Systematic quantification of complex metabolic flux networks using stable isotopes and mass spectrometry

Targeting metabolic vulnerabilities to overcome resistance to therapy in acute myeloid leukemia

Malignant hematopoietic cells gain metabolic plasticity, reorganize anabolic mechanisms to improve anabolic output and prevent oxidative damage, and bypass cell cycle checkpoints, eventually outcompeting normal hematopoietic cells. Current therapeutic strategies of acute myeloid leukemia (AML) are based on prognostic stratification that includes mutation profile as the closest surrogate to disease biology. Clinical efficacy of targeted therapies, e.g., agents targeting mutant FMS-like tyrosine kinase 3 (FLT3) and isocitrate dehydrogenase 1 or 2, are mostly limited to the presence of relevant mutations. Recent studies have not only demonstrated that specific mutations in AML create metabolic vulnerabilities but also highlighted the efficacy of targeting metabolic vulnerabilities in combination with inhibitors of these mutations. Therefore, delineating the functional relationships between genetic stratification, metabolic dependencies, and response to specific inhibitors of these vulnerabilities is crucial for identifying more effective therapeutic regimens, understanding resistance mechanisms, and identifying early response markers, ultimately improving the likelihood of cure. In addition, metabolic changes occurring in the tumor microenvironment have also been reported as therapeutic targets. The metabolic profiles of leukemia stem cells (LSCs) differ, and relapsed/refractory LSCs switch to alternative metabolic pathways, fueling oxidative phosphorylation (OXPHOS), rendering them therapeutically resistant. In this review, we discuss the role of cancer metabolic pathways that contribute to the metabolic plasticity of AML and confer resistance to standard therapy; we also highlight the latest promising developments in the field in translating these important findings to the clinic and discuss the tumor microenvironment that supports metabolic plasticity and interplay with AML cells.

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.

Intracellular Mycobacterium leprae Utilizes Host Glucose as a Carbon Source in Schwann Cells

Leprosy remains a major problem in the world today, particularly affecting the poorest and most disadvantaged sections of society in the least developed countries of the world. The long-term aim of research is to develop new treatments and vaccines, and these aims are currently hampered by our inability to grow the pathogen in axenic culture. In this study, we probed the metabolism of M. leprae while it is surviving and replicating inside its primary host cell, the Schwann cell, and compared it to a related pathogen, M. tuberculosis, replicating in macrophages. Our analysis revealed that unlike M. tuberculosis, M. leprae utilized host glucose as a carbon source and that it biosynthesized its own amino acids, rather than importing them from its host cell. We demonstrated that the enzyme phosphoenolpyruvate carboxylase plays a crucial role in glucose catabolism in M. leprae. Our findings provide the first metabolic signature of M. leprae in the host Schwann cell and identify novel avenues for the development of antileprosy drugs.

New approaches are needed to control leprosy, but understanding of the biology of the causative agent Mycobacterium leprae remains rudimentary, principally because the pathogen cannot be grown in axenic culture. Here, we applied ¹³C isotopomer analysis to measure carbon metabolism of M. leprae in its primary host cell, the Schwann cell. We compared the results of this analysis with those of a related pathogen, Mycobacterium tuberculosis, growing in its primary host cell, the macrophage. Using ¹³C isotopomer analysis with glucose as the tracer, we show that whereas M. tuberculosis imports most of its amino acids directly from the host macrophage, M. leprae utilizes host glucose pools as the carbon source to biosynthesize the majority of its amino acids. Our analysis highlights the anaplerotic enzyme phosphoenolpyruvate carboxylase required for this intracellular diet of M. leprae, identifying this enzyme as a potential antileprosy drug target.

Metabolic switches from quiescence to growth in synchronized Saccharomyces cerevisiae

INTRODUCTION

The switch from quiescence (G0) into G1 and cell cycle progression critically depends on specific nutrients and metabolic capabilities. Conversely, metabolic networks are regulated by enzyme-metabolite interaction and transcriptional regulation that lead to flux modifications to support cell growth. How cells process and integrate environmental information into coordinated responses is challenging to analyse and not yet described quantitatively.

OBJECTIVES

To quantitatively monitor the central carbon metabolism during G0 exit and the first 2 h after reentering the cell cycle from synchronized Saccharomyces cerevisiae.

METHODS

Dynamic tailored ¹³C metabolic flux analysis was used to observe the intracellular metabolite flux changes, and the metabolome and proteome were observed to identify regulatory mechanisms.

