Feeding strategies enhance high cell density cultivation and protein expression in milliliter scale bioreactors

A Unique Response Behavior in the Dissolved Oxygen Tension in E. coli Minibioreactor Cultivations with Intermittent Feeding

Intermittent bolus feeding for E. coli cultivations in minibioreactor systems (MBRs) profoundly affects the cell metabolism. Bolus feeding leads to temporal substrate surplus and transient oxygen limitation, which triggers the formation of inhibitory byproducts. Due to the high oxygen demand right after the injection of the substrate, the dissolved oxygen tension (DOT) signal exhibits a negative pulse. This contribution describes and analyzes this DOT response in E. coli minibioreactor cultivations. In addition to gaining information on culture conditions, a unique response behavior in the DOT signal was observed in the analysis. This response appeared only at a dilution ratio per biomass unit higher than a certain threshold. The analysis highlights a plausible relationship between a metabolic adaptation behavior and the newly observed DOT signal segment not reported in the literature. A hypothesis that links particular DOT segments to specific metabolic states is proposed. The quantitative analysis and mechanistic model simulations support this hypothesis and show the possibility of obtaining cell physiological and growth parameters from the DOT signal.

Control of parallelized bioreactors II: probabilistic quantification of carboxylic acid reductase activity for bioprocess optimization

Autonomously operated parallelized mL-scale bioreactors are considered the key to reduce bioprocess development cost and time. However, their application is often limited to products with very simple analytics. In this study, we investigated enhanced protein expression conditions of a carboxyl reductase from Nocardia otitidiscaviarum in E. coli. Cells were produced with exponential feeding in a L-scale bioreactor. After the desired cell density for protein expression was reached, the cells were automatically transferred to 48 mL-scale bioreactors operated by a liquid handling station where protein expression studies were conducted. During protein expression, the feed rate and the inducer concentration was varied. At the end of the protein expression phase, the enzymatic activity was estimated by performing automated whole-cell biotransformations in a deep-well-plate. The results were analyzed with hierarchical Bayesian modelling methods to account for the biomass growth during the biotransformation, biomass interference on the subsequent product assay, and to predict absolute and specific enzyme activities at optimal expression conditions. Lower feed rates seemed to be beneficial for high specific and absolute activities. At the optimal investigated expression conditions an activity of [Formula: see text] was estimated with a [Formula: see text] credible interval of [Formula: see text]. This is about 40-fold higher than the highest published data for the enzyme under investigation. With the proposed setup, 192 protein expression conditions were studied during four experimental runs with minimal manual workload, showing the reliability and potential of automated and digitalized bioreactor systems.

Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems

The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Automated Bioprocess Feedback Operation in a High-Throughput Facility via the Integration of a Mobile Robotic Lab Assistant

The development of biotechnological processes is challenging due to the diversity of process parameters. For efficient upstream development, parallel cultivation systems have proven to reduce costs and associated timelines successfully while offering excellent process control. However, the degree of automation of such small-scale systems is comparatively low, and necessary sample analysis requires manual steps. Although the subsequent analysis can be performed in a high-throughput manner, the integration of analytical devices remains challenging, especially when cultivation and analysis laboratories are spatially separated. Mobile robots offer a potential solution, but their implementation in research laboratories is not widely adopted. Our approach demonstrates the integration of a small-scale cultivation system into a liquid handling station for an automated cultivation and sample procedure. The samples are transported via a mobile robotic lab assistant and subsequently analyzed by a high-throughput analyzer. The process data are stored in a centralized database. The mobile robotic workflow guarantees a flexible solution for device integration and facilitates automation. Restrictions regarding spatial separation of devices are circumvented, enabling a modular platform throughout different laboratories. The presented cultivation platform is evaluated on the basis of industrially relevant E. coli BW25113 high cell density fed-batch cultivation. The necessary magnesium addition for reaching high cell densities in mineral salt medium is automated via a feedback operation loop between the analysis station located in the adjacent room and the cultivation system. The modular design demonstrates new opportunities for advanced control options and the suitability of the platform for accelerating bioprocess development. This study lays the foundation for a fully integrated facility, where the physical connection of laboratory equipment is achieved through the successful use of a mobile robotic lab assistant, and different cultivation scales can be coupled through the common data infrastructure.

