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The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named i JO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. i JO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the i JO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance. Synopsis Bacterial metabolism constrains the evolution of antibiotic resistance. A modeling approach is developed to interpret the functionality of metabolic rewiring in resistance‐evolving E. coli growing on glycolytic or gluconeogenic carbon sources from metabolomics data. Large‐scale untargeted metabolome profiling reveals metabolic adaptations in 190 evolved antibiotic‐resistant E. coli populations, in part as compensation for consequences of the primary resistance mechanisms. Carbon and energy metabolism strongly constrain the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. A novel constraint‐based modeling approach, together with genome re‐sequencing of resistant populations, identifies condition‐dependent compensatory mechanisms. Graphical Abstract Bacterial metabolism constrains the evolution of antibiotic resistance. A modeling approach is developed to interpret the functionality of metabolic rewiring in resistance‐evolving E. coli growing on glycolytic or gluconeogenic carbon sources from metabolomics data.

The genome‐scale model (GEM) of metabolism in the bacterium Escherichia coli K‐12 has been in development for over a decade and is now in wide use. GEM‐enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model‐driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome‐scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large‐scale network models with sufficient accuracy. This review summarizes the applications enabled by genome‐scale models of metabolism for the bacterium E. coli . It provides an overview of the applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field.

Acetobacterium woodii is known to produce mainly acetate from CO2 and H2, but the production of higher value chemicals is desired for the bioeconomy. Using chain‐elongating bacteria, synthetic co‐cultures have the potential to produce longer‐chained products such as caproic acid. In this study, we present first results for a successful autotrophic co‐cultivation of A. woodii mutants and a Clostridium drakei wild‐type strain in a stirred‐tank bioreactor for the production of caproic acid from CO2 and H2 via the intermediate lactic acid. For autotrophic lactate production, a recombinant A. woodii strain with a deleted Lct‐dehydrogenase complex, which is encoded by the lctBCD genes, and an inserted D‐lactate dehydrogenase (LdhD) originating from Leuconostoc mesenteroides, was used. Hydrogen for the process was supplied using an All‐in‐One electrode for in situ water electrolysis. Lactate concentrations as high as 0.5 g L–1 were achieved with the AiO‐electrode, whereas 8.1 g L–1 lactate were produced with direct H2 sparging in a stirred‐tank bioreactor. Hydrogen limitation was identified in the AiO process. However, with cathode surface area enlargement or numbering‐up of the electrode and on‐demand hydrogen generation, this process has great potential for a true carbon‐negative production of value chemicals from CO2.

Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome‐scale modeling and multi‐omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome‐scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well‐known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de‐novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi‐omic data obtained from LPS‐stimulated RAW cells in the context of our flux‐based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors. Genome‐scale metabolic network reconstruction and analysis of the murine leukemic macrophage cell line RAW 264.7 reveal a complementary relationship between how known metabolic immunomodulators are biochemically processed and their role in macrophage activation. Synopsis Genome‐scale metabolic network reconstruction and analysis of the murine leukemic macrophage cell line RAW 264.7 reveal a complementary relationship between how known metabolic immunomodulators are biochemically processed and their role in macrophage activation. The RAW 264.7 metabolic model was constructed based on transcriptomic and proteomic data, and validated for its quantitative accuracy in the prediction of growth rate, ATP, and nitric oxide production. Metabolic network‐based analyses identified well‐established critical metabolite effectors and intracellular pathways that impact activation or suppression of M1‐ and M2‐metabolic activation phenotypes. Three levels of high‐throughput data (transcriptomic, proteomic, and metabolomic) were analyzed in the context of the model‐based predictions to elucidate underlying metabolic mechanisms of macrophage activation. Results suggest a potential contending link between de‐novo nucleotide synthesis and macrophage activation phenotypes at a glutamine junction.

The maximum entropy theory of ecology (METE) applies the concept of “entropy” from information theory to predict macroecological patterns. The energetic predictions of the METE rely on predetermined metabolic scaling from external theories, and this reliance diminishes the testability of the theory. In this work, I build parameterized METE models by treating the metabolic scaling exponent as a free parameter, and I use the maximum-likelihood method to obtain empirically plausible estimates of the exponent. I test the models using the individual tree data from an oak-dominated deciduous forest in the northeastern United States and from a tropical forest in central Panama. My analysis shows that the metabolic scaling exponents predicted from the parameterized METE models deviate from that of the metabolic theory of ecology and exhibit large variation, at both community and population levels. Assemblage and population abundance may act as ecological constraints that regulate the individual- level metabolic scaling behavior. This study provides a novel example of the use of the parameterized METE models to reveal the biological processes of individual organisms. The implication and possible extensions of the parameterized METE models are discussed.