RESULTS

G0 cells responded immediately to an extracellular increase of glucose. The intracellular metabolic flux changed in time and specific events were observed. High fluxes into trehalose and glycogen synthesis were observed during the G0 exit. Both fluxes then decreased, reaching a minimum at t = 65 min. Here, storage degradation contributed significantly (i.e. 21%) to the glycolytic flux. In contrast to these changes, the glucose uptake rate remained constant after the G0 exit. The flux into the oxidative pentose phosphate pathway was highest (29-fold increase, 36.4% of the glucose uptake) at t = 65 min, while it was very low at other time points. The maximum flux seems to correlate with a late G1 state preparing for the S phase transition. In the G1/S phase (t = 87 min), anaplerotic reactions such as glyoxylate shunt increased. Protein results show that during this transition, proteins belonging to clusters related with ribosome biogenesis and assembly, and initiation transcription factors clusters were continuously synthetised.

CONCLUSION

The intracellular flux distribution changes dynamically and these major rearrangements highlight the coordinate reorganization of metabolic flux to meet requirements for growth during different cell state.

Intracellular Fate of Universally Labelled 13C Isotopic Tracers of Glucose and Xylose in Central Metabolic Pathways of Xanthomonas oryzae

The goal of this study is to map the metabolic pathways of poorly understood bacterial phytopathogen, Xanthomonas oryzae (Xoo) BXO43 fed with plant mimicking media XOM2 containing glutamate, methionine and either 40% [¹³C₅] xylose or 40% [¹³C₆] glucose. The metabolic networks mapped using the KEGG mapper and the mass isotopomer fragments of proteinogenic amino acids derived from GC-MS provided insights into the activities of Xoo central metabolic pathways. The average ¹³C in histidine, aspartate and other amino acids confirmed the activities of PPP, the TCA cycle and amino acid biosynthetic routes, respectively. The similar labelling patterns of amino acids (His, Ala, Ser, Val and Gly) from glucose and xylose feeding experiments suggests that PPP would be the main metabolic route in Xoo. Owing to the lack of annotated gene phosphoglucoisomerase in BXO43, the ¹³C incorporation in alanine could not be attributed to the competing pathways and hence warrants additional positional labelling experiments. The negligible presence of ¹³C incorporation in methionine brings into question its potential role in metabolism and pathogenicity. The extent of the average ¹³C labelling in several amino acids highlighted the contribution of pre-existing pools that need to be accounted for in ¹³C-flux analysis studies. This study provided the first qualitative insights into central carbon metabolic pathway activities in Xoo.

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms

3-Amino-4-hydroxybenzoic acid production from sweet sorghum juice by recombinant Corynebacterium glutamicum

The production of the bioplastic precursor 3-amino-4-hydroxybenzoic acid (3,4-AHBA) from sweet sorghum juice, which contains amino acids and the fermentable sugars sucrose, glucose and fructose, was assessed to address the limitations of producing bio-based chemicals from renewable feedstocks. Recombinant Corynebacterium glutamicum strain KT01 expressing griH and griI derived from Streptomyces griseus produced 3,4-AHBA from the sweet sorghum juice of cultivar SIL-05 at a final concentration (1.0 g l(-1)) that was 5-fold higher than that from pure sucrose. Fractionation of sweet sorghum juice by nanofiltration (NF) membrane separation (molecular weight cut-off 150) revealed that the NF-concentrated fraction, which contained the highest concentrations of amino acids, increased 3,4-AHBA production, whereas the NF-filtrated fraction inhibited 3,4-AHBA biosynthesis. Amino acid supplementation experiments revealed that leucine specifically enhanced 3,4-AHBA production by strain KT01. Taken together, these results suggest that sweet sorghum juice is a potentially suitable feedstock for 3,4-AHBA production by recombinant C. glutamicum.

Cutting the Gordian Knot: Identifiability of anaplerotic reactions in Corynebacterium glutamicum by means of (13) C-metabolic flux analysis

Corynebacterium glutamicum is the major workhorse for the microbial production of several amino and organic acids. As long as these derive from tricarboxylic acid cycle intermediates, the activity of anaplerotic reactions is pivotal for a high biosynthetic yield. To determine single anaplerotic activities (13) C-Metabolic Flux Analysis ((13) C-MFA) has been extensively used for C. glutamicum, however with different network topologies, inconsistent or poorly determined anaplerotic reaction rates. Therefore, in this study we set out to investigate whether a focused isotopomer model of the anaplerotic node can at all admit a unique solution for all fluxes. By analyzing different scenarios of active anaplerotic reactions, we show in full generality that for C. glutamicum only certain anaplerotic deletion mutants allow to uniquely determine the anaplerotic fluxes from (13) C-isotopomer data. We stress that the result of this analysis for different assumptions on active enzymes is directly transferable to other compartment-free organisms. Our results demonstrate that there exist biologically relevant metabolic network topologies for which the flux distribution cannot be inferred by classical (13) C-MFA.

