Microbes experience dynamic conditions in natural habitats as well as in engineered environments, such as large-scale bioreactors, which exhibit increased mixing times and inhomogeneities. While single perturbations have been studied for several organisms and substrates, the impact of recurring short-term perturbations remains largely unknown. In this study, we investigated the response of Saccharomyces cerevisiae to repetitive gradients of four different sugars: glucose, fructose, sucrose, and maltose. Due to different transport mechanisms and metabolic routes, nonglucose sugars lead to varied intracellular responses. To characterize the impact of the carbon sources and the dynamic substrate gradients, we applied both steady-state and dynamic cultivation conditions, comparing the physiology, intracellular metabolome, and proteome. For maltose, the repeated concentration gradients led to a significant decrease in biomass yield. Under glucose, fructose, and sucrose conditions, S. cerevisiae maintained the biomass yield observed under steady-state conditions. Although the physiology was very similar across the different sugars, the intracellular metabolome and proteome were clearly differentiated. Notably, the concentration of upper glycolytic enzymes decreased for glucose and maltose (up to −60% and −40%, respectively), while an increase was observed for sucrose and fructose when exposed to gradients. Nevertheless, for all sugar gradient conditions, a stable energy charge was maintained, ranging between 0.78 and 0.89. This response to maltose is particularly distinct compared to previous single-substrate pulse experiments or limitation to excess shifts, which led to maltose-accelerated death in earlier studies. At the same time, enzymes of lower glycolysis were elevated. Interestingly, common stress-related proteins (GO term: cellular response to oxidative stress) decreased during dynamic conditions.

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We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments.

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With the rapid growing availability of metagenome assembled genomes (MAGs) and associated metabolic models, the identification of metabolic potential in individual community members has become possible. However, the field still lacks an unbiassed systematic evaluation of the generated metagenomic information to uncover not only metabolic potential, but also feasibilities of these models under specific environmental conditions. In this study, we present a systematic analysis of the metabolic potential in species of "Candidatus Accumulibacter", a group of polyphosphate-accumulating organisms (PAOs). We constructed a metabolic model of the central carbon metabolism and compared the metabolic potential among available MAGs for “Ca. Accumulibacter” species. By combining Elementary Flux Modes Analysis (EFMA) with max-min driving force (MDF) optimization, we obtained all possible flux distributions of the metabolic network and calculated their individual thermodynamic feasibility. Our findings reveal significant variations in the metabolic potential among “Ca. Accumulibacter” MAGs, particularly in the presence of anaplerotic reactions. EFMA revealed 700 unique flux distributions in the complete metabolic model that enable the anaerobic uptake of acetate and its conversion into polyhydroxyalkanoates (PHAs), a well-known phenotype of “Ca. Accumulibacter”. However, thermodynamic constraints narrowed down this solution space to 146 models that were stoichiometrically and thermodynamically feasible (MDF > 0 kJ/mol), of which only 8 were strongly feasible (MDF > 7 kJ/mol). Notably, several novel flux distributions for the metabolic model were identified, suggesting putative, yet unreported, functions within the PAO communities. Overall, this work provides valuable insights into the metabolic variability among "Ca. Accumulibacter" species and redefines the anaerobic metabolic potential in the context of phosphate removal. More generally, the integrated workflow presented in this paper can be applied to any metabolic model obtained from a MAG generated from microbial communities to objectively narrow the expected phenotypes from community members.

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Both the identity and the amount of a carbon source present in laboratory or industrial cultivation media have major impacts on the growth and physiology of a microbial species. In the case of the yeast Saccharomyces cerevisiae, sucrose is arguably the most important sugar used in industrial biotechnology, whereas glucose is the most common carbon and energy source used in research, with many well-known and described regulatory effects, e.g. glucose repression. Here we compared the label-free proteomes of exponentially growing S. cerevisiae cells in a defined medium containing either sucrose or glucose as the sole carbon source. For this purpose, bioreactor cultivations were employed, and three different strains were investigated, namely: CEN.PK113-7D (a common laboratory strain), UFMG-CM-Y259 (a wild isolate), and JP1 (an industrial bioethanol strain). These strains present different physiologies during growth on sucrose; some of them reach higher specific growth rates on this carbon source, when compared to growth on glucose, whereas others display the opposite behavior. It was not possible to identify proteins that commonly presented either higher or lower levels during growth on sucrose, when compared to growth on glucose, considering the three strains investigated here, except for one protein, named Mnp1—a mitochondrial ribosomal protein of the large subunit, which had higher levels on sucrose than on glucose, for all three strains. Interestingly, following a Gene Ontology overrepresentation and KEGG pathway enrichment analyses, an inverse pattern of enriched biological functions and pathways was observed for the strains CEN.PK113-7D and UFMG-CM-Y259, which is in line with the fact that whereas the CEN.PK113-7D strain grows faster on glucose than on sucrose, the opposite is observed for the UFMG-CM-Y259 strain.