Recent advances in fed-batch microscale bioreactor design

Advanced fed-batch microbioreactors mitigate scale up risks and more closely mimic industrial cultivation practices. Recently, high throughput microscale feeding strategies have been developed which improve the accessibility of microscale fed-batch cultivation irrespective of experimental budget. This review explores such technologies and their role in accelerating bioprocess development. Diffusion- and enzyme-controlled feeding achieve a continuous supply of substrate while being simple and affordable. More complex feed profiles and greater process control require additional hardware. Automated liquid handling robots may be programmed to predefined feed profiles and have the sensitivity to respond to deviations in process parameters. Microfluidic technologies have been shown to facilitate both continuous and precise feeding. Holistic approaches, which integrate automated high-throughput fed-batch cultivation with strategic design of experiments and model-based optimisation, dramatically enhance process understanding whilst minimising experimental burden. The incorporation of real-time data for online optimisation of feed conditions can further refine screening. Although the technologies discussed in this review hold promise for efficient, low-risk bioprocess development, the expense and complexity of automated cultivation platforms limit their widespread application. Future attention should be directed towards the development of open-source software and reducing the exclusivity of hardware.

Automated multi-scale cascade of parallel stirred-tank bioreactors for fast protein expression studies

Automation, parallelization and autonomous operation of standard lab equipment, usually applied for manual bioprocess development, is considered as the key for reduction of bioprocess development time and costs. An automated bioreactor system with 4 stirred-tank bioreactors on a L-scale was combined with a custom-made biomass transfer system to distribute the cell suspensions produced on the L-scale into 48 parallel stirred-tank bioreactors on a mL-scale. Afterwards parallel protein expression studies automated by a liquid handling system with integrated fluorescence reader were performed. Isopropyl β-D-1-thiogalactopyranoside-induced (IPTG) expression of the red fluorescence protein mCherry was studied as an example of using fed-batch processes with recombinant Escherichia coli. In a first automated study, IPTG concentrations were varied in 48 parallel fed-batch processes with E. coli cells produced at a growth rate of 0.1 h^-1 on an L-scale and transferred automatically to the mL-scale. The mCherry expression rate increased with increasing inducer concentration until the highest protein expression rate was observed at > 9 μM IPTG. In a second automated study, the growth rate of E. coli was varied between 0.1-0.2 h^-1 in parallelly-operated stirred-tank bioreactors on a L-scale. The cells were automatically transferred and distributed into the stirred-tank bioreactors on a mL-scale and the concentration of the inducer IPTG was varied as before in parallel fed-batch processes. An increased growth rate during the production of the recombinant E. coli cells and/or higher cell densities during protein expression resulted in the increased IPTG concentrations necessary to achieve identical expression rates compared to a growth rate of 0.1 h^-1 with the exception of very low inducer concentrations and inducer concentrations in excess. The new automated multi-scale cascade of parallel stirred-tank bioreactors should easily be applicable for performing fast optimisation studies with other microbial production systems and will have the potential to reduce bioprocess development time and staff assignment considerably.

Process adapted calibration improves fluorometric pH sensor precision in sophisticated fermentation processes

Miniaturization and automation have become increasingly popular in bioprocess development in recent years, enabling rapid high-throughput screening and optimization of process conditions. In addition, advances in the bioprocessing industry have led to increasingly complex process designs, such as pH and temperature shifts, in microbial fed-batch fermentations for optimal soluble protein expression in a range of hosts. However, in order to develop an accurate scale-down model for bioprocess screening and optimization, small-scale bioreactors must be able to accurately reproduce these complex process designs. Monitoring methods, such as fluorometric-based pH sensors, provide elegant solutions for the miniaturization of bioreactors, however, previous research suggests that the intrinsic fluorescence of biomass alters the sigmoidal calibration curve of fluorometric pH sensors, leading to inaccurate pH control. In this article, we present results investigating the impact of biomass on the accuracy of a commercially available fluorometric pH sensor. Subsequently, we present our calibration methodology for more precise online measurement and provide recommendations for improved pH control in sophisticated fermentation processes.

