Industrial-scale Bioreactors Research Articles

Combined oxygen and glucose oscillations distinctly change the transcriptional and physiological state of Escherichia coli.

Escherichia coli, a common microbial host for industrial bioproduction, experiences a highly dynamic environment in industrial-scale bioreactors due to significant glucose and oxygen gradients. In this study, we mimic the combined gradients of glucose and oxygen in high-throughput bioreactors to study the transcriptional response of E. coli to industrial-scale conditions. Under oscillating oxygen conditions, E. coli formed less biomass and accumulated the anaerobic by-product acetate. With respect to oxygen-responsive genes, we found that genes of the TCA cycle and of different electron transport chain complexes were differentially expressed. A global analysis of the expression data revealed that oxygen oscillations had caused a transition towards a catabolite-repressed state and upregulation of several stress-related regulatory programs. Interestingly, the transcriptional changes persisted after oxygen limitation stopped. In contrast, the changes we observed due to glucose starvation, such as induction of the stringent response, were primarily transient. Most importantly, we found that effects of combined oxygen and glucose oscillations were distinct from the ones of oxygen and substrate oscillations alone, suggesting an important interplay between the different metabolic regimes in industrial-scale bioreactors.

Repeated glucose oscillations in high cell-density cultures influence stress-related functions of Escherichia coli.

Engineering microbial cells for the commercial production of biomolecules and biochemicals requires understanding how cells respond to dynamically changing substrate (feast-famine) conditions in industrial-scale bioreactors. Scale-down methods that oscillate substrate are commonly applied to predict the industrial-scale behavior of microbes. We followed a compartment modeling approach to design a scale-down method based on the simulation of an industrial-scale bioreactor. This study uses high cell-density scale-down experiments to investigate Escherichia coli knockout strains of five major glucose-sensitive transcription factors (Cra, Crp, FliA, PrpR, and RpoS) to study their regulatory role during glucose oscillations. RNA-sequencing analysis revealed that the glucose oscillations caused the down-regulation of several stress-related functions in E. coli. An in-depth analysis of strain physiology and transcriptome revealed a distinct phenotype of the strains tested under glucose oscillations. Specifically, the knockout strains of Cra, Crp, and RpoS resulted in a more sensitive transcriptional response than the control strain, while the knockouts of FliA and PrpR responded less severely. These findings imply that the regulation orchestrated by Cra, Crp, and RpoS may be essential for robust E. coli production strains. In contrast, the regulation by FliA and PrpR may be undesirable for temporal oscillations in glucose availability.

A coupled metabolic flux/compartmental hydrodynamic model for large-scale aerated bioreactors

Spatial variation of microorganism populations in stirred-tank bioreactors, caused by the interactions of complex multiphase flows and microorganism metabolism, results in undesirable performance characteristics and significant challenges in scale-up activities. In this work, a novel modelling approach is developed to investigate these coupled processes in bioreactors, which involves the integration of a computational fluid dynamics-informed compartmental hydrodynamic and a dynamic flux balance (DFB) model. This coupling poses significant computational challenges especially when dealing with transient scenarios. To tackle these challenges we developed a fast point-location algorithm to solve the DFB model at different bioreactor locations. To validate the presented modelling approach, a binary search tree-based metabolic model for E. coli was developed and integrated with a flow-informed compartmental model of a large-scale four-impeller aerated bioreactor in fed-batch operation. The model results exhibit favorable agreement with the concentration profiles reported in literature for both lab-scale and industrial scale bioreactors. Furthermore, the ability of the approach to deal with transient scenarios permitted to study the effect of oxygen closed loop control responses and the occurrence of oscillatory behaviour which were crucial to explain part of the data.

Advancing precision fermentation: Minimizing power demand of industrial scale bioreactors through mechanistic modelling

Minimizing power consumption in large-scale aerobic fermentation is essential for cost-effective operations. A mechanistic model of aerobic precision fermentation was developed integrating microbial growth parameters, thermodynamic data, and bioreactor properties. Results showed that agitation power dominated energy consumption at low oxygen transfer rates (OTR), shifting to aeration power (70 % of total) at high cell growth rates. In high OTRs, mixing time reduced to 60 s from an initial value of 211 s. Scale-up from 5 m³ to 100 m³ decreased total specific power by 88 %. Operating at elevated headspace pressure lowered agitation speed, reducing total power consumption at high OTR. Impeller to bioreactor diameter ratio impacted the required agitation speed without significantly altering total power demand. Experimental data in a 100 L case study indicated a 0.43 kW.m⁻³ average power requirement across a 96-hour fermentation period. Our model demonstrates effective strategies for minimization of power consumption in industrial-scale aerobic fermentations.