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Utilizing anaerobic metabolisms for the production of biotechnologically relevant products presents potential advantages, such as increased yields and reduced energy dissipation. However, lower energy dissipation may indicate that certain reactions are operating closer to their thermodynamic equilibrium. While stoichiometric analyses and genetic modifications are frequently employed in metabolic engineering, the use of thermodynamic tools to evaluate the feasibility of planned interventions is less documented. In this study, we propose a novel metabolic engineering strategy to achieve an efficient anaerobic production of poly-(R)-3-hydroxybutyrate (PHB) in the model organism Escherichia coli. Our approach involves re-routing of two-thirds of the glycolytic flux through non-oxidative glycolysis and coupling PHB synthesis with NADH re-oxidation. We complemented our stoichiometric analysis with various thermodynamic approaches to assess the feasibility and the bottlenecks in the proposed engineered pathway. According to our calculations, the main thermodynamic bottleneck are the reactions catalyzed by the acetoacetyl-CoA β-ketothiolase (EC 2.3.1.9) and the acetoacetyl-CoA reductase (EC 1.1.1.36). Furthermore, we calculated thermodynamically consistent sets of kinetic parameters to determine the enzyme amounts required for sustaining the conversion fluxes. In the case of the engineered conversion route, the protein pool necessary to sustain the desired fluxes could account for 20% of the whole cell dry weight.

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Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, ¹³C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.

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The understanding of microbial communities and the biological regulation of its members is crucial for implementation of novel technologies using microbial ecology. One poorly understood metabolic principle of microbial communities is resource allocation and biosynthesis. Resource allocation theory in polyphosphate accumulating organisms (PAOs) is limited as a result of their slow imposed growth rate (typical sludge retention times of at least 4 days) and limitations to quantify changes in biomass components over a 6 hours cycle (less than 10% of their growth). As a result, there is no direct evidence supporting that biosynthesis is an exclusive aerobic process in PAOs that alternate continuously between anaerobic and aerobic phases. Here, we apply resource allocation metabolic flux analysis to study the optimal phenotype of PAOs over a temperature range of 4 °C to 20 °C. The model applied in this research allowed to identify optimal metabolic strategies in a core metabolic model with limited constraints based on biological principles. The addition of a constraint limiting biomass synthesis to be an exclusive aerobic process changed the metabolic behaviour and improved the predictability of the model over the studied temperature range by closing the gap between prediction and experimental findings. The results validate the assumption of limited anaerobic biosynthesis in PAOs, specifically “Candidatus Accumulibacter” related species. Interestingly, the predicted growth yield was lower, suggesting that there are mechanistic barriers for anaerobic growth not yet understood nor reflected in the current models of PAOs. Moreover, we identified strategies of resource allocation applied by PAOs at different temperatures as a result of the decreased catalytic efficiencies of their biochemical reactions. Understanding resource allocation is paramount in the study of PAOs and their currently unknown complex metabolic regulation, and metabolic modelling based on biological first principles provides a useful tool to develop a mechanistic understanding.

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Iron, supplemented as iron-loaded transferrin (holotransferrin), is an essential nutrient in mammalian cell cultures, particularly for erythroid cultures. The high cost of human transferrin represents a challenge for large scale production of red blood cells (RBCs) and for cell therapies in general. We evaluated the use of deferiprone, a cell membrane-permeable drug for iron chelation therapy, as an iron carrier for erythroid cultures. Iron-loaded deferiprone (Def₃·Fe³⁺, at 52 µmol/L) could eliminate the need for holotransferrin supplementation during in vitro expansion and differentiation of erythroblast cultures to produce large numbers of enucleated RBC. Only the first stage, when hematopoietic stem cells committed to erythroblasts, required holotransferrin supplementation. RBCs cultured in presence of Def₃·Fe³⁺ or holotransferrin (1000 µg/mL) were similar with respect to differentiation kinetics, expression of cell-surface markers CD235a and CD49d, hemoglobin content, and oxygen association/dissociation. Replacement of holotransferrin supplementation by Def₃·Fe³⁺ was also successful in cultures of myeloid cell lines (MOLM13, NB4, EOL1, K562, HL60, ML2). Thus, iron-loaded deferiprone can partially replace holotransferrin as a supplement in chemically defined cell culture medium. This holds promise for a significant decrease in medium cost and improved economic perspectives of the large scale production of red blood cells for transfusion purposes.

