Efficient operation of bioreactors is crucial for the success of biomanufacturing processes. Traditional Computational Fluid Dynamics (CFD) simulations provide detailed insights but often involve lengthy computation times and complexity, hindering their practicality for real-time applications. This study introduces a novel multivariate unsupervised learning algorithm that clusters bioreactors into physically meaningful regions based on CFD-generated and real-world data. These clusters not only facilitate the determination of internal reactor regimes but also serve as a foundational step for developing compartment models. Our approach utilizes a custom k-means clustering algorithm, which ensures spatial continuity of clusters by incorporating geometric data, and optimizes the number of compartments to maximize physical significance and data retention. This optimization is guided by a Pareto front analysis, balancing the need for clear compartment definition with the preservation of maximum information from the dataset. The effectiveness and versatility of this methodology were verified through case studies involving a 202 m³ Rushton impeller bioreactor (steady state simulation) and an 840 m³ airlift reactor (dynamic simulation). In the airlift reactor, the clustering algorithm accounted for dynamic fluctuations by averaging the simulation results, providing a robust method for incorporating temporal variations into the compartment analysis. The findings highlight the advantages of 3-D compartmentalization in capturing the intricate dynamics of fluid motion and cellular activities, thereby advancing the design of bioreactors and scaling down experiments for more efficient industrial applications.@en

Anticipating the occurrence and effects of mass transport limitations during fermentation scale-up is essential for commercialization, as heterogeneities might affect microorganisms. Tools like Computational Fluid Dynamics (CFD) aid this analysis but are computationally intensive, limiting design space exploration and consequently, fermentation optimization. Compartment models (CMs) based on CFD simulations offer an affordable alternative but require CFD recalibration with changing geometries or operating conditions, restricting their usage in optimization.
In this work, we introduce a hybrid machine-learning-aided compartment model (ML-CM) that accounts for flow pattern dynamics upon changes in both volume and stirring speed in a stirred tank bioreactor. The ML-aided dynamic compartment model (dyn-CM) enabled the spatiotemporal study of a process in 1/500th of the fermentation simulation time, maintaining reasonable accuracy. This method facilitates fed-batch fermentation modeling, process optimization, and scale-up effect analysis with modest computational resources, supporting reactor design and operational improvements within a defined operating space.@en

Flow-following sensor technology offers a method to collect information on flow patterns and local velocities in pilot- and industrial scale reactors, which are practically inaccessible to many measurement techniques. Such data is highly valuable for scale-up of bioprocesses, as well as validation of bioreactor CFD simulations. Flow-following sensors were applied in a pilot-scale (2 m³ filled volume) bubble column fermentor, showing that axially resolved data can be acquired under heterogeneous bubbly flow conditions with high gas holdup. Next the use of the collected data for validation of CFD simulations of the pilot-scale reactor is explored, discriminating between models utilizing different interphase interaction models. The CFD simulation was found capable of capturing the velocity profile and circulation behavior, but full validation was found to be challenging. When simulating virtual sensors via Lagrangian particle tracking, differences are observed in terms of particle distribution and sensitivity to particle density between experimental and simulated data, indicating further development of representative CFD simulations is required.

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As the youngest of the quartet of systems biology tools alongside genomics, transcriptomics, and proteomics, metabolomics provides an immediate and dynamic recording of cells in response to genetic and/or environmental perturbations. Metabolomics study accelerates learning steps within the iterative design, build, test, and learn (DBTL) cycle for enhancing bioproduction capability. The associations between biological networks and environmental factors facilitate predictive modeling of cellular response, which is the basis for industrial application. This chapter presents an update on the metabolomics-driven biosystems engineering and bioprocess design principle from the metabolic perspective of organisms. Along with the introduction of the isotope dilution mass spectrometry (ID-MS) method to the fast sampling, quenching, and extraction protocol for quantitative metabolomics, metabolomics-assisted engineering biology and the establishment of metabolically structured models are highlighted. Furthermore, a computational framework based on a coupled metabolic-hydrodynamic approach is advocated to assess the interlocking architectures between environmental and biological networks in large-scale bioprocesses and provide suggestions toward bioreactor evaluation, scale-down, and optimization.@en

