Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0

Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

At-Line Reversed Phase Liquid Chromatography for In-Process Monitoring of Inclusion Body Solubilization

Refolding is known as the bottleneck in inclusion body (IB) downstream processing in the pharmaceutical industry: high dilutions leading to large operating volumes, slow refolding kinetics and low refolding yields are only a few of the problems that impede industrial application. Solubilization prior to refolding is often carried out empirically and the effects of the solubilizate on the subsequent refolding step are rarely investigated. The results obtained in this study, however, indicate that the quality of the IB solubilizate has a severe effect on subsequent refolding. As the solubilizate contains chaotropic reagents in high molarities, it is commonly analyzed with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). SDS-PAGE, however, suffers from a long analysis time, making at-line analytical implementation difficult. In this study, we established an at-line reversed phase liquid chromatography method to investigate the time-dependent quality of the solubilizate. To verify the necessity of at-line solubilization monitoring, we varied the essential solubilization conditions for horseradish peroxidase IBs. The solubilization time was found to have a major influence on subsequent refolding, underlining the high need for an at-line analysis of solubilization. Furthermore, we used the developed reversed phase liquid chromatography method for an in-process control (IPC). In conclusion, the presented reversed phase liquid chromatography method allows a proper control of IB solubilization applicable for tailored refolding.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

Monitoring and control strategies for inclusion body production in E. coli based on glycerol consumption

The Gram-negative bacterium E. coli is the host of choice for the production of a multitude of recombinant proteins in industry. Generally, cultivation is easy, media are cheap and a high product titer can be obtained. However, harsh induction procedures using IPTG as inducer are often referred to cause stress reactions, leading to a phenomenon known as metabolic burden and expression of inclusion bodies. In this contribution, we present different strategies for determination of critical timepoints for product stability in an E. coli IB bioprocess. As non-controlled feeding during induction regularly led to undesired product loss, we applied physiological feeding control. We found that the feeding strategy has indeed high impact on IB productivity. However, high applied q_s,C increased IB product titer, but subsequently stressed the cells and finally led to product degradation. Calculating the cumulated glycerol uptake of the cells during induction phase (dSn), we found an empirical value, which serves as a strong indicator for process performance and can be used as process analytical tool. We tested different approaches starting from offline control. Glycerol accumulation could be used as trigger to establish a model-based approach to predict titer and viable cell concentration for a model protein. This straight forward control and model-based approach is high beneficial for upstream development and for increasing stability.

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production

This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in comparison to other designed controllers in terms of qualitative and quantitative performance indices.

Control of specific carbon dioxide production in a fed-batch culture producing recombinant protein using a soft sensor

The feeding of a fed-batch cultivation producing recombinant protein was controlled by a soft sensor set-up. It was assumed that the control approach could be based on the cell's production of carbon dioxide and that this parameter indicates the metabolic state occurring at induced protein expression. The soft sensor used the on-line signals from a carbon dioxide analyser and a near-infrared (NIR) probe for biomass to estimate the specific production rate qCO2. Control experiments were carried out with various qCO2 set-points where we observe that the feeding of nutrients to the culture could easily be controlled and resulted in a decreased variability compared to uncontrolled cultivations. We therefore suggest that this control approach could serve as an alternative to other commonly applied methods such as controlling the cell's overflow metabolism of acetate or the cell's specific growth rate. However, further studies of the internal metabolic fluxes of CO2 during protein expression would be recommended for a more precise characterization of the relationship between qCO2 and protein expression in order to fully interpret the control behaviour.

Operator training simulation for integrating cultivation and homogenisation in protein production

Operating training simulators (OTS) are virtual simulation tools used for training of process operators in industry in performing procedures and running processes. Based on structured mathematical models of the unit operations of a bioprocess an OTS can train a process operator by visualising changing conditions during the process, allow testing operator actions, testing controller settings, experience unexpected technical problems and getting practice in using prescribed standard procedures for a plant.

This work shows the design of an OTS where two sequential steps of a recombinant protein production process, a fed-batch cultivation and a high-pressure homogenisation, are integrated. The OTS was evaluated on a user test group and showed that the OTS promoted and developed their understanding of the process, their capability to identify parameters influencing process efficiency and the skills of operating it.

A novel cytosolic NADH:quinone oxidoreductase from Methanothermobacter marburgensis

Methanothermobacter marburgensis is a strictly anaerobic, thermophilic methanogenic archaeon that uses methanogenesis to convert H₂ and CO₂ to energy. M. marburgensis is one of the best-studied methanogens, and all genes required for methanogenic metabolism have been identified. Nonetheless, the present study describes a gene (Gene ID 9704440) coding for a putative NAD(P)H:quinone oxidoreductase that has not yet been identified as part of the metabolic machinery. The gene product, MmNQO, was successfully expressed, purified and characterized biochemically, as well as structurally. MmNQO was identified as a flavin-dependent NADH:quinone oxidoreductase with the capacity to oxidize NADH in the presence of a wide range of electron acceptors, whereas NADPH was oxidized with only three acceptors. The 1.50 Å crystal structure of MmNQO features a homodimeric enzyme where each monomer comprises 196 residues folding into flavodoxin-like α/β domains with non-covalently bound FMN (flavin mononucleotide). The closest structural homologue is the modulator of drug activity B from Streptococcus mutans with 1.6 Å root-mean-square deviation on 161 Cα atoms and 28% amino-acid sequence identity. The low similarity at sequence and structural level suggests that MmNQO is unique among NADH:quinone oxidoreductases characterized to date. Based on preliminary bioreactor experiments, MmNQO could provide a useful tool to prevent overflow metabolism in applications that require cells with high energy demand.

A novel NADH:quinone oxidoreductase, MmNQO, from Methanothermobacter marburgensis was identified. MmNQO oxidizes NADH with several electron acceptors and is structurally similar to bacterial MdaB. It is localized in the cytosol and may provide a useful tool to prevent overflow metabolism.

A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.

Operator training in recombinant protein production using a structured simulator model

Model-based operator training simulators (OTS) could be powerful tools for virtual training of operational procedures and skills of production personnel in recombinant protein processes. The applied model should describe critical events in the bioprocess so accurately that the operators' ability to observe and alertly act upon these events is trained with a high degree of efficiency. In this work is shown how this is accomplished in a structured multi-compartment model for the production of a recombinant protein in an Escherichia coli fed-batch process where in particular the induction procedure, the stress effects and overflow metabolism were highlighted. The structured model was applied on the OTS platform that virtually simulated the operational bioreactor procedures in real or accelerated time. Evaluation of training using the model-based OTS showed that trained groups of operators exhibited improved capability compared with the untrained groups when subsequently performing real laboratory scale cultivations. The results suggest that this model-based OTS may provide a valuable resource for enhancing operator skills in large scale recombinant protein manufacturing.