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Transfusion of donor-derived red blood cells (RBCs) is the most common form of cell therapy. Production of transfusion-ready cultured RBCs (cRBCs) is a promising replacement for the current, fully donor-dependent therapy. A single transfusion unit, however, contains 2 × 10¹² RBC, which requires large scale production. Here, we report on the scale-up of cRBC production from static cultures of erythroblasts to 3 L stirred tank bioreactors, and identify the effect of operating conditions on the efficiency of the process. Oxygen requirement of proliferating erythroblasts (0.55–2.01 pg/cell/h) required sparging of air to maintain the dissolved oxygen concentration at the tested setpoint (2.88 mg O₂/L). Erythroblasts could be cultured at dissolved oxygen concentrations as low as 0.7 O₂ mg/ml without negative impact on proliferation, viability or differentiation dynamics. Stirring speeds of up to 600 rpm supported erythroblast proliferation, while 1800 rpm led to a transient halt in growth and accelerated differentiation followed by a recovery after 5 days of culture. Erythroblasts differentiated in bioreactors, with final enucleation levels and hemoglobin content similar to parallel cultures under static conditions.

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Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

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Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism Saccharomyces cerevisiae by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in S. cerevisiae is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived “overcapacity” present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model’s ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.

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The coupling of PHB generation with NADH reoxidation is required to generate PHB as a fermentation product. A fundamental trait to accomplish this feature is to express a functional NADH-preferring acetoacetyl-CoA reductase, engaged in PHB accumulation. One way to obtain such a reductase is by engineering the cofactor preference of the acetoacetyl-CoA reductase encoded by the phaB1 gene from Cupriavidus necator (AAR^Cn1). Aiming to have a deeper understanding of the structural determinants of the cofactor preference in AAR^Cn1, and to obtain an NADH-preferring acetoacetyl-CoA reductase derived from this protein, some engineered enzymes were expressed, purified and kinetically characterized, together with the parental AAR^Cn1. One of these engineered enzymes, Chimera 5, experimentally showed a selectivity ratio ((k_cat/K_M)^NADH/(k_cat/K_M)^NADPH) ≈ 18, which is 160 times higher than the selectivity ratio experimentally observed in the parental AAR^Cn1. A thermodynamic-kinetic approach was employed to estimate the cofactor preference and flux capacity of Chimera 5 under physiological conditions. According to this approach, Chimera 5 could prefer NADH over NADPH between 25 and 150 times. Being a derivative of AAR^Cn1, Chimera 5 should be readily functional in Escherichia coli and C. necator. Moreover, with the expected expression level, its activity should be enough to sustain PHB accumulation fluxes similar to the fluxes previously observed in these biotechnologically relevant cell factories.

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Lactate production in anaerobic carbohydrate fermentations with mixed cultures of microorganisms is generally observed only in very specific conditions: the reactor should be run discontinuously and peptides and B vitamins must be present in the culture medium as lactic acid bacteria (LAB) are typically auxotrophic for amino acids. State-of-the-art anaerobic fermentation models assume that microorganisms optimise the adenosine triphosphate (ATP) yield on substrate and therefore they do not predict the less ATP efficient lactate production, which limits their application for designing lactate production in mixed-culture fermentations. In this study, a metabolic model taking into account cellular resource allocation and limitation is proposed to predict and analyse under which conditions lactate production from glucose can be beneficial for microorganisms. The model uses a flux balances analysis approach incorporating additional constraints from the resource allocation theory and simulates glucose fermentation in a continuous reactor. This approach predicts lactate production is predicted at high dilution rates, provided that amino acids are in the culture medium. In minimal medium and lower dilution rates, mostly butyrate and no lactate is predicted. Auxotrophy for amino acids of LAB is identified to provide a competitive advantage in rich media because less resources need to be allocated for anabolic machinery and higher specific growth rates can be achieved. The Matlab™ codes required for performing the simulations presented in this study are available at https://doi.org/10.5281/zenodo.4031144.

