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.     

Potential of real-time measurement of GFP-fusion proteins

Building on the basic design concepts of Randers-Eichhorn [Biotechnol. Bioeng. 55 (1997) 921], an on-line, real-time robust, steam sterilisable optical sensor for monitoring green fluorescent protein (GFP) has been developed. A general cloning vector for fusion expression proteins was constructed, allowing expression of both GFP and the target protein as a fusion. Cultivations were carried out at the 20l scale with the signal from the sensor being relayed directly to the control system of the bioreactors. The production of GFP was then measured on-line, the signal was interfaced directly with other controlling parameters, thereby allowing the microbial process to be controlled directly based on recombinant protein expression. A positive expression correlation between on-line and off-line data was obtained. Protein accretion measured off-line was quantified using both LC-MS and plate reader assays. The potential of such a sensor for many aspects of process development is considerable and we have developed a working system which allows the optimisation of production conditions, for example, linking pH control directly to the fusion protein. Results are also presented that illustrate GFP does not alter the cultivation characteristics of the target protein when compared to the native construct. Whether GFP expressed as a fusion influences the solubility of the target protein is also discussed.     

Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization

Modern bioprocess control requires fast data acquisition and in-time evaluation of bioprocess variables. On-line fluorescence spectroscopy and the application of chemometric methods accomplish these goals. In order to demonstrate how time-consuming off-line analysis methods can be replaced for bioprocess monitoring, fluorescence measurements were performed during different cultivations of the fungus Claviceps purpurea. To predict process variables like biomass, protein, and alkaloid concentrations, chemometric models were developed on the basis of the acquired fluorescence spectra. The results of these investigations are presented and the applicability of this approach for bioprocess monitoring is discussed.     

Evaluating metabolic stress and plasmid stability in plasmid DNA production by Escherichia coli

In the context of recombinant DNA technology, the development of feasible and high-yielding plasmid DNA production processes has regained attention as more evidence for its efficacy as vectors for gene therapy and DNA vaccination arise. When producing plasmid DNA in Escherichia coli, a number of biological restraints, triggered by plasmid maintenance and replication as well as culture conditions are responsible for limiting final biomass and product yields. This termed "metabolic burden" can also cause detrimental effects on plasmid stability and quality, since the cell machinery is no longer capable of maintaining an active metabolism towards plasmid synthesis and the stress responses elicited by plasmid maintenance can also cause increased plasmid instability. The optimization of plasmid DNA production bioprocesses is still hindered by the lack of information on the host metabolic responses as well as information on plasmid instability. Therefore, systematic and on-line approaches are required not only to characterise this "metabolic burden" and plasmid stability but also for the design of appropriate metabolic engineering and culture strategies. The monitoring tools described to date rapidly evolve from laborious, off-line and at-line monitoring to online monitoring, at a time-scale that enables researchers to solve these bioprocessing problems as they occur. This review highlights major E. coli biological alterations caused by plasmid maintenance and replication, possible causes for plasmid instability and discusses the ability of currently employed bioprocess monitoring techniques to provide information in order to circumvent metabolic burden and plasmid instability, pointing out the possible evolution of these methods towards online bioprocess monitoring.     

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed‐batch fermentations

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical “quality by testing” to rational, knowledge‐based “quality by design” approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real‐time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed‐batch production processes with varying cultivation conditions employing a comprehensive on‐ and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two‐dimensional multi‐wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.     

On-line infrared spectroscopy for bioprocess monitoring

One of the major aims of bioprocess engineering is the real-time monitoring of important process variables. This is the basis of precise process control and is essential for high productivity as well as the exact documentation of the overall production process. Infrared spectroscopy is a powerful analytical technique to analyze a wide variety of organic compounds. Thus, infrared sensors are ideal instruments for bioprocess monitoring. The sensors are non-invasive, have no time delay due to sensor response times, and have no influence on the bioprocess itself. No sampling is necessary, and several components can be analyzed simultaneously. In general, the direct monitoring of substrates, products, metabolites, as well as the biomass itself is possible. In this review article, insights are provided into the different applications of infrared spectroscopy for bioprocess monitoring and the complex data interpretation. Different analytical techniques are presented as well as example applications in different areas.     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

Evaluation of the GFP signal and its aptitude for novel on-line monitoring strategies of recombinant fermentation processes

A high number of economically important recombinant proteins are produced in Escherichia coli based host/vector systems. The major obstacle for improving current processes is a lack of appropriate on-line in situ methods for the monitoring of metabolic burden and critical state variables. Here, a pre-evaluation of the reporter green fluorescent protein (GFP) was undertaken to assess its use as a reporter of stress associated promoter regulation. The investigation of GFP and its blue fluorescent variant BFP was done in model fermentations using E. coli HMS 174(DE3)/pET11 aGFPmut3.1 and E. coli HMS174(DE3)/pET1aBFP host/vector systems cultured in fed-batch and chemostat regime. Our results prove the suitability of the fluorescent reporter proteins for the design of new strategies of on-line bioprocess monitoring. GFPmut3.1 variant can be detected after a short lag-phase of only 10 min, it shows a high fluorescence yield in relation to the amount of reporter protein, a good signal to noise ratio and a low detection limit. The fluorescence-signal and the amount of fluorescent protein, determined by ELISA, showed a close correlation in all fermentations performed. A combination of reporter technology with state of the art sensors helps to develop new strategies for efficient on-line monitoring needed for industrial process optimisation. The development of efficient monitoring will contribute to advanced control of recombinant protein production and accelerate the development of optimised production processes.     