Study of hydrodynamic stress in cell culture bioreactors via lattice-Boltzmann CFD simulations supported by micro-probe shear stress method

Mammalian cell cultivation in pharmaceutical industry can last up to units of weeks and requires proper transport of nutrients and oxygen for cell growth and production. Given the long time period, cells experience flow fields from all bioreactor’s zones, where the energy dissipation rate (ε) varies substantially. Shear sensitive micro-probes with size comparable to cells and Kolmogorov eddies are used for the determination of the maximum hydrodynamic stress (τmax) in bioreactors. For the very first time, the micro-probe method is applied successfully not only to laboratory (3 L) and pilot scale (80 L and 200 L), but also to industrial production scale bioreactor (12,500 L) with Rushton turbine and pitched-blade (RT-PB) impeller configuration. Experimentally obtained data are used for the validation of comprehensive CFD scale-up study, using the Lattice-Boltzmann large eddy simulation (LB-LES) method. Besides τmax, this work also focuses on the study of mixing time and flow field attributes.

Dauer juvenile recovery transcriptome of two contrasting EMS mutants of the entomopathogenic nematode Heterorhabditis bacteriophora.

The entomopathogenic nematode Heterorhabditis bacteriophora, symbiotically associated with enterobacteria of the genus Photorhabdus, is a biological control agent against many insect pests. Dauer Juveniles (DJ) of this nematode are produced in industrial-scale bioreactors up to 100 m3 in liquid culture processes lasting approximately 11days. A high DJ yield (> 200,000 DJ·mL-1) determines the success of the process. To start the mass production, a DJ inoculum proceeding from a previous monoxenic culture is added to pre-cultured (24h) Photorhabdus bacteria. Within minutes after contact with the bacteria, DJ are expected to perceive signals that trigger their further development (DJ recovery) to reproductive hermaphrodites. A rapid, synchronized, and high DJ recovery is a key factor for an efficient culture process. In case of low percentage of DJ recovery, the final DJ yield is drastically reduced, and the amount of non-desired stages (males and non-fertilized females) hinders the DJ harvest. In a preliminary work, a huge DJ recovery phenotypic variability in H. bacteriophora ethyl methanesulphonate (EMS) mutants was determined. In the present study, two EMS-mutant lines (M31 and M88) with high and low recovery phenotypes were analyzed concerning their differences in gene expression during the first hours of contact with Photorhabdus supernatant containing food signals triggering recovery. A snapshot (RNA-seq analysis) of their transcriptome was captured at 0.5, 1, 3 and 6h after exposure. Transcripts (3060) with significant regulation changes were identified in the two lines. To analyze the RNA-seq data over time, we (1) divided the expression profiles into clusters of similar regulation, (2) identified over and under-represented gene ontology categories for each cluster, (3) identified Caenorhabditis elegans homologous genes with recovery-related function, and (4) combined the information with available single nucleotide polymorphism (SNP) data. We observed that the expression dynamics of the contrasting mutants (M31 and M88) differ the most within the first 3h after Photorhabdus supernatant exposure, and during this time, genes related to changes in the DJ cuticle and molting are more active in the high-recovery line (M31). Comparing the gene expression of DJ exposed to the insect food signal in the haemolymph, genes related to host immunosuppressive factors were not found in DJ upon bacterial supernatant exposure. No link between the position of SNPs associated with high recovery and changes in gene expression was determined for genes with high differential expression. Concerning specific transcripts, nine H. bacteriophora gene models with differential expression are provided as candidate genes for further studies.

Developing rational scale-down simulators for mimicking substrate heterogeneities based on cell lifelines in industrial-scale bioreactors

The influence of extracellular variations on the cellular metabolism and thereby the process performance at large-scale can be evaluated using the so-called scale-down simulators. Nevertheless, the major challenge is to design an appropriate scale-down simulator, which can accurately mimic the cell lifelines that record the flow paths and experiences of cells circulating in large-scale bioreactors. To address this, a dedicated SDSA (scale-down simulator application) was purposedly developed on the basis of black box model and process reaction model established for Penicillium chrysogenum strain as well as cell lifelines or trajectories information in an industrial-scale fermentor. Guided by the SDSA, the industrial-relevant metabolic regimes for substrate availability, i.e., excess, limitation and starvation, were successfully reproduced at laboratory-scale three-compartment scale-down (SD) system. In addition, such SDSA can also display individual process dynamics in each compartment, and demonstrate how individual factors influence the entire bioprocess performance, thus serving both educational and research purposes.