Methods and advances in metabolic flux analysis: a mini-review

Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.

OpenFLUX2: (13)C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments

BACKGROUND

Steady-state (13)C-based metabolic flux analysis ((13)C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen (13)C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the (13)C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, (13)C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based (13)C-MFA is an important factor for PLE implementation.

RESULTS

The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, in silico simulation confirmed that flux precision is improved when (13)C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model's adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method.

CONCLUSIONS

The developed open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state (13)C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.

Mathematical optimization applications in metabolic networks

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

Metabolic cartography: experimental quantification of metabolic fluxes from isotopic labelling studies

For the past decade, flux maps have provided researchers with an in-depth perspective on plant metabolism. As a rapidly developing field, significant headway has been made recently in computation, experimentation, and overall understanding of metabolic flux analysis. These advances are particularly applicable to the study of plant metabolism. New dynamic computational methods such as non-stationary metabolic flux analysis are finding their place in the toolbox of metabolic engineering, allowing more organisms to be studied and decreasing the time necessary for experimentation, thereby opening new avenues by which to explore the vast diversity of plant metabolism. Also, improved methods of metabolite detection and measurement have been developed, enabling increasingly greater resolution of flux measurements and the analysis of a greater number of the multitude of plant metabolic pathways. Methods to deconvolute organelle-specific metabolism are employed with increasing effectiveness, elucidating the compartmental specificity inherent in plant metabolism. Advances in metabolite measurements have also enabled new types of experiments, such as the calculation of metabolic fluxes based on (13)CO(2) dynamic labelling data, and will continue to direct plant metabolic engineering. Newly calculated metabolic flux maps reveal surprising and useful information about plant metabolism, guiding future genetic engineering of crops to higher yields. Due to the significant level of complexity in plants, these methods in combination with other systems biology measurements are necessary to guide plant metabolic engineering in the future.

Active and inactive metabolic pathways in tumor spheroids: determination by GC-MS

Active metabolic pathways in three-dimensional cancer-cell cultures are potential chemotherapeutic targets that would be effective throughout tumors. Chaotic vasculature creates cellular regions in tumors with distinct metabolic behavior that are only present in aggregate cell masses. To quantify cancer cell metabolism, transformed mouse fibroblasts were grown as spheroids and fed isotopically labeled culture medium. Metabolite uptake and production rates were measured as functions of time. Gas chromatography-mass spectrometry was used to quantify the extent of labeling on amino acids present in cytoplasmic extracts. The labeling pattern identified several active and inactive metabolic pathways: Glutaminolysis was found to be active, and malic enzyme and gluconeogenesis were inactive. Transformed cells in spheroids were also found to actively synthesize serine, cysteine, alanine, aspartate, glutamate, and proline; and not synthesize glutamine. The activities of these pathways suggest that cancer cells consume glutamine for biosynthesis and not to provide cellular energy. Determining active metabolic pathways indicates how cells direct carbon flow and may lead to the discovery of novel molecular targets for anticancer therapy.

Metabolic fluxes and beyond-systems biology understanding and engineering of microbial metabolism

The recent years have seen tremendous progress towards the understanding of microbial metabolism on a higher level of the entire functional system. Hereby, huge achievements including the sequencing of complete genomes and efficient post-genomic approaches provide the basis for a new, fascinating era of research-analysis of metabolic and regulatory properties on a global scale. Metabolic flux (fluxome) analysis displays the first systems oriented approach to unravel the physiology of microorganisms since it combines experimental data with metabolic network models and allows determining absolute fluxes through larger networks of central carbon metabolism. Hereby, fluxes are of central importance for systems level understanding because they fundamentally represent the cellular phenotype as integrated output of the cellular components, i.e. genes, transcripts, proteins, and metabolites. A currently emerging and promising area of research in systems biology and systems metabolic engineering is therefore the integration of fluxome data in multi-omics studies to unravel the multiple layers of control that superimpose the flux network and enable its optimal operation under different environmental conditions.

