Microbioreactor Systems for Accelerated Bioprocess Development

PhagoScreener: A novel phagogram platform based on a capillary-wave microbioreactor

Due to the overuse of antibiotics, the number of multidrug-resistant pathogen bacteria is rising in recent years posing a serious threat to human health. One promising alternative for treatment is the application of phage therapy using highly selective bacteriophages. Because of their selectivity, individual screens called phagograms for each patient are required to select phages from a phage library. Phagograms are mostly performed via bacterial cultivation on double layer agar plates and phage addition causing bacterial lysis. However, these assays are work-intensive and have a low ability for parallelization and automation. Hence, highly parallelizable and automatable microbioreactors in the lowest microliter scale could offer an economic solution increasing the throughput of phagograms. This paper demonstrates the applicability of a novel capillary-wave microbioreactor (cwMBR) to perform phagograms. Due to its small volume of only 7 µL and the open-droplet design, it can be easily automated and parallelized in future. Furthermore, the ability of online biomass measurement makes the cwMBR a perfect phagogram platform in the future. Herein, phagograms with E. coli and different concentrations of the phages MM02 and EASG3 were performed as proof of concept for phagograms in the cwMBR. Thereby, the cwMBR was able to measure differences in lysis kinetics of different phages. Furthermore, the phagograms were compared to those in conventional microtiter plate readers revealing the cwMBR as ideal alternative for phagograms as it combines favorable mixing conditions and a phage repellent hydrophilic glass surface with online biomass measurement in an open-droplet design for future parallelization and automation.

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is, local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

2-Stage microfermentations

Cell based factories can be engineered to produce a wide variety of products. Advances in DNA synthesis and genome editing have greatly simplified the design and construction of these factories. It has never been easier to generate hundreds or even thousands of cell factory strain variants for evaluation. These advances have amplified the need for standardized, higher throughput means of evaluating these designs. Toward this goal, we have previously reported the development of engineered E. coli strains and associated 2-stage production processes to simplify and standardize strain engineering, evaluation and scale up. This approach relies on decoupling growth (stage 1), from production, which occurs in stationary phase (stage 2). Phosphate depletion is used as the trigger to stop growth as well as induce heterologous expression. Here, we describe in detail the development of protocols for the evaluation of engineered E. coli strains in 2-stage microfermentations. These protocols are readily adaptable to the evaluation of strains producing a wide variety of protein as well as small molecule products. Additionally, by detailing the approach to protocol development, these methods are also adaptable to additional cellular hosts, as well as other 2-stage processes with various additional triggers.

Thermal segment microwell plate control for automated liquid handling setups

Automated high-throughput liquid handling operations in biolabs necessitate miniaturised and automatised equipment for effective space utilisation and system integration. This paper presents a thermal segment microwell plate control unit designed for enhanced microwell-based experimentation in liquid handling setups. The development of this device stems from the need to move towards geometry standardization and system integration of automated lab equipment. It incorporates features based on Smart Sensor and Sensor 4.0 concepts. An enzymatic activity assay is implemented with the developed device on a liquid handling station, allowing fast characterisation via a high-throughput approach. The device outperforms other comparable devices in certain metrics based on automated liquid handling requirements and addresses the needs of future biolabs in automation, especially in high-throughput screening.

Optimizing microbioreactor cultivation strategies for Trichoderma reesei: from batch to fed-batch operations

BACKGROUND

Filamentous fungi have long been recognized for their exceptional enzyme production capabilities. Among these, Trichoderma reesei has emerged as a key producer of various industrially relevant enzymes and is particularly known for the production of cellulases. Despite the availability of advanced gene editing techniques for T. reesei, the cultivation and characterization of resulting strain libraries remain challenging, necessitating well-defined and controlled conditions with higher throughput. Small-scale cultivation devices are popular for screening bacterial strain libraries. However, their current use for filamentous fungi is limited due to their complex morphology.

RESULTS

This study addresses this research gap through the development of a batch cultivation protocol using a microbioreactor for cellulase-producing T. reesei strains (wild type, RutC30 and RutC30 TR3158) with offline cellulase activity analysis. Additionally, the feasibility of a microscale fed-batch cultivation workflow is explored, crucial for mimicking industrial cellulase production conditions. A batch cultivation protocol was developed and validated using the BioLector microbioreactor, a Round Well Plate, adapted medium and a shaking frequency of 1000 rpm. A strong correlation between scattered light intensity and cell dry weight underscores the reliability of this method in reflecting fungal biomass formation, even in the context of complex fungal morphology. Building on the batch results, a fed-batch strategy was established for T. reesei RutC30. Starting with a glucose concentration of 2.5 g l- 1 in the batch phase, we introduced a dual-purpose lactose feed to induce cellulase production and prevent carbon catabolite repression. Investigating lactose feeding rates from 0.3 to 0.75 g (l h)- 1 , the lowest rate of 0.3 g (l h)- 1 revealed a threefold increase in cellobiohydrolase and a fivefold increase in β -glucosidase activity compared to batch processes using the same type and amount of carbon sources.

CONCLUSION

We successfully established a robust microbioreactor batch cultivation protocol for T. reesei wild type, RutC30 and RutC30 TR3158, overcoming challenges associated with complex fungal morphologies. The study highlights the effectiveness of microbioreactor workflows in optimizing cellulase production with T. reesei, providing a valuable tool for simultaneous assessment of critical bioprocess parameters and facilitating efficient strain screening. The findings underscore the potential of microscale fed-batch strategies for enhancing enzyme production capabilities, revealing insights for future industrial applications in biotechnology.