Applications and perspectives of multi-parameter flow cytometry to microbial biofuels production processes

Conventional microbiology methods used to monitor microbial biofuels production are based on off-line analyses. The analyses are, unfortunately, insufficient for bioprocess optimization. Real time process control strategies, such as flow cytometry (FC), can be used to monitor bioprocess development (at-line) by providing single cell information that improves process model formulation and validation. This paper reviews the current uses and potential applications of FC in biodiesel, bioethanol, biomethane, biohydrogen and fuel cell processes. By highlighting the inherent accuracy and robustness of the technique for a range of biofuel processing parameters, more robust monitoring and control may be implemented to enhance process efficiency.     

Monitoring and control of the physiological state of cell cultures

Advances in bioprocess engineering depends ultimately on the level of understanding and control of the physiological state of the cell population. Process efficiency is strongly influenced by changes in the cellular state which should be monitored, interpreted, and, if possible, properly manipulated. In most control systems this function is not explicitly considered, which hampers process development and optimization. Conventional control logic is based on direct mapping of the growth environment into process efficiency, thereby bypassing the cell state as an intermediate control objective. Today, this limitation is well realized, and explicit monitoring and control of cellular physiology are considered to be among the most challenging tasks of modern bioprocess engineering. We present here a generic methodology for the design of systems capable of performing these advanced monitoring and control functions.The term "physiological state" is quantified by a vector composed of several process variables that convey significant information about cellular state. These variables can be selected among different classes, including specific metabolic rates, metabolic rate ratios, degees of limitation, and others. The real-time monitoring of many of these is possible using commercial sensors. The definition and calculation of representative sets of physiological state variables is demonstrated with examples from several fermentor cultures: recombinant Escherichia coli for phenylalanine production, bioluminescent E. coli (harboring lux genes driven by a heat shock protein promoter) for detection of environmental pollutants, plant cell culture of Perilla frutescensfor anthocyanin production, and perfusion cultures of recombinant mammalian cells (NS0 and CHO) for therapeutic protein production.If the physiological state vector is on-line calculated, the fermentation process can be described by its trajectory in a space defined by the vector components. Then, the goal of the control system is to maintain the physiological state of the cell as close as possible to the trajectory, providing maximum efficiency. A control structure meant to perform this function is proposed, along with the mechanism for its design. In contrast to conventional systems which work in a closed loop in respect to the cell environment, this scheme operates in a closed loop in respect to the cell state. The discussed control concept has been successfully applied to the recombinant phenylalanine production, resulting in physiologically consistent operation, total computer control, and high process efficiency. Initial results from the application of the method to perfusion mammalian cell cultures are also presented. (c) 1996 John Wiley & Sons, Inc.     

On-line load monitoring of wastewaters with a respirographic microbial sensor

This paper demonstrates the functionality, laboratory testing and field application of a microbial sensor that is capable of monitoring the organic pollution extent of wastewaters both off-line in a laboratory and on-line in a wastewater treatment plant. The biosensor was first developed in the laboratory using synthetic wastewater and then applied to monitor the effluent of the unit. The basic working principle of the biosensor is based on the on-line measurement of CO2 concentration in the off gas produced during carbon compound degradation by microbial respiration activities. CO2 concentration under operation conditions (constant oxygen flow rate, residence time and pH) is closely related to the extent of organic pollution (biochemical oxygen demand, chemical oxygen demand). CO2 monitoring is carried out by an infrared spectrometer, whereas current organic pollution is determined off-line according to the conventional 5-day lasting BOD analysis. Off gas analysis of CO2 concentration strongly correlates with off-line biochemical oxygen demand measurements allowing continuous on-line monitoring of the organic load within a wastewater treatment plant. Thus, real time process control and operation become feasible.     

On-line biomass estimation in biosurfactant production process by <Emphasis Type="Italic">Candida lipolytica</Emphasis> UCP 988

Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.     

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.     

Determination of protein concentration in downstream biomanufacturing processes by in-line index of refraction

Protein concentration determination is a necessary in-process control for the downstream operations within biomanufacturing. As production transitions from batch mode to an integrated continuous bioprocess paradigm, there is a growing need to move protein concentration quantitation from off-line to in-line analysis. One solution to fulfill this process analytical technology need is an in-line index of refraction (IoR) sensor to measure protein concentration in real time. Here the performance of an IoR sensor is evaluated through a series of experiments to assess linear response, buffer matrix effects, dynamic range, sensor-to-sensor variability, and the limits of detection and quantitation. The performance of the sensor was also tested in two bioprocessing scenarios, ultrafiltration and capture chromatography. The implementation of this in-line IoR sensor for real-time protein concentration analysis and monitoring has the potential to improve continuous bioprocess manufacturing.     

Insect cell density in bioreactor cultures can be estimated from on-line measurements of optical density

Total cell density in growing insect cell suspension cultures was accurately estimated from both off-line and on-line measurements of optical density (OD). The latter measurements were done with an in-situ autoclavable OD sensor. The ability to continuously monitor cell density in insect cell cultures may be useful for the development of a large-scale process for recombinant protein production using baculovirus expression vectors.     

In situ multi-wavelength fluorescence spectroscopy as effective tool to simultaneously monitor spore germination, metabolic activity and quantitative protein production in recombinant Aspergillus niger fed-batch cultures

The production of a mutant green fluorescent protein (S65TGFP), controlled by the maltose inducible glucoamylase promoter, was followed in situ in fed-batch cultures of recombinant Aspergillus niger using multi-wavelength fluorescence spectroscopy. Disturbance of quantitative product analysis by interfering fluorescence signals was resolved by using a set of defined combinations of excitation and emission wavelengths (lambda(ex)/lambda(em)). This technique resulted in excellent linearity between on-line signal and off-line determined S65TGFP concentrations. Spore germination was detectable in situ by monitoring the back scattered light intensity. Moreover, flavin-like fluorophores were identified as the dominating fungal host fluorophores. The time-dependent intensity of this fluorophore, potentially fungal flavin-containing oxidoreductase(s), did not correlate with the biomass concentration but correlated well with the fungal metabolic activity (e.g. respiratory activity). Other fluorophores commonly found in microbial cultures such NADH, pyridoxine and the aromatic amino acids, tryptophan, phenylalanine and tyrosine did not contribute significantly to the culture fluorescence of A. niger. Thus, multi-wavelength fluorescence spectroscopy has proven to be an effective tool for simultaneous on-line monitoring of the most relevant process variables in fungal cultures, e.g. spore germination, metabolic activity, and quantitative product formation.     

An on-line approach to monitor ethanol fermentation using FTIR spectroscopy

Fermentation process control is currently limited by its inability to measure parameters such as substrate, product, and biomass concentrations rapidly for consistent on-line feedback. Physical and chemical parameters, such as temperature and pH, currently can be obtained on-line using appropriate sensors. However, to obtain information on the concentration of the substrate, product, and biomass, samples must be taken off-line for measurement. With the use of spectroscopic techniques, real-time monitoring of process constituents such as product and substrate is possible. Spectroscopic techniques are rapid and nondestructive, require minimal or no sample preparation, and can be used to simultaneously assess several constituents in complex matrices. The production of ethanol is the largest fermentation process in terms of production volume and economic value as a result of its prominence in the food, agricultural, and fuel industries. This study attempts to develop an on-line ethanol fermentation monitoring technique using Fourier transform infrared (FTIR) spectroscopy with a flow-through ATR capability. Models developed using multivariate statistics, employed to obtain on-line FTIR measurements, were successfully validated by off-line HPLC analysis and spectrophotometry data. Standard errors of prediction (SEP) values of 0.985 g/L (R2 = 0.996), 1.386 g/L (R2 = 0.998), and 0.546 (R2 = 0.972) were obtained for ethanol, glucose, and OD, respectively. This work demonstrates that FTIR spectroscopy could be used for rapid on-line monitoring of fermentation.     

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno-associated virus production by fluorescence spectroscopy

The process analytical technology (PAT) initiative shifted the bioprocess development mindset towards real-time monitoring and control tools to measure relevant process variables online, and acting accordingly when undesirable deviations occur. Online monitoring is especially important in lytic production systems in which released proteases and changes in cell physiology are likely to affect product quality attributes, as is the case of the insect cell-baculovirus expression vector system (IC-BEVS), a well-established system for production of viral vectors and vaccines. Here, we applied fluorescence spectroscopy as a real-time monitoring tool for recombinant adeno-associated virus (rAAV) production in the IC-BEVS. Fluorescence spectroscopy is simple, yet sensitive and informative. To overcome the strong fluorescence background of the culture medium and improve predictive ability, we combined artificial neural network models with a genetic algorithm-based approach to optimize spectra preprocessing. We obtained predictive models for rAAV titer, cell viability and cell concentration with normalized root mean squared errors of 7%, 4%, and 7%, respectively, for leave-one-batch-out cross-validation. Our approach shows fluorescence spectroscopy allows real-time determination of the best time of harvest to maintain rAAV infectivity, an important quality attribute, and detection of deviations from the golden batch profile. This methodology can be applied to other biopharmaceuticals produced in the IC-BEVS, supporting the use of fluorescence spectroscopy as a versatile PAT tool.