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Present knowledge on the quantitative aerobic physiology of the yeast Saccharomyces cerevisiae during growth on sucrose as sole carbon and energy source is limited to either adapted cells or to the model laboratory strain CEN.PK113-7D. To broaden our understanding of this matter and open novel opportunities for sucrose-based biotechnological processes, we characterized three strains, with distinct backgrounds, during aerobic batch bioreactor cultivations. Our results reveal that sucrose metabolism in S. cerevisiae is a strain-specific trait. Each strain displayed distinct extracellular hexose concentrations and invertase activity profiles. Especially, the inferior maximum specific growth rate (0.21 h-1) of the CEN.PK113-7D strain, with respect to that of strains UFMG-CM-Y259 (0.37 h-1) and JP1 (0.32 h-1), could be associated to its low invertase activity (0.04-0.09 U/mgDM). Moreover, comparative experiments with glucose or fructose alone, or in combination, suggest mixed mechanisms of sucrose utilization by the industrial strain JP1, and points out the remarkable ability of the wild isolate UFMG-CM-259 to grow faster on sucrose than on glucose in a well-controlled cultivation system. This work hints to a series of metabolic traits that can be exploited to increase sucrose catabolic rates and bioprocess efficiency.

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Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions – including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.@en

13C-isotope tracing is a frequently employed approach to study metabolic pathway activity. When combined with the subsequent quantification of absolute metabolite concentrations, this enables detailed characterization of the metabolome in biological specimens and facilitates computational time-resolved flux quantification. Classically, a 13C-isotopically labeled sample is required to quantify 13C-isotope enrichments and a second unlabeled sample for the quantification of metabolite concentrations. The rationale for a second unlabeled sample is that the current methods for metabolite quantification rely mostly on isotope dilution mass spectrometry (IDMS) and thus isotopically labeled internal standards are added to the unlabeled sample. This excludes the absolute quantification of metabolite concentrations in 13C-isotopically labeled samples. To address this issue, we have developed and validated a new strategy using an unlabeled internal standard to simultaneously quantify metabolite concentrations and 13C-isotope enrichments in a single 13C-labeled sample based on gas chromatography-mass spectrometry (GC/MS). The method was optimized for amino acids and citric acid cycle intermediates and was shown to have high analytical precision and accuracy. Metabolite concentrations could be quantified in small tissue samples (≥20 mg). Also, we applied the method on 13C-isotopically labeled mammalian cells treated with and without a metabolic inhibitor. We proved that we can quantify absolute metabolite concentrations and 13C-isotope enrichments in a single 13C-isotopically labeled sample.

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Biosynthesis of poly-3-hydroxybutyrate (PHB) as a fermentation product enables the coupling of growth and product generation. Moreover, the reduction of oxygen supply should reduce operative cost and increase product yield. Generation of PHB as a fermentation product depends on the in vivo activity of an NADH-preferring acetoacetyl-CoA reductase. Proof of this concept requires (i) quantification of the cofactor preference, in physiologically relevant conditions, of a putative NADH-preferring acetoacetyl-CoA reductase and (ii) verification of PHB accumulation using an NADH-preferring acetoacetyl-CoA reductase in a species naturally incapable of doing so, for example, Escherichia coli. This dataset contains kinetic data obtained by spectrophotometry and data from a continuous culture of an engineered E. coli strain accumulating PHB under oxygen-limiting conditions. In this dataset it is possible to find (1) enzyme stability assays; (2) initial rates and progress curves from reactions catalyzed by two acetoacetyl-CoA reductases; (3) estimations of the relative use of NADH and NADPH by two acetoacetyl-CoA reductases; (4) estimations of the flux capacity of the reaction catalyzed by an acetoacetyl-CoA reductase; (5) biomass composition of an engineered E. coli strain transformed with a plasmid; (6) calculation of reconciled specific rates of this engineered strain growing on sucrose as the sole carbon source under oxygen limitation and (7) metabolic fluxes distributions during the continuous growth of this engineered strain. Because a relatively small number of acetoacetyl-CoA reductases have been kinetically characterized, data and scripts here provided could be useful for further kinetic characterizations. Moreover, the procedure described to estimate biomass composition could be interesting to estimate plasmid and protein burden in other strains. Application of data reconciliation to fermentations should help to obtain specific rates consistent with the principle of mass and electron conservation. All the required data and scripts to perform these analyses are deposited in a Mendeley Data repository. This article was co-submitted with the manuscript entitled “An NADH preferring acetoacetyl-CoA reductase is engaged in poly-3-hydroxybutyrate accumulation in Escherichiasia. coli”.

