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

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

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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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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Commercial-scale bioreactors create an unnatural environment for microbes from an evolutionary point of view. Mixing insufficiencies expose individual cells to fluctuating nutrient concentrations on a second-to-minute scale while transcriptional and translational capacities limit the microbial adaptation time from minutes to hours. This mismatch carries the risk of inadequate adaptation effects, especially considering that nutrients are available at optimal concentrations on average. Consequently, industrial bioprocesses that strive to maintain microbes in a phenotypic sweet spot, during lab-scale development, might suffer performance losses when said adaptive misconfigurations arise during scale-up. Here, we investigated the influence of fluctuating glucose availability on the gene-expression profile in the industrial yeast Ethanol Red™. The stimulus-response experiment introduced 2 min glucose depletion phases to cells growing under glucose limitation in a chemostat. Even though Ethanol Red™ displayed robust growth and productivity, a single 2 min depletion of glucose transiently triggered the environmental stress response. Furthermore, a new growth phenotype with an increased ribosome portfolio emerged after complete adaptation to recurring glucose shortages. The results of this study serve a twofold purpose. First, it highlights the necessity to consider the large-scale environment already at the experimental development stage, even when process-related stressors are moderate. Second, it allowed the deduction of strain engineering guidelines to optimize the genetic background of large-scale production hosts.

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Abstract: Syngas fermentation is a leading microbial process for the conversion of carbon monoxide, carbon dioxide, and hydrogen to valuable biochemicals. Clostridium autoethanogenum stands as a model organism for this process, showcasing its ability to convert syngas into ethanol industrially with simultaneous fixation of carbon and reduction of greenhouse gas emissions. A deep understanding on the metabolism of this microorganism and the influence of operational conditions on fermentation performance is key to advance the technology and enhancement of production yields. In this work, we studied the individual impact of acetic acid concentration, growth rate, and mass transfer rate on metabolic shifts, product titres, and rates in CO fermentation by C. autoethanogenum. Through continuous fermentations performed at a low mass transfer rate, we measured the production of formate in addition to acetate and ethanol. We hypothesise that low mass transfer results in low CO concentrations, leading to reduced activity of the Wood–Ljungdahl pathway and a bottleneck in formate conversion, thereby resulting in the accumulation of formate. The supplementation of the medium with exogenous acetate revealed that undissociated acetic acid concentration increases and governs ethanol yield and production rates, assumedly to counteract the inhibition by undissociated acetic acid. Since acetic acid concentration is determined by growth rate (via dilution rate), mass transfer rate, and working pH, these variables jointly determine ethanol production rates. These findings have significant implications for process optimisation as targeting an optimal undissociated acetic acid concentration can shift metabolism towards ethanol production. Key points: • Very low CO mass transfer rate leads to leaking of intermediate metabolite formate. • Undissociated acetic acid concentration governs ethanol yield on CO and productivity. • Impact of growth rate, mass transfer rate, and pH were considered jointly. Graphical abstract: [Figure not available: see fulltext.]

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Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L⁻¹ impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.

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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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A novel fermentation process was developed in which renewable electricity is indirectly used as an energy source in fermentation, synergistically decreasing both the consumption of sugar as a first generation carbon source and emission of the greenhouse gas CO₂. As an illustration, a glucose-based process is co-fed with formic acid, which can be generated by capturing CO₂ from fermentation offgas followed by electrochemical reduction with renewable electricity. This “closed carbon loop” concept is demonstrated by a case study in which cofeeding formic acid is shown to significantly increase the yield of biomass on glucose of the industrially relevant yeast species Yarrowia lipolytica. First, the optimal feed ratio of formic acid to glucose is established using chemostat cultivations. Subsequently, guided by a dynamic fermentation process model, a fed-batch protocol is developed and demonstrated on laboratory scale. Finally, the developed fed-batch process is tested and proven to be scalable at pilot scale. Extensions of the concept are discussed to apply the concept to anaerobic fermentations, and to recycle the O₂ that is co-generated with the formic acid to aerobic fermentation processes for intensification purposes.