A reacting multiphase computational flow model for 2,3-butanediol synthesis in industrial-scale bioreactors

The microbe, Zymomonas mobilis, can efficiently convert sugars to 2,3 butanediol (BDO), an important intermediate for downstream chemical products, only in a well-controlled microaerated environment. However, controlling oxygen distribution in industrial-scale bioreactors is challenging, and at-scale BDO production is hard to optimize using traditional engineering methods. This study takes a step towards addressing this problem through a computational model for reacting multiphase flows in large-scale bioreactors. A phenomenological metabolic model was first developed and validated against experiments, and then it was coupled to a multiphase computational fluid dynamics (CFD) solver using a subcycling algorithm for simulating long conversion times ( ∼ 30 h). Large-scale ( ∼ 500 m3) bubble column simulations using this coupled approach demonstrated a 25% improvement in BDO yield with low sparging rates (0.002 m/s) and low height-to-diameter ratio (0.875) compared to a baseline bubble column (35 m height, 5 m diameter) with a superficial gas velocity of 0.06 m/s.

Modelling of industrial-scale bioreactors using the particle lifeline approach

A key factor in improving the performance of large-scale bioreactors is understanding the conditions experienced by the cells inside the reactor. This can be challenging due to the practical difficulties involved, hence there is increasing use of simulation to quantify the environmental conditions found in large-scale bioreactors. In this work we have used the particle lifeline approach to quantify the effect of the reactor design on the conditions experienced by two very commonly used industrial organisms (Escherichia coli and Saccharomyces cerevisiae). It was found that the cells in the stirred tank reactor tended to experience longer fluctuations of both starvation and overflow metabolism when compared with those in the bubble column, this behaviour being caused by differences in mixing between the two reactor designs. It was found that a significant (60%) fraction of the population in the stirred tank reactors experienced starvation conditions for a large fraction (>70%) of the time, with exposure to such conditions being likely to affect the cellular metabolism. Results from this work provide a detailed insight into the conditions experienced inside industrial-scale bioreactors operated at realistic conditions. Such data can be leveraged to optimise large-scale reactor designs as well as for the development of scale-down systems.

Predicting the influence of combined oxygen and glucose gradients based on scale-down and modelling approaches for the scale-up of penicillin fermentations

In large scale fermentors the cultivated cells are exposed to dynamic changes in the nutrient concentrations due to imperfect mixing. Based on the characterization of these nutrient gradients in space and time, a rational scale down design can be obtained. This study focuses on the combined gradients of dissolved sugar and oxygen concentrations. Based on a recent computational fluid dynamics (CFD) study, firstly a scale-down design was developed. From intracellular metabolite measurements during these scale-down experiments, the metabolic behavior of the cells under highly dynamic conditions was revealed. Under the combined influence of oscillating glucose and oxygen concentrations, the penicillin production declined to 50 % of the value under steady state conditions. This decline was similar as observed during glucose oscillations alone. The influence of oxygen oscillations on the levels of the majority of the intracellular metabolites analyzed was negligible, although these metabolites were strongly affected by the varying oxygen levels under solely oxygen oscillations. Additionally, a metabolic structured kinetic model was developed and validated with data from glucose and oxygen oscillation experiments. This model can be coupled to CFD simulations to obtain an accurate prediction of the performance of industrial strains in space and time in large industrial scale bioreactors.

What does respirometric analysis tell us about Metarhizium robertsii

Metarhizium comprises species of importance as biological control agents whose conidia have been well produced in lab-scale packed column bioreactors. However, difficulties such as low oxygen supply, CO2 removal, and overheating limit bioreactor scale-up. To select suitable operating conditions in industrial-scale bioreactors, it is necessary to analyze the bioprocess in terms of O2 consumption and CO2 production. Metarhizium robertsii cultures reached maxima of O2 consumption and CO2 production rates of 0.23 and 0.35 mg/gidm h, respectively. The total O2 consumption (23.81 mg/gidm) and CO2 production (34.52 mg/gidm) were determined by experimental measurements and estimates by mathematical models with a coefficient of determination higher than 0.99. The respiratory quotient presented values from 1 to 1.2 (from 48 to 175 h). The respirometric analysis showed that the culture of is an aerobic process with low CO2 and low metabolic heat production, favoring the scale-up of the bioprocess

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models.

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.

Development of an industrial-scale bioreactor in the context of a global pandemic

This article provides a comparison of biogas plants for waste processing of various modifications, advantages and disadvantages, as well as a description of a biogas plant of its own development of a horizontal type, taking into account local climatic and economic conditions of farm development, in the context of a pandemic. The article discusses of using of our patented pneumatic motor for mixing biowaste, the bioreactor of own production and comparing the development of biogas station in different countries.