Importance of NADPH supply for improved L-valine formation in Corynebacterium glutamicum

Cofactor recycling is known to be crucial for amino acid synthesis. Hence, cofactor supply was now analyzed for L-valine to identify new targets for an improvement of production. The central carbon metabolism was analyzed by stoichiometric modeling to estimate the influence of cofactors and to quantify the theoretical yield of L-valine on glucose. Three different optimal routes for L-valine biosynthesis were identified by elementary mode (EM) analysis. The modes differed mainly in the manner of NADPH regeneration, substantiating that the cofactor supply may be crucial for efficient L-valine production. Although the isocitrate dehydrogenase as an NADPH source within the tricarboxylic acid cycle only enables an L-valine yield of Y(Val/Glc) = 0.5 mol L-valine/mol glucose (mol Val/mol Glc), the pentose phosphate pathway seems to be the most promising NADPH source. Based on the theoretical calculation of EMs, the gene encoding phosphoglucoisomerase (PGI) was deleted to achieve this EM with a theoretical yield Y(Val/Glc) = 0.86 mol Val/mol Glc during the production phase. The intracellular NADPH concentration was significantly increased in the PGI-deficient mutant. L-Valine yield increased from 0.49 +/- 0.13 to 0.67 +/- 0.03 mol Val/mol Glc, and, concomitantly, the formation of by-products such as pyruvate was reduced.

Constraint-based model of Shewanella oneidensis MR-1 metabolism: a tool for data analysis and hypothesis generation

Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level.

Genetic and functional analysis of the soluble oxaloacetate decarboxylase from Corynebacterium glutamicum

Soluble, divalent cation-dependent oxaloacetate decarboxylases (ODx) catalyze the irreversible decarboxylation of oxaloacetate to pyruvate and CO(2). Although these enzymes have been characterized in different microorganisms, the genes that encode them have not been identified, and their functions have been only poorly analyzed so far. In this study, we purified a soluble ODx from wild-type C. glutamicum about 65-fold and used matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) analysis and peptide mass fingerprinting for identification of the corresponding odx gene. Inactivation and overexpression of odx led to an absence of ODx activity and to a 30-fold increase in ODx specific activity, respectively; these findings unequivocally confirmed that this gene encodes a soluble ODx. Transcriptional analysis of odx indicated that there is a leaderless transcript that is organized in an operon together with a putative S-adenosylmethionine-dependent methyltransferase gene. Biochemical analysis of ODx revealed that the molecular mass of the native enzyme is about 62 +/- 1 kDa and that the enzyme is composed of two approximately 29-kDa homodimeric subunits and has a K(m) for oxaloacetate of 1.4 mM and a V(max) of 201 micromol of oxaloacetate converted per min per mg of protein, resulting in a k(cat) of 104 s(-1). Introduction of plasmid-borne odx into a pyruvate kinase-deficient C. glutamicum strain restored growth of this mutant on acetate, indicating that a high level of ODx activity redirects the carbon flux from oxaloacetate to pyruvate in vivo. Consistently, overexpression of the odx gene in an L-lysine-producing strain of C. glutamicum led to accumulation of less L-lysine. However, inactivation of the odx gene did not improve L-lysine production under the conditions tested.

Analysis and engineering of metabolic pathway fluxes in Corynebacterium glutamicum

The Gram-positive soil bacterium Corynebacterium glutamicum was discovered as a natural overproducer of glutamate about 50 years ago. Linked to the steadily increasing economical importance of this microorganism for production of glutamate and other amino acids, the quest for efficient production strains has been an intense area of research during the past few decades. Efficient production strains were created by applying classical mutagenesis and selection and especially metabolic engineering strategies with the advent of recombinant DNA technology. Hereby experimental and computational approaches have provided fascinating insights into the metabolism of this microorganism and directed strain engineering. Today, C. glutamicum is applied to the industrial production of more than 2 million tons of amino acids per year. The huge achievements in recent years, including the sequencing of the complete genome and efficient post genomic approaches, now provide the basis for a new, fascinating era of research - analysis of metabolic and regulatory properties of C. glutamicum on a global scale towards novel and superior bioprocesses.

Increasing the accuracy of mass isotopomer analysis through calibration curves constructed using biologically synthesized compounds

Mass isotopomer analysis is an important technique to measure the production and flow of metabolites in living cells, tissues, and organisms. This technique depends on accurate quantifications of different mass isotopomers using mass spectrometry. Constructing calibration curves using standard samples is the most universal approach to convert raw mass spectrometry measurements into quantitative distributions of mass isotopomers. Calibration curve approach has been, however, of very limited use in comprehensive analyses of biological systems, mainly suffering from the lack of extensive range of standard samples with accurately known isotopic enrichment. Here, we present a biological method capable of synthesizing specifically labeled amino acids. These amino acids have well-determined and estimable mass isotopomer distributions and thus can serve as standard samples. In this method, the bacterium strain Methylobacterium salsuginis sp. nov. was cultivated with partially 13C-labeled methanol as the only carbon source to produce 13C-enriched compounds. We show that the mass isotopomer distributions of the various biosynthesized amino acids are well determined and can be reasonably estimated based on proposed binomial approximation if the labeling state of the biomass reached an isotopic steady state. The interference of intramolecular inhomogeneity of 13C isotope abundances caused by biological isotope fractionation was eliminated by estimating average 13C isotope abundance. Further, the predictions are tested experimentally by mass spectrometry (MS) spectra of the labeled glycine, alanine, and aspartic acid. Most of the error in mass spectrometry measurements was less than 0.74 mol% in the test case, significantly reduced as compared with uncalibrated results, and this error is expected to be less than 0.4 mol% in real experiment as revealed by theoretical analysis.