While traveling through different zones in large-scale bioreactors, microbes are most likely subjected to fluctuating dissolved oxygen (DO) conditions at the timescales of global circulation time. In this study, to mimic industrial-scale spatial DO gradients, we present a scale-down setup based on dynamic feast/famine regime (150 s) that leads to repetitive cycles with rapid changes in DO availability in glucose-limited chemostat cultures of Penicillium chrysogenum. Such DO feast/famine regime induced a stable and repetitive pattern with a reproducible metabolic response in time, and the dynamic response of intracellular metabolites featured specific differences in terms of both coverage and magnitude in comparison to other dynamic conditions, for example, substrate feast/famine cycles. Remarkably, intracellular sugar polyols were considerably increased as the hallmark metabolites along with a dynamic and higher redox state nicotinamide adenine dinucleotide hydrogen/nicotinamide adenine dinucleotide of the cytosol. Despite the increased availability of NADPH for penicillin production under the oscillatory DO conditions, this positive effect may be counteracted by the decreased adenosine triphosphate supply. Moreover, it is interesting to note that not only the penicillin productivity was reduced under such oscillating DO conditions, but also that of the unrecyclable byproduct ortho-hydroxyphenyl acetic acid and degeneration of penicillin productivity. Furthermore, dynamic flux profiles showed the most pronounced variations in central carbon metabolism, amino acid (AA) metabolism, energy metabolism and fatty acid metabolism upon the DO oscillation. Taken together, the metabolic responses of P. chrysogenum to DO gradients reported here are important for elucidating metabolic regulation mechanisms, improving bioreactor design and scale-up procedures as well as for constructing robust cell strains to cope with heterogenous industrial culture conditions.@en

Gradients in dissolved gas concentrations are expected to affect the performance of large reactors for anaerobic gas (CO, H2, CO2) fermentation. To study how these gradients, and the dissolved gas concentration level itself, influence the productivity of the desired product ethanol and the product spectrum of C. autoethanogenum, we coupled a CFD model of an industrial-scale gas fermentor to a metabolic kinetic model for a wide range of metabolic regimes. Our model results, together with literature experimental data and a model with constant
dissolved gas concentrations, indicate high ethanol specificity at low dissolved CO concentrations, with acetate reduction to ethanol at very low dissolved CO concentrations and combined ethanol and acetate production at higher CO concentrations. The gradient was predicted to increase both the biomass-specific ethanol production rate and the electron-to-ethanol yield by ~25%. This might be due to intensified ferredoxin and NAD+ redox cycles, with the rate of the Rnf complex – a critical enzyme for energy conservation – as key driver towards
ethanol production, all at the expense of a reduced flux to acetate. We present improved mechanistic understanding of the gas fermentation process, and novel leads for optimization and fundamental research, by coupling observations from various down-scaled lab experiments to expected microbial lifelines in an industrial-scale reactor. @en

The growing global population and heightened concern for climate change leads to increased interest in utilizing microbial fermentations to replace polluting production processes for e.g., plastics, fuels, and animal proteins. Computational fluid dynamics (CFD) is a valuable tool for accelerating the scale-up and optimization of large-scale bioprocesses. However, the design correlations underlying most of these CFD models are validated with air-water systems, not accounting for the distinct hydrodynamic properties of microbial fermentation broth. In this review, we provide an extensive overview of the current understanding of how various biotechnologically relevant solutes impact the hydrodynamics of bubble columns. We examine the effects of components found in fermentation broths, including salts, surfactants, viscoelastic solutes, alcohols, acids, ketones, sugars, biomass, and proteins, on mass transfer, bubble formation, bubble interactions, and flow regime transitions. These components all exhibit unique effects, yet their combined influences remain poorly understood. Future research should prioritize identifying the concentration at which coalescence inhibition occurs for different compounds, especially in mixtures, and exploring the role of proteins in bubble column hydrodynamics from micro- to macroscale.

