A quantitative description of light-limited cyanobacterial growth using flux balance analysis.

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

Production of pyrimidine nucleosides in microbial systems via metabolic engineering: Theoretical analysis research and prospects.

Pyrimidine nucleosides, as intermediate materials of significant commercial value, find extensive applications in the pharmaceutical industry. However, the current production of pyrimidine nucleosides largely relies on chemical synthesis, creating environmental problems that do not align with sustainable development goals. Recent progress in systemic metabolic engineering and synthetic biology has enabled the synthesis of natural products like pyrimidine nucleosides through microbial fermentation, offering a more sustainable alternative. Nevertheless, the intricate and tightly regulated biosynthetic pathways involved in the microbial production of pyrimidine nucleosides pose a formidable challenge. This study focuses on metabolic engineering and synthetic biology strategies aimed at enhancing pyrimidine nucleoside production. These strategies include gene modification, transcriptional regulation, metabolic flux analysis, cofactor balance optimization, and transporter engineering. Finally, this research highlights the challenges involved in the further development of pyrimidine nucleoside-producing strains and offers potential solutions in order to provide theoretical guidance for future research endeavors in this field.

Extracellular flux assay (Seahorse assay): Diverse applications in metabolic research across biological disciplines.

Metabolic networks are fundamental to cellular processes, driving energy production, biosynthesis, redox regulation, and cellular signaling. Recent advancements in metabolic research tools have provided unprecedented insights into cellular metabolism. Among these tools, the extracellular flux analyzer stands out for its real-time measurement of key metabolic parameters: glycolysis, mitochondrial respiration, and fatty acid oxidation, leading to its widespread use. This review provides a comprehensive summary of the basic principles and workflow of the extracellular flux assay (the Seahorse assay) and its diverse applications. We highlight the assay's versatility across various biological models, including cancer cells, immunocytes, Caenorhabditis elegans, tissues, isolated mitochondria, and three-dimensional structures such as organoids, and summarize key considerations for using extracellular flux assay in these models. Additionally, we discuss the limitations of the Seahorse assay and propose future directions for its development. This review aims to enhance the understanding of extracellular flux assay and its significance in biological studies.

Evaluating the contribution of plant metabolic pathways in the light to the ATP:NADPH demand using a meta-analysis of isotopically non-stationary metabolic flux analyses.

Balancing the ATP: NADPH demand from plant metabolism with supply from photosynthesis is essential for preventing photodamage and operating efficiently, so understanding its drivers is important for integrating metabolism with the light reactions of photosynthesis and for bioengineering efforts that may radically change this demand. It is often assumed that the C3 cycle and photorespiration consume the largest amount of ATP and reductant in illuminated leaves and as a result mostly determine the ATP: NADPH demand. However, the quantitative extent to which other energy consuming metabolic processes contribute in large ways to overall ATP: NADPH demand remains unknown. Here, we used the metabolic flux networks of numerous recently published isotopically non-stationary metabolic flux analyses (INST-MFA) to evaluate flux through the C3 cycle, photorespiration, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and starch/sucrose synthesis and characterize broad trends in the demand of energy across different pathways and compartments as well as in the overall ATP:NADPH demand. These data sets include a variety of species including Arabidopsis thaliana, Nicotiana tabacum, and Camelina sativa as well as varying environmental factors including high/low light, day length, and photorespiratory levels. Examining these datasets in aggregate reveals that ultimately the bulk of the energy flux occurred in the C3 cycle and photorespiration, however, the energy demand from these pathways did not determine the ATP: NADPH demand alone. Instead, a notable contribution was revealed from starch and sucrose synthesis which might counterbalance photorespiratory demand and result in fewer adjustments in mechanisms which balance the ATP deficit.

Combining Gas Exchange and Rapid Quenching of Leaf Tissue for Mass Spectrometry Analysis Directly in Gas Exchange Cuvette.

