Recombinant protein expression in proteome-reduced cells under aerobic and oxygen-limited regimes.

Industrial cultures are hindered by the physiological complexity of the host and the limited mass transfer capacity of conventional bioreactors. In this study, a minimal cell approach was combined with genetic devices to overcome such issues. A flavin mononucleotide-based fluorescent protein (FbFP) was expressed in a proteome-reduced Escherichia coli (PR). When FbFP was expressed from a constitutive protein generator (CPG), the PR strain produced 47% and 35% more FbFP than its wild type (WT), in aerobic or oxygen-limited regimes, respectively. Metabolic and expression models predicted more efficient biomass formation at higher fluxes to FbFP, in agreement with these results. A microaerobic protein generator (MPG) and a microaerobic transcriptional cascade (MTC) were designed to induce FbFP expression upon oxygen depletion. The FbFP fluorescence using the MTC in the PR strain was 9% higher than that of the WT bearing the CPG under oxygen limitation. To further improve the PR strain, the pyruvate dehydrogenase complex regulator gene was deleted, and the Vitreoscilla hemoglobin was expressed. Compared to oxygen-limited cultures of the WT, the engineered strains increased the FbFP expression more than 50% using the MTC. Therefore, the designed expression systems can be a valuable alternative for industrial cultivations.

Production of Modified Nucleosides in a Continuous Enzyme Membrane Reactor.

Nucleoside analogues are important compounds for the treatment of viral infections or cancers. While (chemo-)enzymatic synthesis is a valuable alternative to traditional chemical methods, the feasibility of such processes is lowered by the high production cost of the biocatalyst. As continuous enzyme membrane reactors (EMR) allow the use of biocatalysts until their full inactivation, they offer a valuable alternative to batch enzymatic reactions with freely dissolved enzymes. In EMRs, the enzymes are retained in the reactor by a suitable membrane. Immobilization on carrier materials, and the associated losses in enzyme activity, can thus be avoided. Therefore, we validated the applicability of EMRs for the synthesis of natural and dihalogenated nucleosides, using one-pot transglycosylation reactions. Over a period of 55 days, 2'-deoxyadenosine was produced continuously, with a product yield >90%. The dihalogenated nucleoside analogues 2,6-dichloropurine-2'-deoxyribonucleoside and 6-chloro-2-fluoro-2'-deoxyribonucleoside were also produced, with high conversion, but for shorter operation times, of 14 and 5.5 days, respectively. The EMR performed with specific productivities comparable to batch reactions. However, in the EMR, 220, 40, and 9 times more product per enzymatic unit was produced, for 2'-deoxyadenosine, 2,6-dichloropurine-2'-deoxyribonucleoside, and 6-chloro-2-fluoro-2'-deoxyribonucleoside, respectively. The application of the EMR using freely dissolved enzymes, facilitates a continuous process with integrated biocatalyst separation, which reduces the overall cost of the biocatalyst and enhances the downstream processing of nucleoside production.

High-throughput screening of optimal process conditions using model predictive control.

Modern biotechnological laboratories are equipped with advanced parallel mini-bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed-batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well-defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini-bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.

Spectral Unmixing-Based Reaction Monitoring of Transformations between Nucleosides and Nucleobases.

The increased interest in (enzymatic) transformations between nucleosides and nucleobases has demanded the development of efficient analytical tools. In this report, we present an update and extension of our recently described method for monitoring these reactions by spectral unmixing. The presented method uses differences in the UV absorption spectra of nucleosides and nucleobases after alkaline quenching to derive their ratio based on spectral shape by fitting normalized reference spectra. It is applicable to a broad compound spectrum comprising more than 35 examples, offers HPLC-like accuracy, ease of handling and significant reductions in both cost and data acquisition time compared to other methods. This contribution details the principle of monitoring reactions by spectral unmixing, gives recommendations regarding solutions to common problems and applications that necessitate special sample treatment. We provide software, workflows and reference spectra that facilitate the straightforward and versatile application of the method.

A model-based framework for parallel scale-down fed-batch cultivations in mini-bioreactors for accelerated phenotyping.