Online monitoring of the respiration activity in 96-deep-well microtiter plate Chinese hamster ovary cultures streamlines kill curve experiments

Cell line generation of mammalian cells is a time-consuming and labor-intensive process, especially because of challenges in clone selection after transfection. Antibiotics are common selection agents for mammalian cells due to their simplicity of use. However, the optimal antibiotic concentration must be determined with a kill curve experiment before clone selection starts. The traditional kill curve experiments are resource-intensive and time-consuming due to necessary sampling and offline analysis effort. This study, thus, explores the potential of online monitoring the oxygen transfer rate (OTR), as a non-invasive and efficient alternative for kill curve experiments. The OTR is monitored using the Transfer-rate Online Measurement (TOM) system and the micro(μ)-scale Transfer-rate Online Measurement (μTOM) device, which was used for mammalian cells first. It could be shown that the OTR curves for both devices align perfectly, affirming consistent cultivation conditions. The μTOM device proves effective in performing kill curve experiments in 96-deep-well plates without the need for sampling and offline analysis. The streamlined approach reduces medium consumption by 95%, offering a cost-effective and time-efficient solution for kill curve experiments. The study validates the generalizability of the method by applying it to two different CHO cell lines (CHO-K1 and sciCHO) with two antibiotics (puromycin and hygromycin B) each. In conclusion, the broad application of OTR online monitoring for CHO cell cultures in 96-deep-well plates is highlighted. The μTOM device proves as a valuable tool for high-throughput experiments, paving the way for diverse applications, such as media and clone screening, cytotoxicity tests, and scale-up experiments.

Automated workflow for characterization of bacteriocin production in natural producers Lactococcus lactis and Latilactobacillus sakei

BACKGROUND

Lactic acid bacteria are commonly used as protective starter cultures in food products. Among their beneficial effects is the production of ribosomally synthesized peptides termed bacteriocins that kill or inhibit food-spoiling bacteria and pathogens, e.g., members of the Listeria species. As new bacteriocins and producer strains are being discovered rapidly, modern automated methods for strain evaluation and bioprocess development are required to accelerate screening and development processes.

RESULTS

In this study, we developed an automated workflow for screening and bioprocess optimization for bacteriocin producing lactic acid bacteria, consisting of microcultivation, sample processing and automated antimicrobial activity assay. We implemented sample processing workflows to minimize bacteriocin adsorption to producer cells via addition of Tween 80 and divalent cations to the cultivation media as well as acidification of culture broth prior to cell separation. Moreover, we demonstrated the applicability of the automated workflow to analyze influence of media components such as MES buffer or yeast extract for bacteriocin producers Lactococcus lactis B1629 and Latilactobacillus sakei A1608.

CONCLUSIONS

Our automated workflow provides advanced possibilities to accelerate screening and bioprocess optimization for natural bacteriocin producers. Based on its modular concept, adaptations for other strains, bacteriocin products and applications are easily carried out and a unique tool to support bacteriocin research and bioprocess development is provided.

"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization"

BACKGROUND

In recent years, the production of inclusion bodies that retain substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) are formed by genetic fusion of an aggregation-inducing tag to a gene of interest via short linker polypeptides. The resulting CatIBs are known for their easy and cost-efficient production, recyclability as well as their improved stability. Recent studies have outlined the cooperative effects of linker and aggregation-inducing tag on CatIB activities. However, no a priori prediction is possible so far to indicate the best combination thereof. Consequently, extensive screening is required to find the best performing CatIB variant.

RESULTS

In this work, a semi-automated cloning workflow was implemented and used for fast generation of 63 CatIB variants with glucose dehydrogenase of Bacillus subtilis (BsGDH). Furthermore, the variant BsGDH-PT-CBDCell was used to develop, optimize and validate an automated CatIB screening workflow, enhancing the analysis of many CatIB candidates in parallel. Compared to previous studies with CatIBs, important optimization steps include the exclusion of plate position effects in the BioLector by changing the cultivation temperature. For the overall workflow including strain construction, the manual workload could be reduced from 59 to 7 h for 48 variants (88%). After demonstration of high reproducibility with 1.9% relative standard deviation across 42 biological replicates, the workflow was performed in combination with a Bayesian process model and Thompson sampling. While the process model is crucial to derive key performance indicators of CatIBs, Thompson sampling serves as a strategy to balance exploitation and exploration in screening procedures. Our methodology allowed analysis of 63 BsGDH-CatIB variants within only three batch experiments. Because of the high likelihood of TDoT-PT-BsGDH being the best CatIB performer, it was selected in 50 biological replicates during the three screening rounds, much more than other, low-performing variants.

CONCLUSIONS

At the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings.

Scale-down of CHO cell cultivation from shake flasks based on oxygen mass transfer allows application of parallelized, non-invasive, and time-resolved monitoring of the oxygen transfer rate in 48-well microtiter plates

Cultivating Chinese hamster ovary (CHO) cells in microtiter plates (MTPs) with time-resolved monitoring of the oxygen transfer rate (OTR) is highly desirable to provide process insights at increased throughput. However, monitoring of the OTR in MTPs has not been demonstrated for CHO cells, yet. Hence, a CHO cultivation process was transferred from shake flasks to MTPs to enable monitoring of the OTR in each individual well of a 48-well MTP. For this, the cultivation of an industrially relevant, antibody-producing cell line was transferred from shake flask to MTP based on the volumetric oxygen mass transfer coefficient (kL a). Culture behavior was well comparable (deviation of the final IgG titer less than 10%). Monitoring of the OTR in 48-well MTPs was then used to derive the cytotoxicity of dimethyl sulfoxide (DMSO) based on a dose-response curve in a single experiment using a second CHO cell line. Logistic fitting of the dose-response curve determined after 100 h was used to determine the DMSO concentration that resulted in a cytotoxicity of 50% (IC50). A DMSO concentration of 2.70% ± 0.25% was determined, which agrees with the IC50 previously determined in shake flasks (2.39% ± 0.1%). Non-invasive, parallelized, and time-resolved monitoring of the OTR of CHO cells in MTPs was demonstrated and offers excellent potential to speed up process development and assess cytotoxicity.

Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations

Robotic facilities that can perform advanced cultivations (e.g., fed-batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation for the implementation of feedback control methods in miniaturized bioreactor systems, where observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma-Point Kalman Filter in a high throughput platform with 24 parallel experiments at the mL-scale to demonstrate its viability and added value in high throughput experiments. The filter exploits the information generated by the ammonia-based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.

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.

Engineered Escherichia coli platforms for tyrosine-derivative production from phenylalanine using phenylalanine hydroxylase and tetrahydrobiopterin-regeneration system

BACKGROUND

Aromatic compounds derived from tyrosine are important and diverse chemicals that have industrial and commercial applications. Although these aromatic compounds can be obtained by extraction from natural producers, their growth is slow, and their content is low. To overcome these problems, many of them have been chemically synthesized from petroleum-based feedstocks. However, because of the environmental burden and depleting availability of feedstock, microbial cell factories are attracting much attention as sustainable and environmentally friendly processes.

RESULTS

To facilitate development of microbial cell factories for producing tyrosine derivatives, we developed simple and convenient tyrosine-producing Escherichia coli platforms with a bacterial phenylalanine hydroxylase, which converted phenylalanine to tyrosine with tetrahydromonapterin as a cofactor, using a synthetic biology approach. By introducing a tetrahydrobiopterin-regeneration system, the tyrosine titer of the plasmid-based engineered strain was 4.63 g/L in a medium supplemented with 5.00 g/L phenylalanine with a test tube. The strains were successfully used to produce industrially attractive compounds, such as tyrosol with a yield of 1.58 g/L by installing a tyrosol-producing module consisting of genes encoding tyrosine decarboxylase and tyramine oxidase on a plasmid. Gene integration into E. coli chromosomes has an advantage over the use of plasmids because it increases genetic stability without antibiotic feeding to the culture media and enables more flexible pathway engineering by accepting more plasmids with artificial pathway genes. Therefore, we constructed a plasmid-free tyrosine-producing platform by integrating five modules, comprising genes encoding the phenylalanine hydroxylase and tetrahydrobiopterin-regeneration system, into the chromosome. The platform strain could produce 1.04 g/L of 3,4-dihydroxyphenylalanine, a drug medicine, by installing a gene encoding tyrosine hydroxylase and the tetrahydrobiopterin-regeneration system on a plasmid. Moreover, by installing the tyrosol-producing module, tyrosol was produced with a yield of 1.28 g/L.

CONCLUSIONS

We developed novel E. coli platforms for producing tyrosine from phenylalanine at multi-gram-per-liter levels in test-tube cultivation. The platforms allowed development and evaluation of microbial cell factories installing various designed tyrosine-derivative biosynthetic pathways at multi-grams-per-liter levels in test tubes.

From frozen cell bank to product assay: high-throughput strain characterisation for autonomous Design-Build-Test-Learn cycles

BACKGROUND

Modern genome editing enables rapid construction of genetic variants, which are further developed in Design-Build-Test-Learn cycles. To operate such cycles in high throughput, fully automated screening, including cultivation and analytics, is crucial in the Test phase. Here, we present the required steps to meet these demands, resulting in an automated microbioreactor platform that facilitates autonomous phenotyping from cryo culture to product assay.

RESULTS

First, an automated deep freezer was integrated into the robotic platform to provide working cell banks at all times. A mobile cart allows flexible docking of the freezer to multiple platforms. Next, precultures were integrated within the microtiter plate for cultivation, resulting in highly reproducible main cultures as demonstrated for Corynebacterium glutamicum. To avoid manual exchange of microtiter plates after cultivation, two clean-in-place strategies were established and validated, resulting in restored sterile conditions within two hours. Combined with the previous steps, these changes enable a flexible start of experiments and greatly increase the walk-away time.

CONCLUSIONS

Overall, this work demonstrates the capability of our microbioreactor platform to perform autonomous, consecutive cultivation and phenotyping experiments. As highlighted in a case study of cutinase-secreting strains of C. glutamicum, the new procedure allows for flexible experimentation without human interaction while maintaining high reproducibility in early-stage screening processes.

Model-Based Characterization of E. coli Strains with Impaired Glucose Uptake

The bacterium Escherichia coli is a widely used organism in biotechnology. For high space-time yields, glucose-limited fed-batch technology is the industry standard; this is because an overflow metabolism of acetate occurs at high glucose concentrations. As an interesting alternative, various strains with limited glucose uptake have been developed. However, these have not yet been characterized under process conditions. To demonstrate the efficiency of our previously developed high-throughput robotic platform, in the present work, we characterized three different exemplary E. coli knockout (KO) strains with limited glucose uptake capacities at three different scales (microtiter plates, 10 mL bioreactor system and 100 mL bioreactor system) under excess glucose conditions with different initial glucose concentrations. The extensive measurements of growth behavior, substrate consumption, respiration, and overflow metabolism were then used to determine the appropriate growth parameters using a mechanistic mathematical model, which allowed for a comprehensive comparative analysis of the strains. The analysis was performed coherently with these different reactor configurations and the results could be successfully transferred from one platform to another. Single and double KO mutants showed reduced specific rates for substrate uptake q_Smax and acetate production q_Apmax; meanwhile, higher glucose concentrations had adverse effects on the biomass yield coefficient Y_XSem. Additional parameters compared to previous studies for the oxygen uptake rate and carbon dioxide production rate indicated differences in the specific oxygen uptake rate q_Omax. This study is an example of how automated robotic equipment, together with mathematical model-based approaches, can be successfully used to characterize strains and obtain comprehensive information more quickly, with a trade-off between throughput and analytical capacity.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Central carbon metabolite profiling reveals vector-associated differences in the recombinant protein production host Escherichia coli BL21