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This study describes a methodological framework designed for the systematic processing of experimental syngas fermentation data for its use by metabolic models at pseudo-steady state and at transient state. The developed approach allows the use of not only own experimental data but also from experiments reported in literature which employ a wide range of gas feed compositions (from pure CO to a mixture between H₂ and CO₂), different pH values, two different bacterial strains and bioreactor configurations (stirred tanks and bubble columns). The developed data processing framework includes i) the smoothing of time-dependent concentrations data (using moving averages and statistical methods that reduce the relevance of outliers), ii) the reconciliation of net conversion rates such that mass balances are satisfied from a black-box perspective (using minimizations), and iii) the estimation of dissolved concentrations of the syngas components (CO, H₂ and CO₂) in the fermentation broth (using mass transfer models). Special care has been given such that the framework allows the estimation of missing or unreported net conversion data and metabolite concentrations at the intra or extracellular spaces (considering that there is availability of at least two replicate experiments) through the use of approximative kinetic equations.

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This study explores key success factors for ethanol production via fermentation of gas streams, by assessing the effects of eight process variables driving the fermentation performance on the production costs and greenhouse gas emissions. Three fermentation feedstocks are assessed: off-gases from the steel industry, lignocellulosic biomass-derived syngas and a mixture of H₂ and CO₂. The analysis is done through a sequence of (i) sensitivity analyses based on stochastic simulations and (ii) multi-objective optimizations. In economic terms, the use of steel off-gas leads to the best performance and the highest robustness to low mass transfer coefficients, low microbial tolerance to ethanol, acetic-acid co-production and to dilution of the gas feed with CO₂, due to the relatively high temperature at which the gas feedstock is available. The ethanol produced from the three feedstocks lead to lower greenhouse gas emissions than fossil-based gasoline and compete with first and second generation ethanol.

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In fed-batch operated industrial bioreactors, glucose-limited feeding is commonly applied for optimal control of cell growth and product formation. Still, microbial cells such as yeasts and bacteria are frequently exposed to glucose starvation conditions in poorly mixed zones or far away from the feedstock inlet point. Despite its commonness, studies mimicking related stimuli are still underrepresented in scale-up/scale-down considerations. This may surprise as the transition from glucose limitation to starvation has the potential to provoke regulatory responses with negative consequences for production performance. In order to shed more light, we performed gene-expression analysis of Saccharomyces cerevisiae grown in intermittently fed chemostat cultures to study the effect of limitation-starvation transitions. The resulting glucose concentration gradient was representative for the commercial scale and compelled cells to tolerate about 76 s with sub-optimal substrate supply. Special attention was paid to the adaptation status of the population by discriminating between first time and repeated entry into the starvation regime. Unprepared cells reacted with a transiently reduced growth rate governed by the general stress response. Yeasts adapted to the dynamic environment by increasing internal growth capacities at the cost of rising maintenance demands by 2.7%. Evidence was found that multiple protein kinase A (PKA) and Snf1-mediated regulatory circuits were initiated and ramped down still keeping the cells in an adapted trade-off between growth optimization and down-regulation of stress response. From this finding, primary engineering guidelines are deduced to optimize both the production host's genetic background and the design of scale-down experiments.

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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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In industrial large-scale bioreactors, microorganisms encounter heterogeneous substrate concentration conditions, which can impact growth or product formation. Here we carried out an extended (12 h) experiment of repeated glucose pulsing with a 10-min period to simulate fluctuating glucose concentrations with Aspergillus niger producing glucoamylase, and investigated its dynamic response by rapid sampling and quantitative metabolomics. The 10-min period represents worst-case conditions, as in industrial bioreactors the average cycling duration is usually in the order of 1 min. We found that cell growth and the glucoamylase productivity were not significantly affected, despite striking metabolomic dynamics. Periodical dynamic responses were found across all central carbon metabolism pathways, with different time scales, and the frequently reported ATP paradox was confirmed for this A. niger strain under the dynamic conditions. A thermodynamics analysis revealed that several reactions of the central carbon metabolism remained in equilibrium even under periodical dynamic conditions. The dynamic response profiles of the intracellular metabolites did not change during the pulse exposure, showing no significant adaptation of the strain to the more than 60 perturbation cycles applied. The apparent high tolerance of the glucoamylase producing A. niger strain for extreme variations in the glucose availability presents valuable information for the design of robust industrial microbial hosts.