Concentration gradients that occur in large industrial-scale bioreactors due to mass transfer limitations have significant effects on process efficiency. Hence, it is desirable to investigate the response of strains to such heterogeneities to reduce the risk of failure during process scale-up. Although there are various scale-down techniques to study these effects, scale-down strategies are rarely applied in the early developmental phases of a bioprocess, as they have not yet been implemented on small-scale parallel cultivation devices. In this study, we combine mechanistic growth models with a parallel mini-bioreactor system to create a high-throughput platform for studying the response of Escherichia coli strains to concentration gradients. As a scaled-down approach, a model-based glucose pulse feeding scheme is applied and compared with a continuous feed profile to study the influence of glucose and dissolved oxygen gradients on both cell physiology and incorporation of noncanonical amino acids into recombinant proinsulin. The results show a significant increase in the incorporation of the noncanonical amino acid norvaline in the soluble intracellular extract and in the recombinant product in cultures with glucose/oxygen oscillations. Interestingly, the amount of norvaline depends on the pulse frequency and is negligible with continuous feeding, confirming observations from large-scale cultivations. Most importantly, the results also show that a larger number of the model parameters are significantly affected by the scale-down scheme, compared with the reference cultivations. In this example, it was possible to describe the effects of oscillations in a single parallel experiment. The platform offers the opportunity to combine strain screening with scale-down studies to select the most robust strains for bioprocess scale-up.

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 qSmax and acetate production qApmax; meanwhile, higher glucose concentrations had adverse effects on the biomass yield coefficient YXSem. Additional parameters compared to previous studies for the oxygen uptake rate and carbon dioxide production rate indicated differences in the specific oxygen uptake rate qOmax. 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.

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations.

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

A UV/Vis Spectroscopy-Based Assay for Monitoring of Transformations Between Nucleosides and Nucleobases.

Efficient reaction monitoring is crucial for data acquisition in kinetic and mechanistic studies. However, for conversions of nucleosides to their corresponding nucleobases, as observed in enzymatically catalyzed nucleoside phosphorylation reactions, the current analytical arsenal does not meet modern requirements regarding cost, speed of analysis and high throughput. Herein, we present a UV/Vis spectroscopy-based assay employing an algorithm for spectral unmixing in a 96-well plate format. The algorithm relies on fitting of reference spectra of nucleosides and their bases to experimental spectra and allows determination of nucleoside/nucleobase ratios in solution with high precision. The experimental procedure includes appropriate dilution of a sample into aqueous alkaline solution, transfer to a multi-well plate, measurement of a UV/Vis spectrum and subsequent in silico spectral unmixing. This enables data collection in a high-throughput fashion and reduces costs compared to state-of-the-art HPLC analyses by approximately 5-fold while being 20-fold faster and offering comparable precision. Additionally, the method is robust regarding dilution and sample transfer errors as it only considers spectral form and not absolute intensity. It can be applied to all natural nucleosides and nucleobases and even unnatural ones as demonstrated by several examples.

Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control.

During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but also by its sophistication (e.g., inputs, sensors, sampling frequency, analytics). High-throughput liquid-handling stations can perform a large number of automated experiments in parallel. Nevertheless, the experimental data sets that are obtained are not always relevant for development of industrial bioprocesses, leading to a high rate of failure during scale-up. We present an automated mini bioreactor platform that enables parallel cultivations in the milliliter scale with online monitoring and control, well-controlled conditions, and advanced feeding strategies similar to industrial processes. The combination of two liquid handlers allows both automated mini bioreactor operation and at-line analysis in parallel. A central database enables end-to-end data exchange and fully integrated device and process control. A model-based operation algorithm allows for the accurate performance of complex cultivations for scale-down studies and strain characterization via optimal experimental redesign, significantly increasing the reliability and transferability of data throughout process development. The platform meets the tradeoff between experimental throughput and process control and monitoring comparable to laboratory-scale bioreactors.

Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design.

Especially in biomanufacturing, methods to design optimal experiments are a valuable technique to fully exploit the potential of the emerging technical possibilities that are driving experimental miniaturization and parallelization. The general objective is to reduce the experimental effort while maximizing the information content of an experiment, speeding up knowledge gain in R&D. The approach of model-based design of experiments (known as MBDoE) utilizes the information of an underlying mathematical model describing the system of interest. A common method to predict the accuracy of the parameter estimates uses the Fisher information matrix to approximate the 90% confidence intervals of the estimates. However, for highly non-linear models, this method might lead to wrong conclusions. In such cases, Monte Carlo sampling gives a more accurate insight into the parameter's estimate probability distribution and should be exploited to assess the reliability of the approximations made through the Fisher information matrix. We first introduce the model-based optimal experimental design for parameter estimation including parameter identification and validation by means of a simple non-linear Michaelis-Menten kinetic and show why Monte Carlo simulations give a more accurate depiction of the parameter uncertainty. Secondly, we propose a very robust and simple method to find optimal experimental designs using Monte Carlo simulations. Although computational expensive, the method is easy to implement and parallelize. This article focuses on practical examples of bioprocess engineering but is generally applicable in other fields.

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.