Efficient Allitol Bioproduction from D-Fructose Catalyzed by Recombinant E. coli Whole Cells, and the Condition Optimization, Product Purification

Allitol is a kind of rare sugar alcohol with potential application value. An engineered strain, which simultaneously expressed D-psicose-3-epimerase (DPE), ribitol dehydrogenase (RDH), and formate dehydrogenase (FDH) three enzymes, was constructed by cloning above three genes into one plasmid and transformed into the host E. coli strain, and used as the whole-cell catalysts for biotransformation of allitol from the low-cost substrate of D-fructose. The whole cell allitol biotransformation conditions were optimized. The medium, recombinant gene induction conditions, and the substrate feeding rate for cultivation of the catalytic cells were optimized. Then, the fed-batch culture was made and scaled up to 10 L fermentor. Finally, 63.44 g/L allitol was obtained from 100 g/L D-fructose after 3 h of biotransformation, and the allitol crystals of 99.9% purity were obtained by using cooling recrystallization. The allitol production method developed in this research has high product purity, and is highly efficient, easily scaled up, and suitable for large-scale production of highly purified allitol.

Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators

Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.

High-level expression of Thermobifida fusca glucose isomerase for high fructose corn syrup biosynthesis

Glucose isomerase (GIase), an efficient enzyme in the isomerization of d-glucose to d-fructose, has been widely used in food processing. In this study, an efficient expression system for a Thermobifida fusca GIase (GIase_Tfus) in Escherichia coli was firstly designed via a two-stage feeding strategy for improving expression level. The cultivation strategy was performed at an exponential feeding rate during the pre-induction phase, followed by a gradient-decreasing feeding rate at the induction phase in a 3-L fermenter. During this process, the effect of induction conditions and the complex nitrogen supplementation in feeding solutions on GIase_Tfus production were investigated and optimized. Under the optimal conditions, the yield of GIase_Tfus reached 124.1 U/mL, which is the highest expression level of GIase by recombinant E. coli reported to date. Additionally, the obtained GIase_Tfus was performed to produce high fructose corn syrup (HFCS) with conversion approacing 55 % from glucose (45 %, w/v) to fructose. According to the molecular dynamic simulation, a number of hydrogen bonds existed in the enzyme-substrate complex could stablilize the transient states, and a appreciate reaction distance of M1 catalytic site and oxygen atom of glucose make the reaction proceed easily, thus resulting in the efficient biosynthesis of HFCS. The function of GIase_Tfus renders it a valuable catalyst for HFCS-55 (containing 55 % d-fructose) manufacturing, the most favorable industrial product of HFCS. The efficient expression of GIase_Tfus and its efficient HFCS production lays the foundation for its proming industrial application.

Implementation of a Fully Automated Microbial Cultivation Platform for Strain and Process Screening

Advances in molecular biotechnology have resulted in the generation of numerous potential production strains. Because every strain can be screened under various process conditions, the number of potential cultivations is multiplied. Exploiting this potential without increasing the associated timelines requires a cultivation platform that offers increased throughput and flexibility to perform various bioprocess screening protocols. Currently, there is no commercially available fully automated cultivation platform that can operate multiple microbial fed-batch processes, including at-line sampling, deep freezer off-line sample storage, and complete data handling. To enable scalable high-throughput early-stage microbial bioprocess development, a commercially available microbioreactor system and a laboratory robot are combined to develop a fully automated cultivation platform. By making numerous modifications, as well as supplementation with custom-built hardware and software, fully automated milliliter-scale microbial fed-batch cultivation, sample handling, and data storage are realized. The initial results of cultivations with two different expression systems and three different process conditions are compared using 5 L scale benchmark cultivations, which provide identical rankings of expression systems and process conditions. Thus, fully automated high-throughput cultivation, including automated centralized data storage to significantly accelerate the identification of the optimal expression systems and process conditions, offers the potential for automated early-stage bioprocess development.