Advances in analysis of microbial metabolic fluxes via (13)C isotopic labeling

Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields.

Time-series integrated "omic" analyses to elucidate short-term stress-induced responses in plant liquid cultures

The research that aims at furthering our understanding of plant primary metabolism has intensified during the last decade. The presented study validated a systems biology methodological framework for the analysis of stress-induced molecular interaction networks in the context of plant primary metabolism, as these are expressed during the first hours of the stress treatment. The framework involves the application of time-series integrated full-genome transcriptomic and polar metabolomic analyses on plant liquid cultures. The latter were selected as the model system for this type of analysis, because they provide a well-controlled growth environment, ensuring that the observed plant response is due only to the applied perturbation. An enhanced gas chromatography-mass spectrometry (GC-MS) metabolomic data correction strategy and a new algorithm for the significance analysis of time-series "omic" data are used to extract information about the plant's transcriptional and metabolic response to the applied stress from the acquired datasets; in this article, it is the first time that these are applied for the analysis of a large biological dataset from a complex eukaryotic system. The case-study involved Arabidopsis thaliana liquid cultures subjected for 30 h to elevated (1%) CO2 stress. The advantages and validity of the methodological framework are discussed in the context of the known A. thaliana or plant, in general, physiology under the particular stress. Of note, the ability of the methodology to capture dynamic aspects of the observed molecular response allowed for 9 and 24 h of treatment to be indicated as corresponding to shifts in both the transcriptional and metabolic activity; analysis of the pathways through which these activity changes are manifested provides insight to regulatory processes.

Anaerobic glyoxylate cycle activity during simultaneous utilization of glycogen and acetate in uncultured Accumulibacter enriched in enhanced biological phosphorus removal communities

Enhanced biological phosphorus removal (EBPR) communities protect waterways from nutrient pollution and enrich microorganisms capable of assimilating acetate as polyhydroxyalkanoate (PHA) under anaerobic conditions. Accumulibacter, an important uncultured polyphosphate-accumulating organism (PAO) enriched in EBPR, was investigated to determine the central metabolic pathways responsible for producing PHA. Acetate uptake and assimilation to PHA in Accumulibacter was confirmed using fluorescence in situ hybridization (FISH)-microautoradiography and post-FISH chemical staining. Assays performed with enrichments of Accumulibacter using an inhibitor of glyceraldehyde-3-phosphate dehydrogenase inferred anaerobic glycolysis activity. Significant decrease in anaerobic acetate uptake and PHA production rates were observed using inhibitors targeting enzymes within the glyoxylate cycle. Bioinformatic analysis confirmed the presence of genes unique to the glyoxylate cycle (isocitrate lyase and malate synthase) and gene expression analysis of isocitrate lyase demonstrated that the glyoxylate cycle is likely involved in PHA production. Reduced anaerobic acetate uptake and PHA production was observed after inhibition of succinate dehydrogenase and upregulation of a succinate dehydrogenase gene suggested anaerobic activity. Cytochrome b/b(6) activity inferred that succinate dehydrogenase activity in the absence of external electron acceptors may be facilitated by a novel cytochrome b/b(6) fusion protein complex that pushes electrons uphill to more electronegative electron carriers. Identification of phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase genes in Accumulibacter demonstrated the potential for interconversion of C(3) intermediates of glycolysis and C(4) intermediates of the glyoxylate cycle. Our findings along with previous hypotheses from analysis of microbiome data and metabolic models for PAOs were used to develop a model for anaerobic carbon metabolism in Accumulibacter.

Changes in enzyme activities at the pyruvate node in glutamate-overproducing Corynebacterium glutamicum

Glutamate is industrially produced by fermentation using Corynebacterium glutamicum. The key factor for efficient glutamate production by this microorganism has been considered to be a metabolic change at the 2-oxoglutarate dehydrogenase (ODH) branch point caused by a decrease in ODH activity under glutamate-overproducing conditions. However, this change would be insufficient because the ODH branch is merely the final branch in the glutamate biosynthetic pathway, and efficient glutamate production requires a balanced supply of acetyl-CoA and oxaloacetate (OAA), which are condensed to form a precursor of glutamate, namely, citrate. Therefore, there must be another (other) change(s) in metabolic flux. In this study, we demonstrated that a decrease in pyruvate dehydrogenase (PDH) activity catalyzes the conversion of pyruvate to acetyl-CoA. It is speculated that carbon flux from pyruvate to acetyl-CoA decreases under glutamate-overproducing conditions. Furthermore, an increase in pyruvate carboxylase (PC) activity, which catalyzes the reaction of pyruvate to OAA, is evident under glutamate-overproducing conditions, except under biotin-limited condition, which may lead to an increase in carbon flux from pyruvate to OAA. These data suggest that a novel metabolic change occurs at the pyruvate node, leading to a high yield of glutamate through adequate partitioning of the carbon flux.

