along with fermentation process optimization and scale up, and presents currently applied scale-up strategies while considering future technologies, with emphasis on Escherichia coli as one of the most commonly fermented organisms.

Potential of fermentation profiling via rapid measurement of amino acid metabolism by liquid chromatography-tandem mass spectrometry

Monitoring amino acid metabolism during fermentation has significant potential from the standpoint of strain selection, optimizing growth and production in host strains, and profiling microbial metabolism and growth state. A method has been developed based on rapid quantification of underivatized amino acids using liquid chromatography-electrospray tandem mass spectrometry (LC-MS-MS) to monitor the metabolism of 20 amino acids during microbial fermentation. The use of a teicoplanin-based chiral stationary phase coupled with electrospray tandem mass spectrometry allows complete amino acid analyses in less than 4 min. Quantification is accomplished using five isotopically labeled amino acids as internal standards. Because comprehensive chromatographic separation and derivatization are not required, analysis time is significantly less than traditional reversed- or normal-phase LC-based amino acid assays. Intra-sample precisions for amino acid measurements in fermentation supernatants using this method average 4.9% (R.S.D.). Inter-day (inter-fermentation) precisions for individual amino acid measurements range from 4.2 to 129% (R.S.D.). Calibration curves are linear over the range 0-300 microg/ml, and detection limits are estimated at 50-450 ng/ml. Data visualization techniques for constructing semi-quantitative fermentation profiles of nitrogen source utilization have also been developed and implemented, and demonstrate that amino acid profiles generally correlate with observed growth profiles. Further, cellular growth events, such as lag-time and cell lysis can be detected using this methodology. Correlation coefficients for the time profiles of each amino acid measured illustrate that while several amino acids are differentially metabolized in similar fermentations, a select group of amino acids display strong correlations in these samples, indicating a sub-population of analytes that may be most useful for fermentation profiling.