Hybrid modeling framework for process analytical technology: application to Bordetella pertussis cultures

In the process analytical technology (PAT) initiative, the application of sensors technology and modeling methods is promoted. The emphasis is on Quality by Design, online monitoring, and closed-loop control with the general aim of building in product quality into manufacturing operations. As a result, online high-throughput process analyzers find increasing application and therewith high amounts of highly correlated data become available online. In this study, an hybrid chemometric/mathematical modeling method is adopted for data analysis, which is shown to be advantageous over the commonly used chemometric techniques in PAT applications. This methodology was applied to the analysis of process data of Bordetella pertussis cultivations, namely online data of near-infrared, (NIR), pH, temperature and dissolved oxygen, and off-line data of biomass, glutamate, and lactate concentrations. The hybrid model structure consisted of macroscopic material balance equations in which the specific reactions rates are modeled by nonlinear partial least square (PLS). This methodology revealed a significant higher statistical confidence in comparison to PLSs, translated in a reduction of mean squared prediction errors (e.g., individual root mean squared prediction errors calibration/validation obtained through the hybrid model for the concentrations of lactate: 0.8699/0.7190 mmol/L; glutamate: 0.6057/0.2917 mmol/L; and biomass: 0.0520/0.0283 OD; and obtained through the PLS model for the concentrations of lactate: 1.3549/1.0087 mmol/L; glutamate: 0.7628/0.3504 mmol/L; and biomass: 0.0949/0.0412 OD). Moreover, the analysis of loadings and scores in the hybrid approach revealed that process features can, as for PLS, be extracted by the hybrid method.     

The kinetics of Lagenidium giganteum growth in liquid and solid cultures

AIMS: Production of the mosquito biolarvacide Lagenidium giganteum in solid culture has been proposed as an economic alternative to production in liquid culture because of observations of improved shelf life and efficacy upon storage. Understanding the differences between these production systems and estimating growth rate in solid culture are important for commercialization. In order to address these needs a logistic model was developed to describe the growth kinetics of L. giganteum produced in solid and liquid cultures. METHODS AND RESULTS: Kinetic parameters in the logistic model were estimated by nonlinear regression of CO2 evolution rate (CER) and biomass data from solid and liquid cultivation experiments. Lagenidium giganteum biomass was measured using DNA extracted directly from samples. The logistic model was fit to experimental biomass and CER data with low standard errors for parameter estimates. The model was validated in two independent experiments by examining prediction of biomass using on-line CER measurements. CONCLUSIONS: There were significant differences between maximum biomass density, maintenance coefficients, and specific growth rates for liquid and solid cultures. The maximum biomass density (mg dw ml-1) was 11 times greater for solid cultivation compared with liquid cultivation of L. giganteum; however, the maintenance coefficient (mg CO2 h-1 (mg dw)-1) was six times greater for liquid cultivation than in solid cultivation. The specific growth rate at 30 degrees C was approximately 30% greater in liquid cultivation compared with solid cultivation. Slower depletion of substrate and lower endogenous metabolism may explain the longer shelf life of L. giganteum produced in solid culture. SIGNIFICANCE AND IMPACT OF THE STUDY: A simple logistic model was developed which allows real-time estimation of L. giganteum biomass from on-line CER measurements. Parameter estimates for liquid and solid cultivation models also elucidated observations of longer shelf life for production in solid culture.     

A fully online sensor-equipped,disposable multiphase microbioreactor as a screening platform for biotechnological applications

A new disposable, multiphase, microbioreactor (MBR; with a working volume of 550 μl) equipped with online sensors is presented for biotechnological screening research purposes owing to its high-throughput potential. Its design and fabrication, online sensor integration, and operation are described. During aerobic cultivation, sufficient oxygen supply is the most important factor that influences growth and product formation. The MBR is a microbubble column bioreactor (μBC), and the oxygen supply was realized by active pneumatic bubble aeration, ensuring sufficient volumetric liquid-phase mass transfer (k _L a) and proper homogenization of the cultivation broth. The μBC was equipped with miniaturized sensors for the pH, dissolved oxygen, optical density and glucose concentration that allowed real-time online monitoring of these process variables during cultivation. The challenge addressed here was the integration of sensors in the limited available space. The MBR was shown to be a suitable screening platform for the cultivation of biological systems. Batch cultivations of Saccharomyces cerevisiae were performed to observe the variation in the process variables over time and to show the robustness and operability of all the online sensors in the MBR.     