The Gram-negative bacterium Escherichia coli is the most widely used host for recombinant protein production, both as an industrial expression platform and as a model system at laboratory scale. The recombinant protein production industry generates proteins with direct applications as biopharmaceuticals and in technological processes central to a plethora of fields. Despite the increasing economic significance of recombinant protein production, and the importance of E. coli as an expression platform and model organism, only few studies have focused on the central carbon metabolic landscape of E. coli during high-level recombinant protein production. In the present work, we applied four targeted CapIC- and LC-MS/MS methods, covering over 60 metabolites, to perform an in-depth metabolite profiling of the effects of high-level recombinant protein production in strains derived from E. coli BL21, carrying XylS/Pm vectors with different characteristics. The mass-spectrometric central carbon metabolite profiling was complemented with the study of growth kinetics and protein production in batch bioreactors. Our work shows the robustness in E. coli central carbon metabolism when introducing increased plasmid copy number, as well as the greater importance of induction of recombinant protein production as a metabolic challenge, especially when strong promoters are used.

Black glucose-releasing silicon elastomer rings for fed-batch operation allow measurement of the oxygen transfer rate from the top and optical signals from the bottom for each well of a microtiter plate

BACKGROUND

In industrial microbial biotechnology, fed-batch processes are frequently used to avoid undesirable biological phenomena, such as substrate inhibition or overflow metabolism. For targeted process development, fed-batch options for small scale and high throughput are needed. One commercially available fed-batch fermentation system is the FeedPlate^®, a microtiter plate (MTP) with a polymer-based controlled release system. Despite standardisation and easy incorporation into existing MTP handling systems, FeedPlates^® cannot be used with online monitoring systems that measure optically through the transparent bottom of the plate. One such system that is broadly used in biotechnological laboratories, is the commercial BioLector. To allow for BioLector measurements, while applying the polymer-based feeding technology, positioning of polymer rings instead of polymer disks at the bottom of the well has been proposed. This strategy has a drawback: measurement requires an adjustment of the software settings of the BioLector device. This adjustment modifies the measuring position relative to the wells, so that the light path is no longer blocked by the polymer ring, but, traverses through the inner hole of the ring. This study aimed at overcoming that obstacle and allowing for measurement of fed-batch cultivations using a commercial BioLector without adjustment of the relative measurement position within each well.

RESULTS

Different polymer ring heights, colours and positions in the wells were investigated for their influence on maximum oxygen transfer capacity, mixing time and scattered light measurement. Several configurations of black polymer rings were identified that allow measurement in an unmodified, commercial BioLector, comparable to wells without rings. Fed-batch experiments with black polymer rings with two model organisms, E. coli and H. polymorpha, were conducted. The identified ring configurations allowed for successful cultivations, measuring the oxygen transfer rate and dissolved oxygen tension, pH, scattered light and fluorescence. Using the obtained online data, glucose release rates of 0.36 to 0.44 mg/h could be determined. They are comparable to formerly published data of the polymer matrix.

CONCLUSION

The final ring configurations allow for measurements of microbial fed-batch cultivations using a commercial BioLector without requiring adjustments of the instrumental measurement setup. Different ring configurations achieve similar glucose release rates. Measurements from above and below the plate are possible and comparable to measurements of wells without polymer rings. This technology enables the generation of a comprehensive process understanding and target-oriented process development for industrial fed-batch processes.

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

BACKGROUND

Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided.

RESULTS

In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation.

CONCLUSION

For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.

High-Throughput Expression of Inclusion Bodies on an Automated Platform

In bioprocesses, which target the production of recombinant proteins as inclusion bodies, the upstream process has a decisive influence on the downstream operations, especially regarding cell disruption, inclusion body purity and composition, and refolding yield. Therefore, optimization of the processes in fed-batch mode is a major issue, and screening for strains and process conditions are performed in highly labor, time and cost intensive shake flasks or multiwell plates. Thus, high-throughput experiments performed similar to the industrial operating conditions offer a possibility to develop efficient and robust upstream processes. We present here an automated platform for Escherichia coli fed-batch cultivations in parallelized minibioreactors. The platform allows execution of experiments under multiple conditions while allowing for real-time monitoring of critical process parameters and a controlled fermentation environment. By this, the main factors that affect yields and quality of inclusion bodies can be investigated, speeding up the development process significantly.

Development and Validation of an Artificial Neural-Network-Based Optical Density Soft Sensor for a High-Throughput Fermentation System

Optical density (OD) is a critical process parameter during fermentation, this being directly related to cell density, which provides valuable information regarding the state of the process. However, to measure OD, sampling of the fermentation broth is required. This is particularly challenging for high-throughput-microbioreactor (HT-MBR) systems, which require robotic liquid-handling (LiHa) systems for process control tasks, such as pH regulation or carbon feed additions. Bioreactor volume is limited and automated at-line sampling occupies the resources of LiHa systems; this affects their ability to carry out the aforementioned pipetting operations. Minimizing the number of physical OD measurements is therefore of significant interest. However, fewer measurements also result in less process information. This resource conflict has previously represented a challenge. We present an artificial neural-network-based soft sensor developed for the real-time estimation of the OD in an MBR system. This sensor was able to estimate the OD to a high degree of accuracy (>95%), even without informative process variables stemming from, e.g., off-gas analysis only available at larger scales. Furthermore, we investigated and demonstrated scaling of the soft sensor’s generalization capabilities with the data from different antibody fragments expressing Escherichia coli strains. This study contributes to accelerated biopharmaceutical process development.

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.