Metabolic responses to pyruvate kinase deletion in lysine producing Corynebacterium glutamicum

Background

Pyruvate kinase is an important element in flux control of the intermediate metabolism. It catalyzes the irreversible conversion of phosphoenolpyruvate into pyruvate and is under allosteric control. In Corynebacterium glutamicum, this enzyme was regarded as promising target for improved production of lysine, one of the major amino acids in animal nutrition. In pyruvate kinase deficient strains the required equimolar ratio of the two lysine precursors oxaloacetate and pyruvate can be achieved through concerted action of the phosphotransferase system (PTS) and phosphoenolpyruvate carboxylase (PEPC), whereby a reduced amount of carbon may be lost as CO₂ due to reduced flux into the tricarboxylic acid (TCA) cycle. In previous studies, deletion of pyruvate kinase in lysine-producing C. glutamicum, however, did not yield a clear picture and the exact metabolic consequences are not fully understood.

Results

In this work, deletion of the pyk gene, encoding pyruvate kinase, was carried out in the lysine-producing strain C. glutamicum lysC^fbr, expressing a feedback resistant aspartokinase, to investigate the cellular response to deletion of this central glycolytic enzyme. Pyk deletion was achieved by allelic replacement, verified by PCR analysis and the lack of in vitro enzyme activity. The deletion mutant showed an overall growth behavior (specific growth rate, glucose uptake rate, biomass yield) which was very similar to that of the parent strain, but differed in slightly reduced lysine formation, increased formation of the overflow metabolites dihydroxyacetone and glycerol and in metabolic fluxes around the pyruvate node. The latter involved a flux shift from pyruvate carboxylase (PC) to PEPC, by which the cell maintained anaplerotic supply of the TCA cycle. This created a metabolic by-pass from PEP to pyruvate via malic enzyme demonstrating its contribution to metabolic flexibility of C. glutamicum on glucose.

Conclusion

The metabolic flux analysis performed illustrates the high flexibility of the metabolic network of C. glutamicum to compensate for external perturbation. The organism could almost maintain its growth and production performance through a local redirection of the metabolic flux, thereby fulfilling all anabolic and catabolic needs. The formation of the undesired overflow metabolites dihydroxyacetone and glycerol, in the deletion mutant, however, indicates a limiting capacity of the metabolism down-stream of their common precursor glyceraldehyde 3-phosphate and opens possibilities for further strain engineering.

Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells

Glycerol kinase has several diverse activities in mammalian cells. Glycerol kinase deficiency is a complex, single-gene, inborn error of metabolism wherein no genotype-phenotype correlation has been established. Since glycerol kinase has been suggested to exhibit additional activities than glycerol phosphorylation, expression level perturbation in this enzyme may affect cellular physiology globally. To investigate this possibility, we conducted metabolic investigations of wild-type and two glycerol kinase-overexpressing H4IIE rat hepatoma cell lines constructed in this study. The glycerol kinase-overexpressing cell lines exhibited a significantly higher consumption of carbon sources per cell, suggesting excess carbon expenditure. Furthermore, we quantified intracellular metabolic fluxes by employing stable isotope 13C labeling with a mathematically designed substrate mixture, gas chromatography-mass spectrometry, and comprehensive isotopomer balancing. This flux analysis revealed that the pentose phosphate pathway flux in the glycerol kinase-overexpressing cell lines was 2-fold higher than that in the wild-type, in addition to subtler flux changes in other pathways of carbohydrate metabolism. Furthermore, the activity and transcript level of the lipogenic enzyme glucose-6-phosphate dehydrogenase, the rate-limiting enzyme of the pentose phosphate pathway, were also about 2-fold higher than that of the wild-type; these data corroborate the flux analysis results. This study shows that glycerol kinase affects carbon metabolism globally, possibly through its additional functions, and highlights glycerol kinase's multifaceted role in cellular physiology.