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.     

Investigation and evaluation of a 3D-printed optical modified cultivation vessel for improved scattered light measurement of biotechnologically relevant organisms

In the field of bioprocess development miniaturization, parallelization and flexibility play a key role reducing costs and time. To precisely meet these requirements, additive manufacturing (3D-printing) is an ideal technology. 3D-printing enables rapid prototyping and cost-effective fabrication of individually designed devices with complex geometries on demand. For successful bioprocess development, monitoring of process-relevant parameters, such as pH, dissolved oxygen (DO), and biomass, is crucial. Online monitoring is preferred as offline sampling is time-consuming and leads to loss of information. In this study, 3D-printed cultivation vessels with optical prisms are evaluated for the use in upstream processes of different industrially relevant microorganisms and cell lines. It was shown, that the 3D-printed optically modified well (OMW) is of benefit for a wide range of biotechnologically relevant microorganisms and even for mammalian suspension cells. Evaluation tests with Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, and Chinese hamster ovary (CHO) cells were performed, providing highly reproducible results. Growth behavior of OMW cultures was comparable to behavior of shake flask (SF) cultivations and the signal to noise ratio in online biomass measurement was shown to be reduced up to 95.8% by using the OMW. Especially the cultivation phases with low turbidity respective optical densities below 1.0 rel.AU could be monitored accurately for the first time. Furthermore, it was demonstrated that the 3D-printed optics are transferable to different well geometries and sizes, enabling efficient biomass monitoring for individual requirements with tailor-made 3D-printed cultivation vessels in small scale.     

Aerobic batch cultivation in micro bioreactor with integrated electrochemical sensor array

Aerobic batch cultivations of Candida utilis were carried out in two micro bioreactors with a working volume of 100 μL operated in parallel. The dimensions of the micro bioreactors were similar as the wells in a 96‐well microtiter plate, to preserve compatibility with the current high‐throughput cultivation systems. Each micro bioreactor was equipped with an electrochemical sensor array for the online measurement of temperature, pH, dissolved oxygen, and viable biomass concentration. Furthermore, the CO₂ production rate was obtained from the online measurement of cumulative CO₂ production during the cultivation. The online data obtained by the sensor array and the CO₂ production measurements appeared to be very reproducible for all batch cultivations performed and were highly comparable to measurement results obtained during a similar aerobic batch cultivation carried out in a conventional 4L bench‐scale bioreactor. Although the sensor chip certainly needs further improvement on some points, this work clearly shows the applicability of electrochemical sensor arrays for the monitoring of parallel micro‐scale fermentations, e.g. using the 96‐well microtiterplate format. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l⁻¹ biomass and 0.8 g l⁻¹ total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.     

On-line monitoring of hybridoma cell growth using a laser turbidity sensor

A high-sensitivity turbidity probe was used for on-line monitoring of the cell concentration in batch hybridoma cultivation. Good correlation between off-line cell counts and the linearized sensor signal was found. The quality of the signal was sufficiently high to provide for on-line estimation of the specific growth rate using an efficient filtering procedure. These positive results suggest that such laser turbidity sensors will facilitate development of systems for on-line monitoring and control of animal cell cultivations. (c) 1992 John Wiley & Sons, Inc.     

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.     

On-line biomass measurements in bioreactor cultivations: comparison study of two on-line probes

Two on-line probes for biomass measurement in bioreactor cultivations were evaluated. One probe is based on near infrared (NIR) light absorption and the other on dielectric spectroscopy. The probes were used to monitor biomass production in cultivations of several different microorganisms. Differences in NIR probe response compared to off-line measurement methods revealed that the most significant factor affecting the response was cell shape. The NIR light absorption method is more developed and reliable for on-line in situ biomass estimation than dielectric spectroscopy. The NIR light absorption method is, however, of no significant use, when the cultivation medium is not clear, and especially in processes using adsorbents or solid matrix for the microorganism to grow on. The possibilities offered by dielectric spectroscopy are impressive, but the on-line probe technology needs to be improved.     