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Environmental fluctuations in the availability of nutrients lead to intricate metabolic strategies. "Candidatus Accumulibacter phosphatis," a polyphosphate-accumulating organism (PAO) responsible for enhanced biological phosphorus removal (EBPR) from wastewater treatment systems, is prevalent in aerobic/anaerobic environments. While the overall metabolic traits of these bacteria are well described, the nonavailability of isolates has led to controversial conclusions on the metabolic pathways used. In this study, we experimentally determined the redox cofactor preferences of different oxidoreductases in the central carbon metabolism of a highly enriched "Ca Accumulibacter phosphatis" culture. Remarkably, we observed that the acetoacetyl coenzyme A reductase engaged in polyhydroxyalkanoate (PHA) synthesis is NADH preferring instead of showing the generally assumed NADPH dependency. This allows rethinking of the ecological role of PHA accumulation as a fermentation product under anaerobic conditions and not just a stress response. Based on previously published metaomics data and the results of enzymatic assays, a reduced central carbon metabolic network was constructed and used for simulating different metabolic operating modes. In particular, scenarios with different acetate-to-glycogen consumption ratios were simulated, which demonstrated optima using different combinations of glycolysis, glyoxylate shunt, or branches of the tricarboxylic acid (TCA) cycle. Thus, optimal metabolic flux strategies will depend on the environment (acetate uptake) and on intracellular storage compound availability (polyphosphate/glycogen). This NADH-related metabolic flexibility is enabled by the NADH-driven PHA synthesis. It allows for maintaining metabolic activity under various environmental substrate conditions, with high carbon conservation and lower energetic costs than for NADPH-dependent PHA synthesis. Such (flexible) metabolic redox coupling can explain the competitiveness of PAOs under oxygen-fluctuating environments.IMPORTANCE Here, we demonstrate how microbial storage metabolism can adjust to a wide range of environmental conditions. Such flexibility generates a selective advantage under fluctuating environmental conditions. It can also explain the different observations reported in PAO literature, including the capacity of "Ca Accumulibacter phosphatis" to act like glycogen-accumulating organisms (GAOs). These observations stem from slightly different experimental conditions, and controversy arises only when one assumes that metabolism can operate only in a single mode. Furthermore, we also show how the study of metabolic strategies is possible when combining omics data with functional cofactor assays and modeling. Genomic information can only provide the potential of a microorganism. The environmental context and other complementary approaches are still needed to study and predict the functional expression of such metabolic potential.

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The global market of butanol is increasing due to its growing applications as solvent, flavoring agent, and chemical precursor of several other compounds. Recently, the superior properties of n-butanol as a biofuel over ethanol have stimulated even more interest. (Bio)butanol is natively produced together with ethanol and acetone by Clostridium species through acetone-butanol-ethanol fermentation, at noncompetitive, low titers compared to petrochemical production. Different butanol production pathways have been expressed in Escherichia coli, a more accessible host compared to Clostridium species, to improve butanol titers and rates. The bioproduction of butanol is here reviewed from a historical and theoretical perspective. All tested rational metabolic engineering strategies in E. coli to increase butanol titers are reviewed: manipulation of central carbon metabolism, elimination of competing pathways, cofactor balancing, development of new pathways, expression of homologous enzymes, consumption of different substrates, and molecular biology strategies. The progress in the field of metabolic modeling and pathway generation algorithms and their potential application to butanol production are also summarized here. The main goals are to gather all the strategies, evaluate the respective progress obtained, identify, and exploit the outstanding challenges.

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Background: Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions. Results: In this study we applied a repetitive feast-famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast-famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast-famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/g_CDW/s) than reported during batch growth (3.3 μmol/g_CDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions. Conclusions: The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

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