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Bioprocesses are scaled up for the production of large product quantities. With larger fermenter volumes, mixing becomes increasingly inefficient and environmental gradients get more prominent than in smaller scales. Environmental gradients have an impact on the microorganism's metabolism, which makes the prediction of large-scale performance difficult and can lead to scale-up failure. A promising approach for improved understanding and estimation of dynamics of microbial populations in large-scale bioprocesses is the analysis of microbial lifelines. The lifeline of a microbe in a bioprocess is the experience of environmental gradients from a cell's perspective, which can be described as a time series of position, environment and intracellular condition. Currently, lifelines are predominantly determined using models with computational fluid dynamics, but new technical developments in flow-following sensor particles and microfluidic single-cell cultivation open the door to a more interdisciplinary concept. We critically review the current concepts and challenges in lifeline determination and application of lifeline analysis, as well as strategies for the integration of these techniques into bioprocess development. Lifelines can contribute to a successful scale-up by guiding scale-down experiments and identifying strain engineering targets or bioreactor optimisations.

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In large-scale syngas fermentation, strong gradients in dissolved gas (CO, H₂) concentrations are very likely to occur due to locally varying mass transfer and convection rates. Using Euler-Lagrangian CFD simulations, we analyzed these gradients in an industrial-scale external-loop gas-lift reactor (EL-GLR) for a wide range of biomass concentrations, considering CO inhibition for both CO and H₂ uptake. Lifeline analyses showed that micro-organisms are likely to experience frequent (5 to 30 s) oscillations in dissolved gas concentrations with one order of magnitude. From the lifeline analyses, we developed a conceptual scale-down simulator (stirred-tank reactor with varying stirrer speed) to replicate industrial-scale environmental fluctuations at bench scale. The configuration of the scale-down simulator can be adjusted to match a broad range of environmental fluctuations. Our results suggest a preference for industrial operation at high biomass concentrations, as this would strongly reduce inhibitory effects, provide operational flexibility and enhance the product yield. The peaks in dissolved gas concentration were hypothesized to increase the syngas-to-ethanol yield due to the fast uptake mechanisms in C. autoethanogenum. The proposed scale-down simulator can be used to validate such results and to obtain data for parametrizing lumped kinetic metabolic models that describe such short-term responses.

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This study focuses on the metabolic impacts of simultaneous glucose and oxygen concentration gradients on penicillin production in an industrial-scale fermentor, using the computational fluid dynamics-cellular reaction dynamics approach. Inclusion of oxygen-coupling considerably impacts the glucose uptake and resulting penicillin productivity. This is characterised by six metabolic regimes; lifeline data reconstructed from experimental results, recorded from the cellular perspective, indicates rapid dynamics in glucose and dissolved oxygen uptake by the microorganisms. The results are highly sensitive to variations in the oxygen-related model parameters, requiring accurate insight into the multiphase hydrodynamics and metabolic processes. Hypothetical scenarios with stronger glucose-oxygen limitations than tested experimentally were further explored. A precision scale-down (SD) simulator was designed based on the lifeline data, requiring considerable operational dynamics, with increasing system complexity and implementation difficulty. These insights may inspire further research into alternative SD configurations better suited to mimic the rapid dynamics of large-scale fermentation processes.

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Euler-Lagrange CFD simulations, where the biotic phase is represented by computational particles (parcels), provide information on environmental gradients inside bioreactors from the microbial perspective. Such information is highly relevant for reactor scale-down and process optimization. One of the major challenges is the computational intensity of CFD simulations, especially when resolution of dynamics in the flowfield is required. Lattice-Boltzmann large-eddy simulations (LB-LES) form a very promising approach for simulating accurate, dynamic flowfields in stirred reactors, at strongly reduced computation times compared to finite volume approaches. In this work, the performance of LB-LES in resolving substrate gradients in large-scale bioreactors is explored, combined with the inclusion of a Lagrangian biotic phase to provide the microbial perspective. In addition, the hydrodynamic performance of the simulations is confirmed by verification of hydrodynamic characteristics (radial velocity, turbulent kinetic energy, energy dissipation) in the impeller discharge stream of a 29 cm diameter stirred tank. The results are compared with prior finite volume simulation results, both in terms of hydrodynamic and biokinetic observations, and time requirements.

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In gas fermentations (using O2, CO, H2, CH4 or CO2), gas-to-liquid mass transfer is often regarded as one of the limiting processes. However, it is widely known that components in fermentation broths (e.g., salts, biomass, proteins, antifoam, and organic products such as alcohols and acids) have tremendous impact on the volumetric mass transfer coefficient kLa. We studied the influence of ethanol on mass transfer in three fermentation broths derived from syngas fermentation. In demineralized water, we observed that the addition of ethanol, the expected product, increased kLa two-fold in the 0–5 g L−1 range, after which near-constant kLa values were obtained. In the fermentation broths, kLa was increased significantly (2–4 fold compared to water) by ethanol supplementation, and to be highly influenced by broth salinity. Our results indicate that kLa is a dynamic parameter in gas fermentation experiments and can be significantly increased due to broth components.@en

Mass transfer limitations in syngas fermentation processes are mostly attributed to poor solubility of CO and H₂ in water. Despite these assumed limitations, a syngas fermentation process has recently been commercialized. Using large-sale external-loop gas-lift reactors (EL-GLR), CO-rich off-gases are converted into ethanol, with high mass transfer performance (7–8.5 g.L^-1.h⁻¹). However, when applying established mass transfer correlations, a much poorer performance is predicted (0.3–2.7 g.L^-1.h⁻¹). We developed a CFD model, validated on pilot-scale data, to provide detailed insights on hydrodynamics and mass transfer in a large-scale EL-GLR. As produced ethanol could increase gas hold-up (+30%) and decrease the bubble diameter (≤2 mm) compared to air–water mixtures, we found with our model that a high volumetric mass transfer coefficient (650–750 h⁻¹) and mass transfer capacity (7.5–8 g.L^-1.h⁻¹) for CO are feasible. Thus, the typical mass transfer limitations encountered in air–water systems can be alleviated in the syngas-to-ethanol fermentation process.

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The compartment model (CM) is a well-known approach for computationally affordable, spatially resolved hydrodynamic modeling of unit operations. Recent implementations use flow profiles based on Computational Fluid Dynamics (CFD) simulations, and several authors included microbial kinetics to simulate gradients in bioreactors. However, these studies relied on black-box kinetics that do not account for intracellular changes and cell population dynamics in response to heterogeneous environments. In this paper, we report the implementation of a Lagrangian reaction model, where the microbial phase is tracked as a set of biomass-parcels, each linked with an intracellular composition vector and a structured reaction model describing their intracellular response to extracellular variations. A stochastic parcel tracking approach is adopted, in contrast to the resolved trajectories used in CFD implementations. A penicillin production process is used as a case study. We show good performance of the model compared with full CFD simulations, both regarding the extracellular gradients and intracellular pool response, using the mixing time as a matching criterion and taking into account that the mixing time is sensitive to the number of compartments. The sensitivity of the model output towards some of the inputs is explored. The coarsest representative CM requires a few minutes to solve 80 h of flow time, compared with approximately 2 weeks for a full Euler–Lagrange CFD simulation of the same case. This alleviates one of the major bottlenecks for the application of such CFD simulations towards the analysis and optimization of industrial fermentation processes.

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Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.

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Bioprocess scale-up is a critical step in process development. However, loss of production performance upon scaling-up, including reduced titer, yield, or productivity, has often been observed, hindering the commercialization of biotech innovations. Recent developments in scale-down studies assisted by computational fluid dynamics (CFD) and powerful stimulus–response metabolic models afford better process prediction and evaluation, enabling faster scale-up with minimal losses. In the future, an ideal bioprocess design would be guided by an in silico model that integrates cellular physiology (spatiotemporal multiscale cellular models) and fluid dynamics (CFD models). Nonetheless, there are challenges associated with both establishing predictive metabolic models and CFD coupling. By highlighting these and providing possible solutions here, we aim to advance the development of a computational framework to accelerate bioprocess scale-up.

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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.

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We show experimentally, and explain theoretically, what velocity is needed to break an elongated droplet entering a microfluidic T-junction. Our experiments on short droplets confirm previous experimental and theoretical work that shows that the critical velocity for breakup scales with the inverse of the length of the droplet raised to the fifth power. For long elongated droplets that have a length about thrice the channel width, we reveal a drastically different scaling. Taking into account that a long droplet remains squeezed between the channel walls when it enters a T-junction, such that the gutters in the corners of the channel are the main route for the continuous phase to flow around the droplet, we developed a model that explains that the critical velocity for breakup is inversely proportional to the droplet length. This model for the transition between breaking and nonbreaking droplets is in excellent agreement with our experiments.

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In a 54 m³ large-scale penicillin fermentor, the cells experience substrate gradient cycles at the timescales of global mixing time about 20–40 s. Here, we used an intermittent feeding regime (IFR) and a two-compartment reactor (TCR) to mimic these substrate gradients at laboratory-scale continuous cultures. The IFR was applied to simulate substrate dynamics experienced by the cells at full scale at timescales of tens of seconds to minutes (30 s, 3 min and 6 min), while the TCR was designed to simulate substrate gradients at an applied mean residence time ((Formula presented.)) of 6 min. A biological systems analysis of the response of an industrial high-yielding P. chrysogenum strain has been performed in these continuous cultures. Compared to an undisturbed continuous feeding regime in a single reactor, the penicillin productivity (q_PenG) was reduced in all scale-down simulators. The dynamic metabolomics data indicated that in the IFRs, the cells accumulated high levels of the central metabolites during the feast phase to actively cope with external substrate deprivation during the famine phase. In contrast, in the TCR system, the storage pool (e.g. mannitol and arabitol) constituted a large contribution of carbon supply in the non-feed compartment. Further, transcript analysis revealed that all scale-down simulators gave different expression levels of the glucose/hexose transporter genes and the penicillin gene clusters. The results showed that q_PenG did not correlate well with exposure to the substrate regimes (excess, limitation and starvation), but there was a clear inverse relation between q_PenG and the intracellular glucose level.

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In the present work, by performing chemostat experiments at 400 and 600 RPM, two typical power inputs representative of industrial penicillin fermentation (P/V, 1.00 kW/m³ in more remote zones and 3.83 kW/m³ in the vicinity of the impellers, respectively) were scaled-down to bench-scale bioreactors. It was found that at 400 RPM applied in prolonged glucose-limited chemostat cultures, the previously reported degeneration of penicillin production using an industrial Penicillium chrysogenum strain was virtually absent. To investigate this, the cellular response was studied at flux (stoichiometry), residual glucose, intracellular metabolite and transcript levels. At 600 RPM, 20% more cell lysis was observed and the increased degeneration of penicillin production was accompanied by a 22% larger ATP gap and an unexpected 20-fold decrease in the residual glucose concentration (C_s,out). At the same time, the biomass specific glucose consumption rate (q_s) did not change but the intracellular glucose concentration was about sixfold higher, which indicates a change to a higher affinity glucose transporter at 600 RPM. In addition, power input differences cause differences in the diffusion rates of glucose and the calculated Batchelor diffusion length scale suggests the presence of a glucose diffusion layer at the glucose transporting parts of the hyphae, which was further substantiated by a simple proposed glucose diffusion-uptake model. By analysis of calculated mass action ratios (MARs) and energy consumption, it indicated that at 600 RPM glucose sensing and signal transduction in response to the low C_s,out appear to trigger a gluconeogenic type of metabolic flux rearrangement, a futile cycle through the pentose phosphate pathway (PPP) and a declining redox state of the cytosol. In support of the change in glucose transport and degeneration of penicillin production at 600 RPM, the transcript levels of the putative high-affinity glucose/hexose transporter genes Pc12g02880 and Pc06g01340 increased 3.5- and 3.3-fold, respectively, and those of the pcbC gene encoding isopenicillin N-synthetase (IPNS) were more than twofold lower in the time range of 100–200 hr of the chemostat cultures. Summarizing, changes at power input have unexpected effects on degeneration and glucose transport, and result in significant metabolic rearrangements. These findings are relevant for the industrial production of penicillin, and other fermentations with filamentous microorganisms.

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