Marine Bioprospecting, Biocatalysis and Process Development

Oceans possess tremendous diversity in microbial life. The enzymatic machinery that marine bacteria present is the result of extensive evolution to assist cell survival under the harsh and continuously changing conditions found in the marine environment. Several bacterial cells and enzymes are already used at an industrial scale, but novel biocatalysts are still needed for sustainable industrial applications, with benefits for both public health and the environment. Metagenomic techniques have enabled the discovery of novel biocatalysts, biosynthetic pathways, and microbial identification without their cultivation. However, a key stage for application of novel biocatalysts is the need for rapid evaluation of the feasibility of the bioprocess. Cultivation of not-yet-cultured bacteria is challenging and requires new methodologies to enable growth of the bacteria present in collected environmental samples, but, once a bacterium is isolated, its enzyme activities are easily measured. High-throughput screening techniques have also been used successfully, and innovative in vitro screening platforms to rapidly identify relevant enzymatic activities continue to improve. Small-scale approaches and process integration could improve the study and development of new bioprocesses to produce commercially interesting products. In this work, the latest studies related to (i) the growth of marine bacteria under laboratorial conditions, (ii) screening techniques for bioprospecting, and (iii) bioprocess development using microreactors and miniaturized systems are reviewed and discussed.

Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations of Hansenula polymorpha

Multi-wavelength (2D) fluorescence spectroscopy represents an important step towards exploiting the monitoring potential of microtiter plates (MTPs) during early-stage bioprocess development. In combination with multivariate data analysis (MVDA), important process information can be obtained, while repetitive, cost-intensive sample analytics can be reduced. This study provides a comprehensive experimental dataset of online and offline measurements for batch cultures of Hansenula polymorpha. In the first step, principal component analysis (PCA) was used to assess spectral data quality. Secondly, partial least-squares (PLS) regression models were generated, based on spectral data of two cultivation conditions and offline samples for glycerol, cell dry weight, and pH value. Thereby, the time-wise resolution increased 12-fold compared to the offline sampling interval of 6 h. The PLS models were validated using offline samples of a shorter sampling interval. Very good model transferability was shown during the PLS model application to the spectral data of cultures with six varying initial cultivation conditions. For all the predicted variables, a relative root-mean-square error (RMSE) below 6% was obtained. Based on the findings, the initial experimental strategy was re-evaluated and a more practical approach with minimised sampling effort and elevated experimental throughput was proposed. In conclusion, the study underlines the high potential of multi-wavelength (2D) fluorescence spectroscopy and provides an evaluation workflow for PLS modelling in microtiter plates.

Microsensor in Microbioreactors: Full Bioprocess Characterization in a Novel Capillary-Wave Microbioreactor

Microbioreactors (MBRs) with a volume below 1 mL are promising alternatives to established cultivation platforms such as shake flasks, lab-scale bioreactors and microtiter plates. Their main advantages are simple automatization and parallelization and the saving of expensive media components and test substances. These advantages are particularly pronounced in small-scale MBRs with a volume below 10 µL. However, most described small-scale MBRs are lacking in process information from integrated sensors due to limited space and sensor technology. Therefore, a novel capillary-wave microbioreactor (cwMBR) with a volume of only 7 µL has the potential to close this gap, as it combines a small volume with integrated sensors for biomass, pH, dissolved oxygen (DO) and glucose concentration. In the cwMBR, pH and DO are measured by established luminescent optical sensors on the bottom of the cwMBR. The novel glucose sensor is based on a modified oxygen sensor, which measures the oxygen uptake of glucose oxidase (GOx) in the presence of glucose up to a concentration of 15 mM. Furthermore, absorbance measurement allows biomass determination. The optical sensors enabled the characterization of an Escherichia coli batch cultivation over 8 h in the cwMBR as proof of concept for further bioprocesses. Hence, the cwMBR with integrated optical sensors has the potential for a wide range of microscale bioprocesses, including cell-based assays, screening applications and process development.

Accelerated strain construction and characterization of C. glutamicum protein secretion by laboratory automation

Secretion of bacterial proteins into the culture medium simplifies downstream processing by avoiding cell disruption for target protein purification. However, a suitable signal peptide for efficient secretion needs to be identified, and currently, there are no tools available to predict optimal combinations of signal peptides and target proteins. The selection of such a combination is influenced by several factors, including protein biosynthesis efficiency and cultivation conditions, which both can have a significant impact on secretion performance. As a result, a large number of combinations must be tested. Therefore, we have developed automated workflows allowing for targeted strain construction and secretion screening using two platforms. Key advantages of this experimental setup include lowered hands-on time and increased throughput. In this study, the automated workflows were established for the heterologous production of Fusarium solani f. sp. pisi cutinase in Corynebacterium glutamicum. The target protein was monitored in culture supernatants via enzymatic activity and split GFP assay. Varying spacer lengths between the Shine-Dalgarno sequence and the start codon of Bacillus subtilis signal peptides were tested. Consistent with previous work on the secretory cutinase production in B. subtilis, a ribosome binding site with extended spacer length to up to 12 nt, which likely slows down translation initiation, does not necessarily lead to poorer cutinase secretion by C. glutamicum. The best performing signal peptides for cutinase secretion with a standard spacer length were identified in a signal peptide screening. Additional insights into the secretion process were gained by monitoring secretion stress using the C. glutamicum K9 biosensor strain. KEY POINTS: • Automated workflows for strain construction and screening of protein secretion • Comparison of spacer, signal peptide, and host combinations for cutinase secretion • Signal peptide screening for secretion by C. glutamicum using the split GFP assay.

Implementation of Perforated Concentric Ring Walls Considerably Improves Gas-Liquid Mass Transfer of Shaken Bioreactors

Since their first use in the 1930s, shake flasks have been a widely used bioreactor type for screening and process development due to a number of advantages. However, the limited gas-liquid mass transfer capacities—resulting from practical operation limits regarding shaking frequency and filling volumes—are a major drawback. The common way to increase the gas-liquid mass transfer in shake flasks with the implementation of baffles is generally not recommended as it comes along with several severe disadvantages. Thus, a new design principle for shaken bioreactors that aims for improving the gas-liquid mass transfer without losing the positive characteristics of unbaffled shake flasks is introduced. The flasks consist of cylindrical glass vessels with implemented perforated concentric ring walls. The ring walls improve the gas-liquid mass transfer via the formation of additional liquid films on both of its sides, whereas the perforations allow for mixing between the compartments. Sulfite oxidation experiments revealed over 200% higher maximum oxygen transfer capacities (OTR_max) compared to conventional shake flasks. In batch cultivations of Escherichia coli BL21 in mineral media, unlimited growth until glucose depletion and oxygen transfer rates (OTR) of up to 138 mmol/L/h instead of an oxygen limitation at 57 mmol/L/h as in normal shake flasks under comparable conditions could be achieved. Even overflow metabolism could be prevented due to sufficient oxygen supply without the use of unconventional shaking conditions or oxygen enrichment. Therefore, we believe that the new perforated ring flask principle has a high potential to considerably improve biotechnological screening and process development steps.

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.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

bletl - A Python package for integrating BioLector microcultivation devices in the Design-Build-Test-Learn cycle

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rateμ ⃗ t based on unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switch-points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Device for respiration activity measurement enables the determination of oxygen transfer rates of microbial cultures in shaken 96-deepwell microtiter plates

Mini-bioreactors with integrated online monitoring capabilities are well established in the early stages of process development. Mini-bioreactors fulfil the demand for high-throughput-applications and a simultaneous reduction of material costs and total experimental time. One of the most essential online monitored parameters is the oxygen transfer rate (OTR). OTR-monitoring allows fast characterization of bioprocesses and process transfer to larger scales. Currently, OTR-monitoring on a small-scale is limited to shake flasks and 48-well microtiter plates (MTP). Especially, 96-deepwell MTP are used for high-throughput-experiments during early-stage bioprocess development. However, a device for OTR monitoring in 96-deepwell MTP is still not available. To determine OTR values, the measurement of the gas composition in each well of a MTP is necessary. Therefore, a new micro(µ)-scale Transfer rate Online Measurement device (µTOM) was developed. The µTOM includes 96 parallel oxygen-sensitive sensors and a single robust sealing mechanism. Different organisms (Escherichia. coli, Hansenula polymorpha, and Ustilago maydis) were cultivated in the µTOM. The measurement precision for 96 parallel cultivations was 0.21 mmol·L-1·h-1 (pooled standard deviation). In total, a more than 15-fold increase in throughput and an up to a 50-fold decrease in media consumption, compared with the shake flask RAMOS-technology, was achieved using the µTOM for OTR-monitoring. This article is protected by copyright. All rights reserved.

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.

Growth and eGFP Production of CHO-K1 Suspension Cells Cultivated From Single Cell to Laboratory Scale

Scaling down bioproduction processes has become a major driving force for more accelerated and efficient process development over the last decades. Especially expensive and time-consuming processes like the production of biopharmaceuticals with mammalian cell lines benefit clearly from miniaturization, due to higher parallelization and increased insights while at the same time decreasing experimental time and costs. Lately, novel microfluidic methods have been developed, especially microfluidic single-cell cultivation (MSCC) devices have been proved to be valuable to miniaturize the cultivation of mammalian cells. So far, growth characteristics of microfluidic cultivated cell lines were not systematically compared to larger cultivation scales; however, validation of a miniaturization tool against initial cultivation scales is mandatory to prove its applicability for bioprocess development. Here, we systematically investigate growth, morphology, and eGFP production of CHO-K1 cells in different cultivation scales ranging from a microfluidic chip (230 nl) to a shake flask (125 ml) and laboratory-scale stirred tank bioreactor (2.0 L). Our study shows a high comparability regarding specific growth rates, cellular diameters, and eGFP production, which proves the feasibility of MSCC as a miniaturized cultivation tool for mammalian cell culture. In addition, we demonstrate that MSCC provides insights into cellular heterogeneity and single-cell dynamics concerning growth and production behavior which, when occurring in bioproduction processes, might severely affect process robustness.

Definitive screening accelerates Taxol biosynthetic pathway optimization and scale up in Saccharomyces cerevisiae cell factories

BACKGROUND

Recent technological advancements in synthetic and systems biology have enabled the construction of microbial cell factories expressing diverse heterologous pathways in unprecedentedly short time scales. However, the translation of such laboratory scale breakthroughs to industrial bioprocesses remains a major bottleneck.

METHODS AND MAJOR RESULTS

In this study, an accelerated bioprocess development approach was employed to optimize the biosynthetic pathway of the blockbuster chemotherapy drug, Taxol. Statistical design of experiments approaches were coupled with an industrially relevant high-throughput microbioreactor system to optimize production of key Taxol intermediates, Taxadien-5α-ol and Taxadien-5α-yl-acetate, in engineered yeast cell factories. The optimal factor combination was determined via data driven statistical modelling and validated in 1 L bioreactors leading to a 2.1-fold improvement in taxane production compared to a typical defined media. Elucidation and mitigation of nutrient limitation enhanced product titers a further two-fold and titers of the critical Taxol precursors, Taxadien-5α-ol and Taxadien-5α-yl-acetate were improved to 34 and 11 mg L^-1 , representing a three-fold improvement compared to the highest literature titers in S. cerevisiae. Comparable titers were obtained when the process was scaled up a further five-fold using 5 L bioreactors.

CONCLUSIONS

The results of this study highlight the benefits of a holistic design of experiments guided approach to expedite early stage bioprocess development.

Equal mixing time enables scale-down and optimization of a CHO cell culture process using a shaken microbioreactor system

The advancement of microbioreactor technology in recent years has transformed early- and mid-stage process development. The monitoring and control capabilities of microbioreactors not only promote the quick accumulation of process knowledge but has also led to an increased scalability when compared to traditionally used systems such as shake flasks and microtitre plates. This study seeks to establish a framework for the micro-Matrix microbioreactor (Applikon-Biotechnology BV) as process development tool. Using the Dual Indicator System for Mixing Time, the system was initially characterized for mixing properties at varying operating conditions, which was found to yield mixing times between 0.9 and 41.8 s. A matched mixing time was proposed as scale-down criterion for an IgG4 producing GS-CHO fed-batch process between a 5 L stirred tank reactor (STR) and the micro-Matrix microbioreactor. Growth trends, maximum viable cell concentrations, final titre, and glycoprofiles were nearly identical at both scales. The scale-down model was then employed to optimize a bolus feeding regime using response surface methodology, which led to a 25.4% increase of the space-time yield and a 25% increase of the final titre. The optimized feeding strategy was validated at the small-scale and successfully scaled up to the 5 L STR. This work for the first time provides a framework of how the micro-Matrix microbioreactor can be implemented in a bioprocess development workflow and demonstrates scalability of growth and production kinetics as well as IgG4 glycosylation between the micro-Matrix and a benchtop-scale STR system.

Lab on a Chip: Bioreactors miniaturization for rapid optimization of biomedical processes and its impact on SARS-CoV-2 diagnosis

Lab on a Chip (LoC) as part of Microbioreactors (MBRs) constitute an emergent technology to carry out micro-bioprocesses based on microfluidics research. In this review, the usefulness of LoCs is exposed since its inception, demonstrating that it is a multidisciplinary research field, gathering different science branches to develop this technology. As a result, a beneficial point of advancement is reached, producing useful consumables for humanity. Some of the described LoCs throughout this work are also used to detect infectious diseases caused by bacteria or viruses, allowing accelerated studies on emerging or high-impact diseases, such as COVID-19. Here are also displayed with an updated panorama, different strategies to improve the use, applications in the biomedical field, and spread of these devices aimed at their availability to solve social problems.

Microbioreactor (micro-Matrix) potential in aerobic and anaerobic conditions with different industrially relevant microbial strains

Microscale fermentation systems are important high throughput tools in clone selection, and bioprocess set up and optimization, since they provide several parallel experiments in controlled conditions of pH, temperature, agitation, and gas flow rate. In this work we evaluated the performance of biotechnologically relevant strains with different respiratory requirements in the micro‐Matrix microbioreactor. In particular Escherichia coli K4 requires well aerated fermentation conditions to improve its native production of chondroitin‐like capsular polysaccharide, a biomedically attractive polymer. Results from batch and fed‐batch experiments demonstrated high reproducibility with those obtained on 2 L reactors, although highlighting a pronounced volume loss for longer‐term experiments. Basfia succiniciproducens and Actinobacillus succinogenes need CO₂ addition for the production of succinic acid, a building block with several industrial applications. Different CO₂ supply modes were tested for the two strains in 24 h batch experiments and results well compared with those obtained on lab‐scale bioreactors. Overall, it was demonstrated that the micro‐Matrix is a useful scale‐down tool that is suitable for growing metabolically different strains in simple batch process, however, a series of issues should still be addressed in order to fully exploit its potential.

Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis

Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.

Need for speed: evaluation of dilute and shoot-mass spectrometry for accelerated metabolic phenotyping in bioprocess development

With the utilization of small-scale and highly parallelized cultivation platforms embedded in laboratory robotics, microbial phenotyping and bioprocess development have been substantially accelerated, thus generating a bottleneck in bioanalytical bioprocess sample analytics. While microscale cultivation platforms allow the monitoring of typical process parameters, only limited information about product and by-product formation is provided without comprehensive analytics. The use of liquid chromatography mass spectrometry can provide such a comprehensive and quantitative insight, but is often limited by analysis runtime and throughput. In this study, we developed and evaluated six methods for amino acid quantification based on two strong cation exchanger columns and a dilute and shoot approach in hyphenation with either a triple-quadrupole or a quadrupole time-of-flight mass spectrometer. Isotope dilution mass spectrometry with 13C15N labeled amino acids was used to correct for matrix effects. The versatility of the methods for metabolite profiling studies of microbial cultivation supernatants is confirmed by a detailed method validation study. The methods using chromatography columns showed a linear range of approx. 4 orders of magnitude, sufficient response factors, and low quantification limits (7-443 nM) for single analytes. Overall, relative standard deviation was comparable for all analytes, with < 8% and < 11% for unbuffered and buffered media, respectively. The dilute and shoot methods with an analysis time of 1 min provided similar performance but showed a factor of up to 35 times higher throughput. The performance and applicability of the dilute and shoot method are demonstrated using a library of Corynebacterium glutamicum strains producing L-histidine, obtained from random mutagenesis, which were cultivated in a microscale cultivation platform.

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.

Engineering interventions in enzyme production: Lab to industrial scale

Commercial enzyme production has gained popularity due to its extensive applications in traditional and modern industrial sectors. Rigorous research activities are being conducted worldwide to make the enzyme production system more efficient, cost-effective and hence, sustainable. To overcome the lacunae in earlier enzyme production methods, new engineering interventions are being introduced to meet the growing demand for industrial enzymes. This review focuses initially on the current global scenario of the enzyme market followed by a discussion on different bioreactor design approaches. The use of novel membrane based, airlift and reciprocating plate bioreactors along with the emergence of micro-reactors have also been discussed. Further, the review covers different modelling and optimization strategies for the enzyme production process including advanced techniques like neural networks, adaptive neuro-fuzzy inference systems and genetic algorithms. Finally, the required thrust areas in the enzyme production sector have been highlighted with directions for future research.

3D-printed micro bubble column reactor with integrated microsensors for biotechnological applications: From design to evaluation

With the technological advances in 3D printing technology, which are associated with ever-increasing printing resolution, additive manufacturing is now increasingly being used for rapid manufacturing of complex devices including microsystems development for laboratory applications. Personalized experimental devices or entire bioreactors of high complexity can be manufactured within few hours from start to finish. This study presents a customized 3D-printed micro bubble column reactor (3D-µBCR), which can be used for the cultivation of microorganisms (e.g., Saccharomyces cerevisiae) and allows online-monitoring of process parameters through integrated microsensor technology. The modular 3D-µBCR achieves rapid homogenization in less than 1 s and high oxygen transfer with k_La values up to 788 h⁻¹ and is able to monitor biomass, pH, and DOT in the fluid phase, as well as CO₂ and O₂ in the gas phase. By extensive comparison of different reactor designs, the influence of the geometry on the resulting hydrodynamics was investigated. In order to quantify local flow patterns in the fluid, a three-dimensional and transient multiphase Computational Fluid Dynamics model was successfully developed and applied. The presented 3D-µBCR shows enormous potential for experimental parallelization and enables a high level of flexibility in reactor design, which can support versatile process development.

Microbioreactor-assisted cultivation workflows for time-efficient phenotyping of protein producing Aspergillus niger in batch and fed-batch mode

In recent years, many fungal genomes have become publicly available. In combination with novel gene editing tools, this allows for accelerated strain construction, making filamentous fungi even more interesting for the production of valuable products. However, besides their extraordinary production and secretion capacities, fungi most often exhibit challenging morphologies, which need to be screened for the best operational window. Thereby, combining genetic diversity with various environmental parameters results in a large parameter space, creating a strong demand for time-efficient phenotyping technologies. Microbioreactor systems, which have been well established for bacterial organisms, enable an increased cultivation throughput via parallelization and miniaturization, as well as enhanced process insight via non-invasive online monitoring. Nevertheless, only few reports about microtiter plate cultivation for filamentous fungi in general and even less with online monitoring exist in literature. Moreover, screening under batch conditions in microscale, when a fed-batch process is performed in large-scale might even lead to the wrong identification of optimized parameters. Therefore, in this study a novel workflow for Aspergillus niger was developed, allowing for up to 48 parallel microbioreactor cultivations in batch as well as fed-batch mode. This workflow was validated against lab-scale bioreactor cultivations to proof scalability. With the optimized cultivation protocol, three different micro-scale fed-batch strategies were tested to identify the best protein production conditions for intracellular model product GFP. Subsequently, the best feeding strategy was again validated in a lab-scale bioreactor.

Enabling high-throughput biology with flexible open-source automation

Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand-pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open-source Python platform that can execute complex pipetting patterns required for custom high-throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log-phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real-time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology.

Screening for optimal protease producing Bacillus licheniformis strains with polymer-based controlled-release fed-batch microtiter plates

Background

Substrate-limited fed-batch conditions have the favorable effect of preventing overflow metabolism, catabolite repression, oxygen limitation or inhibition caused by elevated substrate or osmotic concentrations. Due to these favorable effects, fed-batch mode is predominantly used in industrial production processes. In contrast, screening processes are usually performed in microtiter plates operated in batch mode. This leads to a different physiological state of the production organism in early screening and can misguide the selection of potential production strains. To close the gap between screening and production conditions, new techniques to enable fed-batch mode in microtiter plates have been described. One of these systems is the ready-to-use and disposable polymer-based controlled-release fed-batch microtiter plate (fed-batch MTP). In this work, the fed-batch MTP was applied to establish a glucose-limited fed-batch screening procedure for industrially relevant protease producing Bacillus licheniformis strains.

Results

To achieve equal initial growth conditions for different clones with the fed-batch MTP, a two-step batch preculture procedure was developed. Based on this preculture procedure, the standard deviation of the protease activity of glucose-limited fed-batch main culture cultivations in the fed-batch MTP was ± 10%. The determination of the number of replicates revealed that a minimum of 6 parallel cultivations were necessary to identify clones with a statistically significant increased or decreased protease activity. The developed glucose-limited fed-batch screening procedure was applied to 13 industrially-relevant clones from two B. licheniformis strain lineages. It was found that 12 out of 13 clones (92%) were classified similarly as in a lab-scale fed-batch fermenter process operated under glucose-limited conditions. When the microtiter plate screening process was performed in batch mode, only 5 out of 13 clones (38%) were classified similarly as in the lab-scale fed-batch fermenter process.

Conclusion

The glucose-limited fed-batch screening process outperformed the usual batch screening process in terms of the predictability of the clone performance under glucose-limited fed-batch fermenter conditions. These results highlight that the implementation of glucose-limited fed-batch conditions already in microtiter plate scale is crucial to increase the precision of identifying improved protease producing B. licheniformis strains. Hence, the fed-batch MTP represents an efficient high-throughput screening tool that aims at closing the gap between screening and production conditions.

High-throughput microbioreactor provides a capable tool for early stage bioprocess development

Tremendous advancements in cell and protein engineering methodologies and bioinformatics have led to a vast increase in bacterial production clones and recombinant protein variants to be screened and evaluated. Consequently, an urgent need exists for efficient high-throughput (HTP) screening approaches to improve the efficiency in early process development as a basis to speed-up all subsequent steps in the course of process design and engineering. In this study, we selected the BioLector micro-bioreactor (µ-bioreactor) system as an HTP cultivation platform to screen E. coli expression clones producing representative protein candidates for biopharmaceutical applications. We evaluated the extent to which generated clones and condition screening results were transferable and comparable to results from fully controlled bioreactor systems operated in fed-batch mode at moderate or high cell densities. Direct comparison of 22 different production clones showed great transferability. We observed the same growth and expression characteristics, and identical clone rankings except one host-Fab-leader combination. This outcome demonstrates the explanatory power of HTP µ-bioreactor data and the suitability of this platform as a screening tool in upstream development of microbial systems. Fast, reliable, and transferable screening data significantly reduce experiments in fully controlled bioreactor systems and accelerate process development at lower cost.