Bacterial hosts for natural product production

Four bacterial hosts are reviewed in the context of either native or heterologous natural product production. E. coli, B. subtilis, pseudomonads, and Streptomyces bacterial systems are presented with each having either a long-standing or more recent application to the production of therapeutic natural compounds. The four natural product classes focused upon include the polyketides, nonribosomal peptides, terpenoids, and flavonoids. From the perspective of both innate and heterologous production potential, each bacterial host is evaluated according to biological properties that would either hinder or facilitate natural product biosynthesis.

An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis

Nonstationary metabolic flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that fluxes could be successfully estimated using only nonstationary labeling data and external flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).

Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis

Metabolic flux analysis based on stable-isotope labeling experiments and analysis of mass isotopomer distributions (MID) of cellular metabolites is a tool of great significance for metabolic engineering and study of human disease. This method relies on accurate and precise measurements of mass isotopomers by gas chromatography/mass spectrometry. To improve flux estimates, we assessed potential errors in determining MID of tert-butyldimethylsilyl-derivatized amino acids, which were attributed to (i) the choice of integration algorithm, (ii) concentration effects, and (iii) overlapping fragments. We report 29 amino acid fragments that are useful for flux analysis and another 18 fragments that should be rejected, most importantly Val-302, Leu-200, Leu-302, Ile-302, Ser-302, and Asp-316. In addition, we provide a protocol to minimize errors for determining MID to less than 0.4 mol % for accepted fragments.

GC-EI-TOF-MS analysis of in vivo carbon-partitioning into soluble metabolite pools of higher plants by monitoring isotope dilution after 13CO2 labelling

The established GC-EI-TOF-MS method for the profiling of soluble polar metabolites from plant tissue was employed for the kinetic metabolic phenotyping of higher plants. Approximately 100 typical GC-EI-MS mass fragments of trimethylsilylated and methoxyaminated metabolite derivatives were structurally interpreted for mass isotopomer analysis, thus enabling the kinetic study of identified metabolites as well as the so-called functional group monitoring of yet non-identified metabolites. The monitoring of isotope dilution after (13)CO(2) labelling was optimized using Arabidopsis thaliana Col-0 or Oryza sativa IR57111 plants, which were maximally labelled with (13)C. Carbon isotope dilution was evaluated for short (2h) and long-term (3 days) kinetic measurements of metabolite pools in root and shoots. Both approaches were shown to enable the characterization of metabolite specific partitioning processes and kinetics. Simplifying data reduction schemes comprising calculation of (13)C-enrichment from mass isotopomer distributions and of initial (13)C-dilution rates were employed. Metabolites exhibited a highly diverse range of metabolite and organ specific half-life of (13)C-label in their respective pools ((13)C-half-life). This observation implied the setting of metabolite specific periods for optimal kinetic monitoring. A current experimental design for the kinetic metabolic phenotyping of higher plants is proposed.

Compartment-specific labeling information in 13C metabolic flux analysis of plants

Metabolic engineering of plants has great potential for the low cost production of chemical feedstocks and novel compounds, but to take full advantage of this potential a better understanding of plant central carbon metabolism is needed. Flux studies define the cellular phenotype of living systems and can facilitate rational metabolic engineering. However the measurements usually made in these analyses are often not sufficient to reliably determine many fluxes that are distributed between different subcellular compartments of eukaryotic cells. We have begun to address this shortcoming by increasing the number and quality of measurements that provide (13)C labeling information from specific compartments within the plant cell. The analysis of fatty acid groups, cell wall components, protein glycans, and starch, using both gas chromatography/mass spectrometry and nuclear magnetic resonance spectroscopy are presented here. Fatty acid labeling determinations are sometimes highly convoluted. Derivatization to butyl amides reduces the errors in isotopomer resolution and quantification, resulting in better determination of fluxes into seed lipid reserves, including both plastidic and cytosolic reactions. While cell walls can account for a third or more of biomass in many seeds, no quantitative cell wall labeling measurements have been reported for plant flux analysis. Hydrolyzing cell wall and derivatizing sugars to the alditol acetates, provides novel labeling information and thereby can improve identification of flux through upper glycolytic intermediates of the cytosol. These strategies improve the quantification of key carbon fluxes in the compartmentalized flux network of plant cells.

Study on roles of anaplerotic pathways in glutamate overproduction of Corynebacterium glutamicum by metabolic flux analysis

BACKGROUND

Corynebacterium glutamicum has several anaplerotic pathways (anaplerosis), which are essential for the productions of amino acids, such as lysine and glutamate. It is still not clear how flux changes in anaplerotic pathways happen when glutamate production is induced by triggers, such as biotin depletion and the addition of the detergent material, Tween 40. In this study, we quantitatively analyzed which anaplerotic pathway flux most markedly changes the glutamate overproduction induced by Tween 40 addition.