An automated and parallelised DIY-dosing unit for individual and complex feeding profiles: Construction, validation and applications

Since biotechnological research becomes more and more important for industrial applications, there is an increasing need for scalable and controllable laboratory procedures. A widely used approach in biotechnological research to improve the performance of a process is to vary the growth rates in order to find the right balance between growth and the production. This can be achieved by the application of a suitable feeding strategy. During this initial bioprocess development, it is beneficial to have at hand cheap and easy setups that work in parallel (e.g. in shaking flasks). Unfortunately, there is a gap between these easy setups and defined and controllable processes, which are necessary for up-scaling to an industrial relevant volume. One prerequisite to test and evaluate different process strategies apart from batch-mode is the availability of pump systems that allow for defined feeding profiles in shaking flasks. To our knowledge, there is no suitable dosing device on the market which fulfils the requirements of being cheap, precise, programmable, and parallelizable. Commercially available dosing units are either already integrated in bioreactors and therefore inflexible, or not programmable, or expensive, or a combination of those. Here, we present a LEGO-MINDSTORMS-based syringe pump, which has the potential of being widely used in daily laboratory routine due to its low price, programmability, and parallelisability. The acquisition costs do not exceed 350 € for up to four dosing units, that are independently controllable with one EV3 block. The system covers flow rates ranging from 0.7 μL min-1 up to 210 mL min-1 with a reliable flux. One dosing unit can convey at maximum a volume of 20 mL (using all 4 units even up to 80 mL in total) over the whole process time. The design of the dosing unit enables the user to perform experiments with up to four different growth rates in parallel (each measured in triplicates) per EV3-block used. We estimate, that the LEGO-MINDSTORMS-based dosing unit with 12 syringes in parallel is reducing the costs up to 50-fold compared to a trivial version of a commercial pump system (~1500 €) which fits the same requirements. Using the pump, we set the growth rates of a E. coli HMS174/DE3 culture to values between 0.1 and 0.4 h-1 with a standard deviation of at best 0.35% and an average discrepancy of 13.2%. Additionally, we determined the energy demand of a culture for the maintenance of the pTRA-51hd plasmid by quantifying the changes in biomass yield with different growth rates set. Around 25% of total substrate taken up is used for plasmid maintenance. To present possible applications and show the flexibility of the system, we applied a constant feed to perform microencapsulation of Pseudomonas putida and an individual dosing profile for the purification of a his-tagged eGFP via IMAC. This smart and versatile dosing unit, which is ready-to-use without any prior knowledge in electronics and control, is affordable for everyone and due to its flexibility and broad application range a valuable addition to the laboratory routine.

Accelerated Bioprocess Development of Endopolygalacturonase-Production with Saccharomyces cerevisiae Using Multivariate Prediction in a 48 Mini-Bioreactor Automated Platform

Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations are a promising alternative to accelerate and optimize bioprocess development allowing for sophisticated cultivation experiments in high throughput. These include fed-batch and continuous cultivations with multiple options of process control and sample analysis which deliver valuable screening tools for industrial production. However, the model-based methods needed to operate these robotic facilities efficiently considering the complexity of biological processes are missing. We present an automated experiment facility that integrates online data handling, visualization and treatment using multivariate analysis approaches to design and operate dynamical experimental campaigns in up to 48 mini-bioreactors (8⁻12 mL) in parallel. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed, running and comparing 16 experimental conditions in triplicate. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable to different strains and cultivation strategies, and suitable for automatized process development reducing the experimental times and costs.

Improved microscale cultivation of Pichia pastoris for clonal screening

Background

Expanding the application of technical enzymes, e.g., in industry and agriculture, commands the acceleration and cost-reduction of bioprocess development. Microplates and shake flasks are massively employed during screenings and early phases of bioprocess development, although major drawbacks such as low oxygen transfer rates are well documented. In recent years, miniaturization and parallelization of stirred and shaken bioreactor concepts have led to the development of novel microbioreactor concepts. They combine high cultivation throughput with reproducibility and scalability, and represent promising tools for bioprocess development.

Results

Parallelized microplate cultivation of the eukaryotic protein production host Pichia pastoris was applied effectively to support miniaturized phenotyping of clonal libraries in batch as well as fed-batch mode. By tailoring a chemically defined growth medium, we show that growth conditions are scalable from microliter to 0.8 L lab-scale bioreactor batch cultivation with different carbon sources. Thus, the set-up allows for a rapid physiological comparison and preselection of promising clones based on online data and simple offline analytics. This is exemplified by screening a clonal library of P. pastoris constitutively expressing AppA phytase from Escherichia coli. The protocol was further modified to establish carbon-limited conditions by employing enzymatic substrate-release to achieve screening conditions relevant for later protein production processes in fed-batch mode.