Impact of Altered Trehalose Metabolism on Physiological Response of Penicillium chrysogenum Chemostat Cultures during Industrially Relevant Rapid Feast/Famine Conditions

Due to insufficient mass transfer and mixing issues, cells in the industrial-scale bioreactor are often forced to experience glucose feast/famine cycles, mostly resulting in reduced commercial metrics (titer, yield and productivity). Trehalose cycling has been confirmed as a double-edged sword in the Penicillium chrysogenum strain, which facilitates the maintenance of a metabolically balanced state, but it consumes extra amounts of the ATP responsible for the repeated breakdown and formation of trehalose molecules in response to extracellular glucose perturbations. This loss of ATP would be in competition with the high ATP-demanding penicillin biosynthesis. In this work, the role of trehalose metabolism was further explored under industrially relevant conditions by cultivating a high-yielding Penicillium chrysogenum strain, and the derived trehalose-null strains in the glucose-limited chemostat system where the glucose feast/famine condition was imposed. This dynamic feast/famine regime with a block-wise feed/no feed regime (36 s on, 324 s off) allows one to generate repetitive cycles of moderate changes in glucose availability. The results obtained using quantitative metabolomics and stoichiometric analysis revealed that the intact trehalose metabolism is vitally important for maintaining penicillin production capacity in the Penicillium chrysogenum strain under both steady state and dynamic conditions. Additionally, cells lacking such a key metabolic regulator would become more sensitive to industrially relevant conditions, and are more able to sustain metabolic rearrangements, which manifests in the shrinkage of the central metabolite pool size and the formation of ATP-consuming futile cycles.

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.

Using data analytics to accelerate biopharmaceutical process scale-up

In the biopharmaceutical industry, the selection of high-quality cell lines characterized by high productivity and stability over time plays a critical role for the development of processes meeting the desired quality and manufacturability criteria. Cell line selection involves their testing through subsequent stages at different process scales to progressively recreate the conditions of an industrial-scale bioreactor, from static micro-well plates, to shake flasks to reactors of increasing volumes and complexity. In this context both scale-up and scale-down are challenging due to the variety and quantity of data to mine and the scarcity of first-principles understanding on cells culture dynamics.This paper illustrates the use of advanced data-driven modeling where patterns across multiple scales are identified and interpreted to aid the cell line selection scale-up process. Methods such as multiway principal components analysis, Procrustes analysis and joint-Y projection to latent structures are used to characterize cell lines cross correlation and their dynamic behavior over time, and to carry out performance predictions across different scales. The approach has the potential to mature into a general framework to aid scale-up for cell-line selection and so drive acceleration in the development cycle of biopharmaceuticals through an optimal use of the available data acquisition platforms.

NS1 Recombinant Proteins Are Efficiently Produced in Pichia pastoris and Have Great Potential for Use in Diagnostic Kits for Dengue Virus Infections.

Dengue is one of the major diseases causing global public health concerns. Despite technological advances in vaccine production against all its serotypes, it is estimated that the dengue virus is responsible for approximately 390 million infections per year. Laboratory diagnosis has been the key point for the correct treatment and prevention of this disease. Currently, the limiting factor in the manufacture of dengue diagnostic kits is the large-scale production of the non-structural 1 (NS1) antigen used in the capture of the antibody present in the infected patients’ serum. In this work, we demonstrate the production of the non-structural 1 protein of dengue virus (DENV) serotypes 1–4 (NS1-DENV1, NS1-DENV2, NS1-DENV3, and NS1-DENV4) in the methylotrophic yeast Pichia pastoris KM71H. Secreted recombinant protein was purified by affinity chromatography and characterized by SDS-PAGE and ELISA. The objectives of this study were achieved, and the results showed that P. pastoris is a good heterologous host and worked well in the production of NS1DENV 1–4 recombinant proteins. Easy to grow and quick to obtain, this yeast secreted ready-to-use proteins, with a final yield estimated at 2.8–4.6 milligrams per liter of culture. We reached 85–91% sensitivity and 91–93% specificity using IgM as a target, and for anti-dengue IgG, 83–87% sensitivity and 81–93% specificity were achieved. In this work, we conclude that the NS1 recombinant proteins are efficiently produced in P. pastoris and have great potential for use in diagnostic kits for dengue virus infections. The transformed yeast obtained can be used for production in industrial-scale bioreactors.

Modeling the effects of substrate fluctuations on the maintenance rate in bioreactors with a probabilistic approach

A simple interaction by exchange with the mean (IEM) mixing model is implemented to describe the glucose concentration segregations in industrial and laboratory scale bioreactors. This approach is coupled with a population balance model (PBM) for the growth rate adaptation and a metabolic model dependent on the individuals state, both from the literature [1]. The model formulation is validated against different published experiments and it is shown that the IEM model reduces the computational costs when just the segregation of few species is of interest. A model for the maintenance costs of Escherichia coli subject to glucose concentration fluctuation is also presented and implemented in the context of the IEM mixing model. An Eulerian formulation of the effects of the substrate fluctuations on the maintenance rate is proposed and tied to a more intuitive Lagrangian vision. The study of these metabolic changes due to substrate heterogeneities helps the understanding of the relationships between hydrodynamics and cells metabolism and it improves the agreement between numerical and experimental data.

Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors.

Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses.

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.