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This work presents a strategy for optimizing the production process of ethanol via integrated gasification and syngas fermentation, a conversion platform of growing interest for its contribution to carbon recycling. The objective functions (minimum ethanol selling price (MESP), energy efficiency, and carbon footprint) were evaluated for the combinations of different input variables in models of biomass gasification, energy production from syngas, fermentation, and ethanol distillation, and a multi-objective genetic algorithm was employed for the optimization of the integrated process. Two types of waste feedstocks were considered, wood residues and sugarcane bagasse, with the former leading to lower MESP and a carbon footprint of 0.93 USD/L and 3 g CO2eq/MJ compared
to 1.00 USD/L and 10 g CO2eq/MJ for sugarcane bagasse. The energy efficiency was found to be 32% in both cases. An uncertainty analysis was conducted to determine critical decision variables, which were found to be the gasification zone temperature, the split fraction of the unreformed syngas sent to the combustion chamber, the dilution rate, and the gas residence time in the bioreactor. Apart from the abovementioned objectives, other aspects such as water footprint, ethanol yield, and energy
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Syngas fermentation to biofuels and chemicals is an emerging technology in the biobased economy. Mass transfer is usually limiting the syngas fermentation rate, due to the low aqueous solubilities of the gaseous substrates. Membrane bioreactors, as efficient gas–liquid contactors, are a promising configuration for overcoming this gas-to-liquid mass transfer limitation, so that sufficient productivity can be achieved. We summarize the published performances of these reactors. Moreover, we highlight numerous parameters settings that need to be used for the enhancement of membrane bioreactor performance. To facilitate this enhancement, we relate mass transfer and other performance indicators to the type of membrane material, module, and flow configuration. Hollow fiber modules with dense or asymmetric membranes on which biofilm might form seem suitable. A model-based approach is advocated to optimize their performance.

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This study assesses the sensitivity of the technical, environmental and economic performance of three ethanol production process based on the fermentation of three gas mixtures: i) CO-rich flue gas from steel manufacturing, ii) biomass-based syngas with a H₂/CO ratio of 2 and iii) a 3:1 combination between H₂ and CO₂. The sensitivity analysis is based on stochastic bioreactor simulations constructed by randomly generated combinations of eight parameters that command the fermentation process i.e., temperature, pressure, gas feed dilution with an inert components, ethanol concentration, height of the liquid column, mass transfer coefficients, superficial gas velocity and, acetic acid co-production. The sensitivity analysis identified that the bioreactor technical performance is highly sensitive to variations on pressure, liquid column height and the mass transfer coefficients. The pressure mainly improves mass transfer and consequently ethanol productivity whereas liquid column height improves the gas residence time and consequently the efficiency in the gas utilization. The trend was common for the three gas supply options. The results suggested that in order to produce an optimal bioreactor design, there are options to optimize the productivity and the gas utilization simultaneously. The results from the sensitivity analysis may help guiding a subsequent multi-objective process optimization study.

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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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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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In this work, a model was developed to predict the performance of a bubble column reactor for syngas fermentation and the subsequent recovery of anhydrous ethanol. The model was embedded in an optimization framework which employs surrogate models (artificial neural networks) and multi-objective genetic algorithm to optimize different process conditions and design variables with objectives related to investment, minimum selling price, energy efficiency and bioreactor productivity. The results indicate the optimal trade-offs between these objectives while providing a range of solutions such that, if desired, a single solution can be picked, depending on the priority conferred to different process targets. The Pareto-optimal values of the decision variables were discussed for different case studies with and without the recovery unit. It was shown that enhancing the gas-liquid mass transfer coefficient is a key strategy toward sustainability improvement.

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