RESULTS

We performed a metabolic flux analysis (MFA) with [1-13C]- and [U-13C]-labeled glucose in the glutamate production phase of C. glutamicum, based on the analysis of the time courses of 13C incorporation into proteinogenic amino acids by gas chromatography-mass spectrometry (GC-MS). The flux from phosphoenolpyruvate (PEP) to oxaloacetate (Oxa) catalyzed by phosphoenolpyruvate carboxylase (PEPc) was active in the growth phase not producing glutamate, whereas that from pyruvate to Oxa catalyzed by pyruvate carboxylase (Pc) was inactive. In the glutamate overproduction phase induced by the addition of the detergent material Tween 40, the reaction catalyzed by Pc also became active in addition to the reaction catalyzed by PEPc.

CONCLUSION

It was clarified by a quantitative 13C MFA that the reaction catalyzed by Pc is most markedly increased, whereas other fluxes of PEPc and PEPck remain constant in the glutamate overproduction induced by Tween 40. This result is consistent with the previous results obtained in a comparative study on the glutamate productions of genetically recombinant Pc- and PEPc-overexpressing strains. The importance of a specific reaction in an anaplerotic pathway was elucidated at a metabolic level by MFA.

The YfiD protein contributes to the pyruvate formate-lyase flux in an Escherichia coli arcA mutant strain

The product of yfiD gene is similar to pyruvate formate-lyase (PFL) activase and it has been reported to activate PFL by replacing the glycyl radical domain. To quantitate the effect of YfiD on the cell metabolism in microaerobic cultures, glucose-limited chemostat cultures were conducted with Escherichia coli yfiD mutant and yfiDarcA mutant strains. The microaerobic condition was controlled by purging the culture media with 2.5% O(2) in N(2). The intracellular metabolic flux distributions in these cultures were estimated based on C-13 labeling experiments. By comparing with the flux distributions in wild-type E. coli and the arcA mutant, it was shown that YfiD contributes to about 18% of the PFL flux in the arcA mutant, but it did not contribute to the PFL flux in wild-type E. coli. It was also shown that the cell used both PFL and pyruvate dehydrogenase (PDH) to supplement the acetyl-coenzyme A (AcCoA) pool under microaerobic conditions. The flux through PDH was about 22-30% of the total flux toward AcCoA in the wild-type, the yfiD mutant and yfiDarcA mutant strains. Relatively higher lactate production was seen in the yfiDarcA mutant than the other strains, which was due to the lower total flux through PFL and PDH toward AcCoA in this strain.

Metabolic flux elucidation for large-scale models using 13C labeled isotopes

A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraint-based modeling framework with isotopic label tracing on a large scale. When cells are fed a growth substrate with certain carbon positions labeled with (13)C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the antimalarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-(13)C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.

Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol

Metabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of Escherichia coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism.

Fluxome analysis using GC-MS

Fluxome analysis aims at the quantitative analysis of in vivo carbon fluxes in metabolic networks, i. e. intracellular activities of enzymes and pathways. It allows investigating the effects of genetic or environmental modifications and thus precisely provides a global perspective on the integrated genetic and metabolic regulation within the intact metabolic network. The experimental and computational approaches developed in this area have revealed fascinating insights into metabolic properties of various biological systems. Most of the comprehensive approaches for metabolic flux studies today involve isotopic tracer studies and GC-MS for measurement of the labeling pattern of metabolites. Initially developed and applied mainly in the field of biomedicine these GC-MS based metabolic flux approaches have been substantially extended and optimized during recent years and today display a key technology in metabolic physiology and biotechnology.

Precise metabolic flux analysis of coryneform bacteria by gas chromatography-mass spectrometry and verification by nuclear magnetic resonance

Precise metabolic flux analysis (MFA) by gas chromatography-mass spectrometry (GC-MS) and computer calculation was performed, and the consistency of the estimated results was verified by independently performed nuclear magnetic resonance (NMR) analysis. The precise estimation of flux by the integration method of the mass isotopomer signal, defined as the coefficient of variance (CV) of multiple determination, was investigated, and the results estimated using different data sets with the same magnitude of error were confirmed. The CV of multiple determinations was sufficiently small to discuss and compare the fluxes of a metabolic pathway. The estimated fluxes using the GC-MS data were cross-validated with the NMR data that were independently measured and not used for MFA. The developed method was successfully applied to the MFA of the growth phase of two different glutamate-producing coryneform bacteria, Corynebacterium glutamicum and C. efficiens. The difference in the growth rate between these two bacterial species was discussed while considering the results of MFA, including forward and backward (exchange) fluxes.

Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition

Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using 13C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from 13C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the 13C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied.