Physiological description of multivariate interdependencies between process parameters,morphology and physiology during fed‐batch penicillin production

Optimization of productivity and economics of industrial bioprocesses requires characterization of interdependencies between process parameters and process performance. In the case of penicillin production, as in other processes, process performance is often closely interlinked with the physiology and morphology of the organism used for production. This study presents a systematic approach to efficiently characterize the physiological effects of multivariate interdependencies between bioprocess design parameters (spore inoculum concentration, pO₂ control level and substrate feed rate), morphology, and physiology. Method development and application was performed using the industrial model process of penicillin production. Applying traditional, statistical bioprocess analysis, multivariate correlations of raw bioprocess design parameters (high spore inoculum concentration, low pO₂ control as well as reduced glucose feeding) and pellet morphology were identified. A major drawback of raw design parameter correlation models; however, is the lack of transferability across different process scales and regimes. In this context, morphological and physiological bioprocess modeling based on scalable physiological parameters is introduced. In this study, raw parameter effects on pellet morphology were efficiently summarized by the physiological parameter of the biomass yield per substrate. Finally, for the first time to our knowledge, the specific growth rate per spore was described as time‐independent determinant for switching from pellet to disperse growth during penicillin production and thus introduced as a novel, scalable key process parameter for pellet morphology and process performance. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:689–699, 2014     

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.     

Impact of balanced substrate flux on the metabolic process employing fuzzy logic during the cultivation of <Emphasis Type="Italic">Bacillus thuringiensis</Emphasis> var.<Emphasis Type="Italic"> Galleriae</Emphasis>

Summary The insecticidal crystal protein (ICP) synthesized at the onset of sporulation by Bacillus thuringiensis var. galleriae (Btg) is lethal against specific pests. Attempts were made to enhance the synthesis of biomass and ICP by Btg employing process optimization strategies. The process optimization was carried out with residual glucose concentration control in a bench scale bioreactor. A fuzzy logic-based feedback control system for maintaining the residual glucose concentration at a constant level during cultivation was developed in LabVIEW. This control system indicated the possibilities in providing a balanced substrate flux during cultivation. The identified optimum level of 2.72 g/l in residual glucose concentration was maintained by fed-batch cultivation with glucose and yeast extract fed at equal concentration with the above control system. High cell density of 16.0 g/l with specific growth rate of 0.69 h^-1 was obtained during the cultivation. The balanced flux of substrate during cultivation has resulted in the enhanced synthesis of biomass and ICP. This optimized process could be commercially exploited by comparing the fluxes of basal compounds in different media sources used in fermentation.     

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSE_cvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSE_Prel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.     

Estimation of biomass concentrations in fermentation processes for recombinant protein production

Online biomass estimation for bioprocess supervision and control purposes is addressed. As the biomass concentration cannot be measured online during the production to sufficient accuracy, indirect measurement techniques are required. Here we compare several possibilities for the concrete case of recombinant protein production with genetically modified Escherichia coli bacteria and perform a ranking. At normal process operation, the best estimates can be obtained with artificial neural networks (ANNs). When they cannot be employed, statistical correlation techniques can be used such as multivariate regression techniques. Simple model-based techniques, e.g., those based on the Luedeking/Piret-type are not as accurate as the ANN approach; however, they are very robust. Techniques based on principal component analysis can be used to recognize abnormal cultivation behavior. For the cases investigated, a complete ranking list of the methods is given in terms of the root-mean-square error of the estimates. All techniques examined are in line with the recommendations expressed in the process analytical technology (PAT)-initiative of the FDA.     

On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation

In situ near-infrared (NIR) spectroscopy and in-line electronic nose (EN) mapping were used to monitor and control a cholera-toxin producing Vibrio cholerae fed-batch cultivation carried out with a laboratory method as well as with a production method. Prediction models for biomass, glucose and acetate using NIR spectroscopy were developed based on spectral identification and partial-least squares (PLS) regression resulting in high correlation to reference data (standard errors of prediction for biomass, glucose and acetate were 0.20 gl(-1), 0.26 gl(-1) and 0.28 gl(-1)). A compensation algorithm for aerated bioreactor disturbances was integrated in the model computation, which in particular improved the prediction by the biomass model. First, the NIR data were applied together with EN in-line data selected by principal component analysis (PCA) for generating a trajectory representation of the fed-batch cultivation. A correlation between the culture progression and EN signals was demonstrated, which proved to be beneficial in monitoring the culture quality. It was shown that a deviation from a normal cultivation behavior could easily be recognized and that the trajectory was able to alarm a bacterial contamination. Second, the NIR data indicated the potential of predicting the concentration of formed cholera toxin with a model prediction error of 0.020 gl(-1). Third, the on-line biomass prediction based on the NIR model was used to control the overflow metabolism acetate formation of the V. cholerae culture. The controller compared actual specific growth rate as estimated from the prediction with the critical acetate formation growth rate, and from that difference adjusted the glucose feed rate.     

Enhanced production of L-(+)-lactic acid in chemostat by Lactobacillus casei DSM 20011 using ion-exchange resins and cross-flow filtration in a fully automated pilot plant controlled via NIR

Due to the lack of suitable in-process sensors, on-line monitoring of fermentation processes is restricted almost exclusively to the measurement of physical parameters only indirectly related to key process variables, i.e., substrate, product, and biomass concentration. This obstacle can be overcome by near infrared (NIR) spectroscopy, which allows not only real-time process monitoring, but also automated process control, provided that NIR-generated information is fed to a suitable computerized bioreactor control system. Once the relevant calibrations have been obtained, substrate, biomass and product concentration can be evaluated on-line and used by the bioreactor control system to manage the fermentation. In this work, an NIR-based control system allowed the full automation of a small-scale pilot plant for lactic acid production and provided an excellent tool for process optimization. The growth-inhibiting effect of lactic acid present in the culture broth is enhanced when the growth-limiting substrate, glucose, is also present at relatively high concentrations. Both combined factors can result in a severe reduction of the performance of the lactate production process. A dedicated software enabling on-line NIR data acquisition and reduction, and automated process management through feed addition, culture removal and/or product recovery by microfiltration was developed in order to allow the implementation of continuous fermentation processes with recycling of culture medium and cell recycling. Both operation modes were tested at different dilution rates and the respective cultivation parameters observed were compared with those obtained in a conventional continuous fermentation. Steady states were obtained in both modes with high performance on lactate production. The highest lactate volumetric productivity, 138 g L(-1) h(-1), was obtained in continuous fermentation with cell recycling.     

Biomass and lipid production of heterotrophic microalgae <Emphasis Type="Italic">Chlorella protothecoides</Emphasis> by using biodiesel-derived crude glycerol

Microalgal lipids may be a more sustainable biodiesel feedstock than crop oils. We have investigated the potential for using the crude glycerol as a carbon substrate. In batch mode, the biomass and lipid concentration of Chlorella protothecoides cultivated in a crude glycerol medium were, respectively, 23.5 and 14.6 g/l in a 6-day cultivation. In the fed-batch mode, the biomass and lipid concentration improved to 45.2 and 24.6 g/l after 8.2 days of cultivation, respectively. The maximum lipid productivity of 3 g/l day in the fed-batch mode was higher than that produced by batch cultivation. This work demonstrates the feasibility of crude biodiesel glycerol as an alternative carbon substrate to glucose for microalgal cultivation and a cost reduction of carbon substrate feed in microalgal lipid production may be expected.     

Disturbance-accommodating control approach applied for the control of a continuous stirred tank reactor

This paper describes an experimental study of linear adaptive control to achieve the monitoring of a continuous stirred tank reactor. The practical control objective was the regulation of the substrate concentration at a pre-specified value in the process effluent despite local changes and/or culture physiology variations. The substrate concentration and the dilution rate have been selected as the controlled and the control variable respectively. The results obtained confirm that this approach offers the possibility to combine simplicity and effectiveness in bioprocess control.     

Energetics of growth of <Emphasis Type="Italic">Aspergillus tamarii</Emphasis> in a biological real-time reaction calorimeter

Fungal cultivation in a biological real-time reaction calorimeter (BioRTCal) is arduous due to the heterogeneous nature of the system and difficulty in optimizing the process variables. The aim of this investigation is to monitor the growth of fungi Aspergillus tamarii MTCC 5152 in a calorimeter. Experiments carried out with a spore concentration of 10⁵ spores/mL indicate that the growth based on biomass and heat generation profiles was comparable to those obtained hitherto. Heat yield due to biomass growth, substrate uptake, and oxygen uptake rate was estimated from calorimetric experiments. The results would be useful in fermenter design and scale-up. Heat of combustion of fungal biomass was determined experimentally and compared to the four models reported so far. The substrate concentration had significant effects on pellet formation with variation in pellet porosity and apparent density. Metabolic heat generation is an online process variable portraying the instantaneous activity of monitoring fungal growth and BioRTCal is employed to measure the exothermic heat in a noninvasive way.     

Modeling of growth and sporulation of <Emphasis Type="Italic">Bacillus thuringiensis</Emphasis> in an intermittent fed batch culture with total cell retention

An extended dynamical model for growth and sporulation of Bacillus thuringiensis subsp. kurstaki in an intermittent fed-batch culture with total cell retention is proposed. This model differs from reported models, by including dynamics for natural death of cells and substrate consumption for cell maintenance. The proposed model uses sigmoid functions to describe these kinetic parameters. Equations for time evolution of substrate, vegetative, sporulated and total cell concentration were taken from previous works. Model parameters were determined from batch experimental data obtained in pilot plant. Parameter identification was developed in two stages: (1) coarse identification using a multivariable optimization with constraints algorithm, (2) fine identification by heuristic fit of model parameters looking for a minimal model error. The proposed model estimates adequate time evolution of the process variables with a mean error of 2.6% on substrate concentration and 6.7% on biomass concentration.     

Control of ��-amylase production by Bacillus subtilis

This study proposes two adaptive control algorithms for the fed-batch production of α-amylase. The first one uses online information from hardware measuring glucose. Online information of both biomass and glucose concentrations measured with different frequency is used in the second algorithm. Hardware measuring variables are inputs for software sensors of glucose concentration and (specific) glucose consumption rate. Either of the algorithms do not require any kinetic coefficients. This is a benefit, because the kinetic coefficients can vary during cultivation and between cultivations, leading to low process reproducibility and the non-stationary state of the bioprocess. The results of simulation investigations show good performance of the proposed control schemes.     

Extended fed-batch-cultivation of exponential growing microorganisms — an analysis for systems with and without outflow

The method of extended cultivation was studied mathematical-analytical by a simple mathematical model. It was reflected on the control algorithm of feeding rate. Because corresponding to the model the substrate is consumed only for the biomass growth, the feeding is to control proportional to the biomass variation. It is shown that the biomass concentration in this case is independent of outflow and approaches a limit value. Relations between biomass growth and feeding rate of limiting substrate are given for all cases. In particular for the cyclie fed batch, it is shown that assuming periodic cycles the cultivation volume at the end of the cycles approaches a maximum value defined only by initial volume, specific growth rate and cycle period.     

Controlled pilot development unit-scale fed-batch cultivation of yeast on spruce hydrolysates

Yeast production on hydrolysate is a likely process solution in large-scale ethanol production from lignocellulose. The hydrolysate will be available on site, and the yeast has furthermore been shown to acquire an increased inhibitor tolerance when cultivated on hydrolysate. However, due to over-flow metabolism and inhibition, efficient yeast production on hydrolysate can only be achieved by well-controlled substrate addition. In the present work, a method was developed for controlled addition of hydrolysate to PDU (process development unit)-scale aerobic fed-batch cultivations of Saccharomyces cerevisiae TMB 3000. A feed rate control strategy, which maintains the ethanol concentration at a low constant level, was adapted to process-like conditions. The ethanol concentration was obtained from on-line measurements of the ethanol mole fraction in the exhaust gas. A computer model of the system was developed to optimize control performance. Productivities, biomass yields, and byproduct formation were evaluated. The feed rate control worked satisfactorily and maintained the ethanol concentration close to the setpoint during the cultivations. Biomass yields of 0.45 g/g were obtained on added hexoses during cultivation on hydrolysate and of 0.49 g/g during cultivation on a synthetic medium with glucose as the carbon source. Exponential growth was achieved with a specific growth rate of 0.18 h-1 during cultivation on hydrolysate and 0.22 h-1 during cultivation on glucose.     

Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes

The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify “biomass” in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with “state of the art” methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.     

Cultivation of the yeast Candida lipolytica on hydrocarbons. II. One-stage continuous cultivation on gas oil

Continuous cultivation of the yeast Candida lipolytica on gas oil was studied from the viewpoint of biomass production and oil deparaffination. Optimum conditions wore found at the dilution rate D = 0.16–0.19 when biomass productivity 1.7 g/l/hr and yield coefficient. y = 0.92 were achieved. At deparaffination to the same freezing point, more than double the production of biomass and deparaffined oil during a given time unit was achieved in a continuous process than in batch cultivation. Consumption of substrate was followed in both cultivation processes and it was confirmed that individual n-alkanes of gas oil were degraded at various rates and yields. Results proved optimum cultivation conditions to depend on concentration and composition of the paraffinic fraction of gas oil used. To achieve these conditions the continuous process may be controlled by choice; of suitable dilution rate and concentration of gas oil.     

Recombinant Candida rugosa LIP2 expression in Pichia pastoris under the control of the AOX1 promoter

The LIP2 isoenzyme gene from Candida rugosa has been completely synthesised and functionally expressed under the AOX1 promoter control in Pichia pastoris. The on-line monitoring and control of methanol, the key inducer carbon source in fed-batch cultures, has enhanced the yield product/biomass 7.8-fold and the productivity 12.8-fold compared to the best batch cultivation with the Pichia system and, 10-fold compared to the fed-batch cultivation process using the native C. rugosa strain.Nevertheless, the high ionic strength of culture broth favoured aggregation of Lip2, leading to total loss of lipolytic activity. After cultivation, a diaultrafiltration process was implemented to diminish ionic strength, allowing for the recovery of lipolytic activity in the diaultrafiltrate. The developed bioprocess resulted into a reproducible product in terms of quality and productivity.