The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. Despite extensive research, the biochemical basis for this phenomenon remains obscure. We hypothesize that the outcome of a competition for membrane space between glucose transporters and respiratory chain (which we refer to as economics of membrane occupancy) proteins influences respiration and fermentation. By incorporating a sole constraint based on this concept in the genome‐scale metabolic model of Escherichia coli , we were able to simulate respiro‐fermentation. Further analysis of the impact of this constraint revealed differential utilization of the cytochromes and faster glucose uptake under anaerobic conditions than under aerobic conditions. Based on these simulations, we propose that bacterial cells manage the composition of their cytoplasmic membrane to maintain optimal ATP production by switching between oxidative and substrate‐level phosphorylation. These results suggest that the membrane occupancy constraint may be a fundamental governing constraint of cellular metabolism and physiology, and establishes a direct link between cell morphology and physiology. Synopsis Many heterotrophs can produce ATP through both respiratory and fermentative pathways, allowing them to survive with or without oxygen. Since the molar ATP yield (molar ATP yield: mole of ATP produced/mole of substrate consumed) from respiration is about 15‐fold higher than that from fermentation, ATP production via respiration is more efficient. Surprisingly, at high catabolic rate, many facultative aerobic organisms employ fermentative pathways simultaneously with respiration, even in the presence of abundant oxygen to produce ATP (Pfeiffer et al , 2001 ; Vemuri et al , 2006 ; Molenaar et al , 2009 ). This leads to an observable tradeoff between the ATP yield and the catabolic rate (Pfeiffer et al , 2001 ; Vemuri et al , 2006 ). This respiro‐fermentation physiology is commonly observed in microorganisms, including Escherichia coli , Bacillus subtilis , Saccharomyces cerevisiae (Molenaar et al , 2009 ), as well as cancer cells (Vander Heiden et al , 2009 ). Despite extensive research, existing theories (Majewski and Domach, 1990 ; Varma and Palsson, 1994 ; Pfeiffer et al , 2001 ; Vazquez et al , 2008 ; Molenaar et al , 2009 ) cannot fully explain the respiro‐fermentation phenomenon. The membrane economics theory We propose the hypothesis that the prokaryotic metabolism is fundamentally constrained by the finite cytoplasmic surface area available for protein expression—in order to maximize fitness, prokaryotic organisms such as E. coli must economically manage the expression of membrane proteins based on the membrane cost and the fitness benefit of the proteins. This hypothesis is proposed based on theoretical considerations (in this work), numerical analysis (Phillips and Milo, 2009 ), and experimental observation that the overexpression of non‐respiratory membrane protein significantly reduces the oxygen consumption rate and induces aerobic fermentation (Wagner et al , 2007 ). Such a constraint on transmembrane protein expression may have significant physiological consequences in prokaryotes, such as E. coli , at higher catabolic rates. First, since both substrate transporters and respiratory enzymes are localized on the cytoplasmic membrane in prokaryotes, increased substrate uptake rates necessitates a decrease in the respiratory rate. This decrease in the respiratory rate, forces prokaryotes to process the additional substrate through the fermentative pathways, which are not catalyzed by transmembrane proteins, for continued ATP production. Furthermore, since the membrane requirement of an enzyme is inversely related to its turnover rate (see Materials and methods section in the manuscript), the faster and inefficient respiratory enzymes (such as Cyd‐I and Cyd‐II in E. coli ) might be preferred over the slower and efficient enzymes (such as Cyo in E. coli ), leading to an altered respiratory stoichiometry at higher catabolic rates. Finally, the absence of the respiratory enzymes under anaerobic conditions explains why the maximum glucose uptake rate (GUR) of E. coli is much higher. Applying membrane economics theory to E. coli To illustrate that the ‘membrane economics’ theory could satisfactorily explain the physiological changes associated with the respiro‐fermentation phenomenon in E. coli , we modified the genome‐scale metabolic model of E. coli (Feist et al , 2007 ) to include a cytoplasmic membrane occupancy constraint. Using ‘relative membrane costs’ calculated from experimental data, the new modeling framework—FBA with membrane economics (FBA ME )—predicted that wild‐type E. coli has a GUR of 10.7 mmol/gdw/h, an oxygen uptake rate (OUR) of 15.8 mmol/gdw/h, and a specific growth rate of 0.69 per hour during aerobic growth with excess glucose. FBA ME also predicted that under the same growth condition, an E. coli knockout strain with no cytochromes has a GUR of 18 mmol/gdw/h and growth rate of 0.42. These values agree very well with the reported experimental values for E. coli grown in batch cultures (Vemuri et al , 2006 ; Portnoy et al , 2008 ), which supports our hypothesis that the higher GUR of E. coli during glucose‐excess anaerobiosis than under aerobic conditions is due to the absence of the respiratory enzymes. We also simulated the aerobic growth of E. coli in glucose‐limited chemostat using both conventional FBA and FBA ME . FBA ME successfully predicted the growth rate and yield changes with respect to increasing GUR (Figure 2A and B ), as well as the aerobic production of acetate (Figure 2C ) and concomitant repression of oxygen uptake (Figure 2D ). On the other hand, traditional FBA significantly overestimated the growth rate and yield at higher GURs (this overestimation cannot be explained by varying the growth‐associated maintenance (GAM) energy parameter; Figure 2A ), and failed to predict the decrease in yield independent of acetate overflow and reduction in oxygen uptake at higher GURs (Figure 2 ). In addition, FBA ME was able to predict the reduction of the TCA cycle activities at higher uptake rates (Figure 3C and D ) as well as the selective expression of Cyo and Cyd‐II at lower uptake rates (Figure 3A and B ), whereas conventional FBA cannot predict the expression of inefficient Cyd‐II. These predictions agree with the gene expression data from glucose‐limited chemostat (Figure 3 ). Given the simplicity of the constraint we imposed, our model predictions agree surprisingly well with experimental observations, lending strong credibility to the membrane economics hypothesis. Concluding remarks Although it has been long suggested that cellular evolution are governed by non‐adjustable mechanistic constraints (Palsson, 2000 ; Papin et al , 2005 ; Novak et al , 2006 ), to date, most metabolic models rely on empirically derived parameters such as glucose and OUR. In this article, we showed that complex phenomena, such as the respiro‐fermentation in E. coli , could be satisfactorily explained and accurately predicted by using constraint‐based optimization by introducing a simple mechanistic constraint on membrane enzyme occupancy. Given that the cytoplasmic membrane occupancy constraint is directly related to the surface area to volume (S/V) ratio of the cell, it is possible that this constraint resulted in the evolution of mitochondria in eukaryotes as mitochondria allows for a significantly increased S/V ratio. Further efforts to elucidate such fundamental cellular constraints as well as the underlying design principles could significantly improve our understanding of the regulation and evolution of metabolism. We propose that prokaryotic cellular metabolism is fundamentally constrained by the finite cytoplasmic membrane surface area available for protein expression. A metabolic model of Escherichia coli updated to include a cytoplasmic membrane constraint is capable of predicting a variety of puzzling phenomena in this organism, including the respiro‐fermentation phenomenon. Because the surface area to volume ratio is directly related to the morphology of the cell, this constraint provides a direct link between prokaryotic morphology and physiology. The potential relevance of this constraint to eukaryotes is discussed.

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome‐scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red‐blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome‐scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10‐fold increase in biomarker detection performance.

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high‐throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human‐defined pathways. Here, we introduce an unbiased, pathway structure for genome‐scale metabolic networks defined based on principles of parsimony that do not mimic canonical human‐defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway‐associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated. Synopsis The MinSpan algorithm is presented that defines the shortest functional metabolic pathways at the genome scale, based on whole network function and parsimonious use of cellular components. The pathways are biologically supported by biomolecular interaction networks. Pathways are traditionally defined by biochemical experimentation and are the universal paradigm for describing cellular processes. The MinSpan algorithm defines pathways at the genome scale using metabolic network reconstructions based on a principle of minimal use of biochemical transformations. MinSpan derived pathways are as or more representative of the underlying protein–protein, positive genetic, and transcriptional regulatory interactions compared to traditional pathways. The MinSpan pathways are used in conjunction with constraint‐based modeling to predict transcriptional regulation in E. coli . Graphical Abstract The MinSpan algorithm is presented that defines the shortest functional metabolic pathways at the genome scale, based on whole network function and parsimonious use of cellular components. The pathways are biologically supported by biomolecular interaction networks.

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint‐based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA‐Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue‐specific genome‐scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well‐known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin‐converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS‐CoV‐2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment. Identifying differentially expressed genes is critical in exploring molecular mechanisms of cancers. A high level of differential gene expression is not an essential factor for tumorigenesis. We developed an optimization platform that can identify oncogenes with low levels of differential expression and potential therapeutic targets for cancer treatment. The platform was applied on LUAD and LUSC to illustrate its performance.