Growth behavior and protease production by an icelandicthermus sp. isolate in batch and continuous culture

AThermus strain, producing an extracellular protease, was isolated in a hot spring in Iceland. The main growth characteristics of this isolate were studied with different cultivation vessels and different cultivation techniques. A clear and striking dependence of the growth behavior on the cultivation technique was apparent. Higher maximum yield of biomass, higher productivity of biomass, and higher maximum growth rate were found in continuous cultivations compared with batch cultivations. The substrate utilization and the yield of biomass of this strain were much higher than reported for several otherThermus strains. Reproducibility of kinetic data seemed not to depend on the type of cultivation vessel, on the basis of the types of vessels tested, and instability of the population was not observed during cultivations. Production of extracellular protease in our cultivations was apparently growth associated in batch culture, and the specific rate of production in continuous culture was dependent of the dilution rate, implying that certain kinds of regulatory mechanism(s) might be involved.     

Simulation of penicillin production in fed-batch cultivations using a morphologically structured model

A mathematical model is formulated to describe trends in biomass and penicillin formation as well as substrate consumption for fed-batch cultivations. The biomass is structured into three morphological compartments, and glucose and corn steep liquor are considered as substrates for growth. Penicillin formation is assumed to take place in the subapical compartment and in the growing region of the hyphal compartment. Furthermore, it is inhibited by glucose. Model parameters are estimated using an evolutionary algorithm and fitting the model to a standard fed-batch cultivation. The model is validated on experimental data from three different fed-batch cultivations, including two repeated fed-batch cultivations. The model predictions show good agreement with the measurements of biomass and pencillin concentrations for all fed-batch cultivations. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 593-604, 1997.     

Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine

The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In this work, NARX-ANN, NARMAX-ANN, NARX-SVM and NARMAX-SVM models are compared when acting as software sensors of biomass concentration for a solid substrate cultivation (SSC) process. Results show that NARMAX-SVM outperforms the other models with an SMAPE index under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as they include prediction errors as inputs. In the case of perturbation of initial conditions of the autoregressive variable, NARX models exhibited better convergence capabilities. This work also confirms that a difficult to measure variable, like biomass concentration, can be estimated on-line from easy to measure variables like CO₂ and O₂ using an adequate software sensor based on computational intelligence techniques.     

Hydrogen online monitoring based on thermal conductivity for anaerobic microorganisms

H₂-producing microorganisms are a promising source of sustainable biohydrogen. However, most H₂-producing microorganisms are anaerobes, which are difficult to cultivate and characterize. While several methods for measuring H₂ exist, common H₂ sensors often require oxygen, making them unsuitable for anaerobic processes. Other sensors can often not be operated at high gas humidity. Thus, we applied thermal conductivity (TC) sensors and developed a parallelized, online H₂ monitoring for time-efficient characterization of H₂ production by anaerobes. Since TC sensors are nonspecific for H₂, the cross-sensitivity of the sensors was evaluated regarding temperature, gas humidity, and CO₂ concentrations. The systems' measurement range was validated with two anaerobes: a high H₂-producer (Clostridium pasteurianum) and a low H₂-producer (Phocaeicola vulgatus). Online monitoring of H₂ production in shake flask cultivations was demonstrated, and H₂ transfer rates were derived. Combined with online CO₂ and pressure measurements, molar gas balances of the cultivations were closed, and an anaerobic respiration quotient was calculated. Thus, insight into the effect of medium components and inhibitory cultivation conditions on H₂ production with the model anaerobes was gained. The presented online H₂ monitoring method can accelerate the characterization of anaerobes for biohydrogen production and reveal metabolic changes without expensive equipment and offline analysis.     

Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors

By enabling the estimation of difficult‐to‐measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first‐principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory‐scale cultivations in addition to in‐silico experiments enable a comparison of theoretical aspects of observability analysis and the real‐life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1703–1715, 2015     

Process analytical technology tools for perfusion cell culture

During cell cultivation processes for the production of biopharmaceuticals, good process performance and good product quality can be ensured by online monitoring of critical process parameters (e.g. temperature, pH, or dissolved oxygen). These data can be used in real‐time for process control, as suggested by the process analytical technology (PAT) initiative. Today, solutions for real‐time monitoring of parameters such as concentrations of cells, main nutrients, and metabolism by‐products are developing, but applications of these more complex tools in industrial settings are still limited. Here, we evaluated the use of dielectric spectroscopy (DS) and near‐infrared spectroscopy (NIRS) as PAT tools for a perfusion PER.C6^® cultivation process. We showed that DS enabled predictions of viable cell density from the cultivation vessel, with a root mean square error of prediction (RMSEP) of 4.4% of the calibration range. Additionally, predictions of glucose and lactate concentrations from the cultivation vessel (RMSEP of 10 and 14%, respectively) and from the perfusion stream (RMSEP of 12 and 10%, respectively) were achieved with NIRS. We also showed that the perfusion stream offers great opportunities for noninvasive, yet frequent process monitoring. Accurate online monitoring of critical process parameters with PAT tools is the essential first step toward increased control of process output.     

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 monitoring biomass concentration in Streptomyces clavuligerus cultivations with industrial media using a capacitance probe

On-line monitoring biomass concentration in mycelial fed-batch cultivations of Streptomyces clavuligerus grown with soluble and partially insoluble complex media, was investigated with an in-situ capacitance probe fitted to an industrial pilot-plant tank. Standard off-line and on-line biomass determinations, including cell dry weight, packed mycelial volume, viscosity, DNA concentration and total CO(2) evolution in the exhaust gases, were performed throughout the experiments and compared to on-line capacitance measurements. Linear relations between capacitance and all other measurements were developed for both media that hold only in defined process phases, depending on the biomass state and the amount of insoluble matter present. For the industrial complex culture media good linear relations were obtained in the fast growth phase between capacitance and DNA concentration and total CO(2) evolution, while in the subsequent transition and stationary phases only with apparent viscosity was a reasonable correlation found. The capacitance probe was shown to be a valuable tool for real-time monitoring biomass concentration in industrial-like cultivation of mycelial streptomycetes.     

On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy

Multi-wavelength fluorescence spectroscopy was evaluated as a tool for on-line monitoring of recombinant Escherichia coli cultivations expressing human basic fibroblast growth factor (hFGF-2). The data sets for the various combinations of the excitation and emission spectra from batch cultivations were analyzed using principal component analysis. Chemometric models (the partial least squares method) were developed for correlating the fluorescence data and the experimentally measured variables such as the biomass and glucose concentrations as well as the carbon dioxide production rate. Excellent correlations were obtained for these variables for the calibration cultivations. The predictability of these models was further tested in batch and fed-batch cultivations. The batch cultivations were well predicted by the PLS models for biomass, glucose concentrations and carbon dioxide production rate (RMSEPs were respectively 5%, 7%, 9%). However, when tested for biomass concentrations in fed-batch cultivations (with final biomass three times higher than the highest calibration data) the models had good predictability at high growth rates (RMSEPs were 3% and 4%, respectively for uninduced and induced fed-batch cultivations), which was as good as for the batch cultivations used for developing the models (RMSEPs were 3% and 5%, respectively for uninduced and induced batch cultivations). The fed-batch cultivations performed at low growth rates exhibited much higher fluorescence for fluorophores such as flavin and NAD(P)H as compared to fed-batch cultivations at high growth rate. Therefore, the PLS models tended to over-predict the biomass concentrations at low growth rates. Obviously the cells changed their concentration of biogenic fluorophores depending on the growth rate. Although multi-wavelength fluorescence spectroscopy is a valuable tool for on-line monitoring of bioprocess, care must be taken to re-calibrate the PLS models at different growth rates to improve the accuracy of predictions.