Isotopically nonstationary metabolic flux analysis (INST-MFA) is a powerful technique for studying plant central metabolism, which involves introducing a 13CO2 tracer to plant leaves and sampling the labeled metabolic intermediates during the transient period before reaching an isotopic steady state. The metabolic intermediates involved in the C3 cycle have exceptionally fast turnover rates, with some intermediates turning over many times a second. As a result, it is necessary to rapidly introduce the label and then rapidly quench the plant tissue to determine concentrations in the light or capture the labeling kinetics of these intermediates at early labeling time points. Here, we describe a rapid quenching (0.1-0.5 s) system for 13CO2 labeling experiments in plant leaves to minimize metabolic changes during labeling and quenching experiments. This system is integrated into a commercially available gas exchange analyzer to measure initial rates of gas exchange, precisely control ambient conditions, and monitor the conversion from 12CO2 to 13CO2.

Systems biology of plant metabolic interactions.

Metabolism is the key cellular process of plant physiology. Understanding metabolism and its dynamical behavior under different conditions may help plant biotechnologists to design new cultivars with desired goals. Computational systems biochemistry and incorporation of different omics data unravelled active metabolism and its variations in plants. In this review, we mainly focus on the basics of flux balance analysis (FBA), elementary flux mode analysis (EFMA), and some advanced computational tools. We describe some important results that were obtained using these tools. Limitations and challenges are also discussed.

Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference.

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

Comparative transcriptome analysis reveals the redirection of metabolic flux from cell growth to astaxanthin biosynthesis in Yarrowia lipolytica.

Engineering Yarrowia lipolytica to produce astaxanthin provides a promising route. Here, Y. lipolytica M2 producing a titer of 181 mg/L astaxanthin was isolated by iterative atmospheric and room-temperature plasma mutagenesis and diphenylamine-mediated screening. Interestingly, a negative correlation was observed between cell biomass and astaxanthin production. To reveal the underlying mechanism, RNA-seq analysis of transcriptional changes was performed in high producer M2 and reference strain M1, and a total of 1379 differentially expressed genes were obtained. Data analysis revealed that carbon flux was elevated through lipid metabolism, acetyl-CoA and mevalonate supply, but restrained through central carbon metabolism in strain M2. Moreover, upregulation of other pathways such as ATP-binding cassette transporter and thiamine pyrophosphate possibly provided more cofactors for carotenoid hydroxylase and relieved cell membrane stress caused by astaxanthin insertion. These results suggest that balancing cell growth and astaxanthin production may be important to promote efficient biosynthesis of astaxanthin in Y. lipolytica.

Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities.

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST-MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast-growing algae, and produced a synergistic multi-organ flux map that is a first in MFA for any biological system. These insights could not be elucidated through other approaches and show the potential of INST-MFA to correct an oversimplified understanding of plant metabolism.

Modelling metabolic fluxes of tomato stems reveals that nitrogen shapes central metabolism for defence against Botrytis cinerea.

Among plant pathogens, the necrotrophic fungus Botrytis cinerea is one of the most prevalent, leading to severe crop damage. Studies related to its colonization of different plant species have reported variable host metabolic responses to infection. In tomato, high N availability leads to decreased susceptibility. Metabolic flux analysis can be used as an integrated method to better understand which metabolic adaptations lead to effective host defence and resistance. Here, we investigated the metabolic response of tomato infected by B. cinerea in symptomless stem tissues proximal to the lesions for 7 d post-inoculation, using a reconstructed metabolic model constrained by a large and consistent metabolic dataset acquired under four different N supplies. An overall comparison of 48 flux solution vectors of Botrytis- and mock-inoculated plants showed that fluxes were higher in Botrytis-inoculated plants, and the difference increased with a reduction in available N, accompanying an unexpected increase in radial growth. Despite higher fluxes, such as those involved in cell wall synthesis and other pathways, fluxes related to glycolysis, the tricarboxylic acid cycle, and amino acid and protein synthesis were limited under very low N, which might explain the enhanced susceptibility. Limiting starch synthesis and enhancing fluxes towards redox and specialized metabolism also contributed to defence independent of N supply.

Intracellular Flux Prediction of Recombinant Escherichia coli Producing Gamma-Aminobutyric Acid.

Genome-scale metabolic model (GEM) can be used to simulate cellular metabolic phenotypes under various environmental or genetic conditions. This study utilized the GEM to observe the internal metabolic fluxes of recombinant Escherichia coli producing gamma-aminobutyric acid (GABA). Recombinant E. coli was cultivated in a fermenter under three conditions: pH 7, pH 5, and additional succinic acids. External fluxes were calculated from cultivation results, and internal fluxes were calculated through flux optimization. Based on the internal flux analysis, glycolysis and pentose phosphate pathways were repressed under cultivation at pH 5, even though glutamate dehydrogenase increased GABA production. Notably, this repression was halted by adding succinic acid. Furthermore, proper sucA repression is a promising target for developing strains more capable of producing GABA.

Ex Vivo 13C-Metabolic Flux Analysis of Porcine Circulating Immune Cells Reveals Cell Type-Specific Metabolic Patterns and Sex Differences in the Pentose Phosphate Pathway.

In general, females present with stronger immune responses than males, but scarce data are available on sex-specific differences in immunometabolism. In this study, we characterized porcine peripheral blood mononuclear cell (PBMC) and granulocyte energy metabolism using a Bayesian 13C-metabolic flux analysis, which allowed precise determination of the glycolytic, pentose phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) fluxes, together with an assessment of the superoxide anion radical (O2•-) production and mitochondrial O2 consumption. A principal component analysis allowed for identifying the cell type-specific patterns of metabolic plasticity. PBMCs displayed higher TCA cycle activity, especially glutamine-derived aspartate biosynthesis, which was directly related to mitochondrial respiratory activity and inversely related to O2•- production. In contrast, the granulocytes mainly utilized glucose via glycolysis, which was coupled to oxidative PPP utilization and O2•- production rates. The granulocytes of the males had higher oxidative PPP fluxes compared to the females, while the PBMCs of the females displayed higher non-oxidative PPP fluxes compared to the males associated with the T helper cell (CD3+CD4+) subpopulation of PBMCs. The observed sex-specific differences were not directly attributable to sex steroid plasma levels, but we detected an inverse correlation between testosterone and aldosterone plasma levels and showed that aldosterone levels were related with non-oxidative PPP fluxes of both cell types.

A continuous flow bioreactor system for high-throughput hyperpolarized metabolic flux analysis.

Hyperpolarized carbon-13 labeled compounds are increasingly being used in medical MR imaging (MRI) and MR imaging (MRI) and spectroscopy (MRS) research, due to its ability to monitor tissue and cell metabolism in real-time. Although radiological biomarkers are increasingly being considered as clinical indicators, biopsies are still considered the gold standard for a large variety of indications. Bioreactor systems can play an important role in biopsy examinations because of their ability to provide a physiochemical environment that is conducive for therapeutic response monitoring ex vivo. We demonstrate here a proof-of-concept bioreactor and microcoil receive array setup that allows for ex vivo preservation and metabolic NMR spectroscopy on up to three biopsy samples simultaneously, creating an easy-to-use and robust way to simultaneously run multisample carbon-13 hyperpolarization experiments. Experiments using hyperpolarized [1-13C]pyruvate on ML-1 leukemic cells in the bioreactor setup were performed and the kinetic pyruvate-to-lactate rate constants ( k PL ) extracted. The coefficient of variation of the experimentally found k PL s for five repeated experiments was C V = 35 % . With this statistical power, treatment effects of 30%-40% change in lactate production could be easily differentiable with only a few hyperpolarization dissolutions on this setup. Furthermore, longitudinal experiments showed preservation of ML-1 cells in the bioreactor setup for at least 6 h. Rat brain tissue slices were also seen to be preserved within the bioreactor for at least 1 h. This validation serves as the basis for further optimization and upscaling of the setup, which undoubtedly has huge potential in high-throughput studies with various biomarkers and tissue types.

Flux sampling in genome-scale metabolic modeling of microbial communities.

BACKGROUND: Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates. RESULTS: In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux. CONCLUSIONS: Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.

Adjusting for false discoveries in constraint-based differential metabolic flux analysis.

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts.

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Model validation and selection in metabolic flux analysis and flux balance analysis.

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Valoración del impacto de la tasa libre de litiasis en los resultados de la evaluación metabólica / Is stone free status really necessary for metabolic evaluation following stone removal procedures?

Objetivo Evaluar si la tasa libre de litiasis afecta a los resultados del estudio metabólico en pacientes con alto riesgo de litiasis recidivante tras tratamiento completo mediante ureteroscopia. Pacientes y métodos Un total de 78 pacientes sometidos a cirugía retrógrada intrarrenal (CRIR) para el tratamiento de litiasis fueron incluidos en este estudio. Cuatro semanas después del tratamiento, los casos se dividieron en dos grupos en base a los resultados de la tomografía computarizada sin contraste (TCSC). Los casos del grupo 1 (n=54) presentaban una tasa libre de litiasis del 100% y los del grupo 2 (n=24) presentaban litiasis residuales en el riñón. Cuatro semanas después de la ureteroscopia flexible (URF) se realizó un análisis completo de orina de 24h a todos los pacientes de ambos grupos, para detectar los factores de riesgo implicados en la litogénesis. Los resultados del estudio metabólico (orina de 24h y suero) se compararon entre los dos grupos. Resultados La evaluación preoperatoria en orina y suero de los factores de riesgo asociados a la formación de cálculos no reveló diferencias estadísticas entre los dos grupos. El análisis comparativo de los factores de riesgo implicados en la formación de la litiasis mediante pruebas de orina de 24h tampoco reveló diferencias estadísticamente significativas entre los resultados preoperatorios y postoperatorios en los casos del grupo 2 con cálculos residuales. Tampoco se observaron diferencias significativas entre las medias de las variables séricas preoperatorias y postoperatorias de ambos grupos. Conclusiones Según nuestros resultados, y dada la similitud de los hallazgos obtenidos en los estudios metabólicos de los casos con y sin litiasis residual, la tasa libre de litiasis puede no constituir un factor imprescindible para la realización del estudio metabólico detallado (suero y orina de 24h) tras las intervenciones endourológicas para la extracción de los cálculos renales. (AU)

Objective To evaluate the impact of stone free status on the outcomes of metabolic evaluation in recurrent stone formers after ureteroscopic stone removal. Patients and methods A total of 78 patients undergoing retrograde intrarenal surgery (RIRS) for renal stones were included and cases were divided into two groups after 4 weeks based on the NCCT findings. While cases in the Group 1 (n=54) was completely stone free, cases in Group 2 (n=24) had residual fragments in the kidney. A full 24-hour urine analysis for relevant stone forming risk factors has been performed after 4 weeks following the fURS procedures in all patients of both groups. Outcomes of metabolic evaluation (24-hour urine and serum) have been comparatively evaluated in both groups. Results Evaluation of the preoperative serum and urine stone forming risk factors revelaed no statistical difference in both groups. Comparative evaluation of the 24-hour urinary stone forming risk factors also revealed no statistically significant difference between preoperative and postoperative findings in cases of Group 2 with residual stones. Last but not least, no significant difference was observed between the mean preoperative and postoperative serum variables between two groups. Conclusions Our results show that in the light of the similar metabolic evaluation outcomes obtained in cases with and without residual fragments, ‘stone free status’ may not be an essential factor to perform a detailed metabolic evaluation (24-hour urine analysis and serum parameters) after endourological stone removal procedures. (AU)

ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology.

BACKGROUND: Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights. RESULTS: To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering. CONCLUSION: ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .

Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments.

Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA. ONE SENTENCE SUMMARY: An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.