Conclusion

The comparison of clonal rankings under batch and fed-batch-like conditions emphasizes the necessity to perform screenings under process-relevant conditions. Increased biomass and product concentrations achieved after fed-batch microscale cultivation facilitates the selection of top producers. By reducing the demand to conduct laborious and cost-intensive lab-scale bioreactor cultivations during process development, this study will contribute to an accelerated development of protein production processes.

Electronic supplementary material

The online version of this article (10.1186/s40694-018-0053-6) contains supplementary material, which is available to authorized users.

Microbioreactor Systems for Accelerated Bioprocess Development

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

Systematic evaluation of characteristics of the membrane-based fed-batch shake flask

BACKGROUND

The initial part of process development involves extensive screening programs to identify optimal biological systems and cultivation conditions. For a successful scale-up, the operation mode on screening and production scale must be as close as possible. To enable screening under fed-batch conditions, the membrane-based fed-batch shake flask was developed. It is a shake flask mounted with a central feed reservoir with an integrated rotating membrane tip for a controlled substrate release. Building on the previously provided proof of principle for this tool, this work extends its application by constructive modifications and improved methodology to ensure reproducible performance.

RESULTS

The previously limited operation window was expanded by a systematic analysis of reservoir set-up variations for cultivations with the fast-growing organism Escherichia coli. Modifying the initial glucose concentration in the reservoir as well as interchanging the built-in membrane, resulted in glucose release rates and oxygen transfer rate levels during the fed-batch phase varying up to a factor of five. The range of utilizable membranes was extended from dialysis membranes to porous microfiltration membranes with the design of an appropriate membrane tip. The alteration of the membrane area, molecular weight cut-off and liquid volume in the reservoir offered additional parameters to fine-tune the duration of the initial batch phase, the oxygen transfer rate level of the fed-batch phase and the duration of feeding. It was shown that a homogeneous composition of the reservoir without a concentration gradient is ensured up to an initial glucose concentration of 750 g/L. Finally, the experimental validity of fed-batch shake flask cultivations was verified with comparable results obtained in a parallel fed-batch cultivation in a laboratory-scale stirred tank reactor.

CONCLUSIONS

The membrane-based fed-batch shake flask is a reliable tool for small-scale screening under fed-batch conditions filling the gap between microtiter plates and scaled-down stirred tank reactors. The implemented reservoir system offers various set-up possibilities, which provide a wide range of process settings for diverse biological systems. As a screening tool, it accurately reflects the cultivation conditions in a fed-batch stirred tank reactor and enables a more efficient bioprocess development.

Recent advances in high-throughput 13C-fluxomics

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT ¹³C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT ¹³C-fluxomics workflows.

Evaluation of fluorimetric pH sensors for bioprocess monitoring at low pH

Optical chemical sensors are the standard for pH monitoring in small-scale bioreactors such as microtiter plates, shaking flasks or other single-use bioreactors. The dynamic pH range of the so far commercially available fluorescent pH sensors applied in small-scale bioreactors is restricted to pH monitoring around neutral pH, although many fermentation processes are performed at pH < 6 on industrial scale. Thus, two new prototype acidic fluorescence pH sensors immobilized in single-use stirred-tank bioreactors, one with excitation at 470 nm and emission at 550 nm (sensor 470/550) and the other with excitation at 505 nm and emission at 600 nm (sensor 505/600), were characterized with respect to dynamic ranges and operational stability in representative fermentation media. Best resolution and dynamic range was observed with pH sensor 505/600 in mineral medium (dynamic range of 3.9 < pH < 7.2). Applying the same pH sensors to complex medium results in a drastic reduction of resolution and dynamic ranges. Yeast extract in complex medium was found to cause background fluorescence at the sensors' operating wavelength combinations. Optical isolation of the sensor by adding a black colored polymer layer above the sensor spot and fixing an aperture made of adhesive photoresistant foil between the fluorescence reader and the transparent bottom of the polystyrene reactors enabled full re-establishment of the sensor's characteristics. Reliability and operational stability of sensor 505/600 was shown by online pH monitoring (4.5 < pH < 5.8) of parallel anaerobic batch fermentations of Clostridium acetobutylicum for the production of acetone, butanol and ethanol (ABE) with offline pH measurements with a standard glass electrode as reference.