Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Metabolic analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed2000

Determination of full 13C isotopomer distributions for metabolic flux analysis using heteronuclear spin echo difference NMR spectroscopy.

13C-isotopomer labeling experiments play an increasingly important role in the analysis of intracellular metabolic fluxes for genetic engineering purposes. 13C NMR spectroscopy is a key technique in the experimental determination of isotopomer distributions. However, only subsets of isotopomers can be quantitated using this technique due to redundancies in the scalar coupling patterns and due to invisibility of the 12C isotope in NMR. Therefore, we developed and describe in this paper a 1H NMR spectroscopy method that allows to determine the complete isotopomer distribution in metabolites having a backbone consisting of up to at least four carbons. The proposed pulse sequences employ up to three alternately applied frequency-selective inversion pulses in the 13C channel. In a first application study, the complete isotopomer distribution of aspartate isolated from [1-13C]ethanol-grown Ashbya gossypii was determined. A tentative model of the central metabolism of this organism was constructed and used for metabolic flux analysis. The aspartate isotopomer NMR data played a key role in the successful determination of the flux through the peroxisomal glyoxylate pathway. The new NMR method can be highly instrumental in generating the data upon which isotopomer labeling experiments for flux analysis, that are becoming increasingly important, are based.

Ascomycota↗
PubMed2000

Metabolic control analysis: biological applications and insights.

Metabolic control analysis provides a robust mathematical and theoretical framework for describing metabolic and signaling pathways and networks, and for quantifying the controls over these processes. Its application has already shed light on some of the principles underlying the regulation of metabolic pathways, and it is well suited to the analysis of the types of data emerging from genomic studies.

Animals↗
PubMed2004

Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks.

Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure.

Biomass↗
PubMed2000

A metabolic control analysis of kinetic controls in ATP free energy metabolism in contracting skeletal muscle.

A system analysis of ATP free energy metabolism in skeletal muscle was made using the principles of metabolic control theory. We developed a network model of ATP free energy metabolism in muscle consisting of actomyosin ATPase, sarcoplasmic reticulum (SR) Ca(2+)-ATPase, and mitochondria. These components were sufficient to capture the major aspects of the regulation of the cytosolic ATP-to-ADP concentration ratio (ATP/ADP) in muscle contraction and had inherent homeostatic properties regulating this free energy potential. As input for the analysis, we used ATP metabolic flux and the cytosolic ATP/ADP at steady state at six contraction frequencies between 0 and 2 Hz measured in human forearm flexor muscle by (31)P-NMR spectroscopy. We used the mathematical formalism of metabolic control theory to analyze the distribution of fractional kinetic control of ATPase flux and the ATP/ADP in the network at steady state among the components over this experimental range and an extrapolated range of stimulation frequencies (up to 10 Hz). The control analysis showed that the contractile actomyosin ATPase has dominant kinetic control of ATP flux in forearm flexor muscle over the 0- to 1.6-Hz range of contraction frequencies that resulted in steady states, as determined by (31)P-NMR. However, flux control begins to shift toward mitochondria at >1 Hz. This inversion of flux control from ATP demand to ATP supply control hierarchy progressed as the contraction frequency increased past 2 Hz and was nearly complete at 10 Hz. The functional significance of this result is that, at steady state, ATP free energy consumption cannot outstrip the ATP free energy supply. Therefore, this reduced, three-component muscle ATPase system is inherently homeostatic.

Adenosine Diphosphate↗
PubMed1993

A second-order approach to metabolic control analysis.

In the mathematical formalism of metabolic control analysis the changes in metabolite concentrations and fluxes have been related to the underlying parameter perturbations by a linear approximation, thus restricting the analysis to very small parameter perturbations. Obviously, the response of the system variables to larger parameter perturbations can be described more accurately if, in addition to the linear terms, the second-order terms are considered. The basic equations of this approach are derived. The second-order effects can be expressed (i) by the second-order elasticity coefficients of the reaction rates with respect to the kinetic parameters and/or metabolite concentrations, and (ii) by the control coefficients and pi-elasticities of the linear theory. Parameter-independent second-order control coefficients can be defined if the reaction rates depend linearly on the perturbation parameters. These coefficients satisfy summation theorems similar to those of the linear theory. The formalism is applied to an unbranched chain of monomolecular reactions and to a skeleton model of glycolysis. The second-order approximation turns out to be more accurate for a relatively wide range of rate perturbations. However, the use of the second- and higher-order expansions for tackling practical problems seems limited since the required higher-order elasticity coefficients may be hard, if not impossible, to obtain experimentally. These difficulties, as well as the general limitations of the local approach of metabolic control analysis, may be compensated for by a combination with kinetic modelling.

Animals↗
PubMed1998

13C tracer experiments and metabolite balancing for metabolic flux analysis: comparing two approaches

Conventional metabolic flux analysis uses the information gained from determination of measurable fluxes and a steady-state assumption for intracellular metabolites to calculate the metabolic fluxes in a given metabolic network. The determination of intracellular fluxes depends heavily on the correctness of the assumed stoichiometry including the presence of all reactions with a noticeable impact on the model metabolite balances. Determination of fluxes in complex metabolic networks often requires the inclusion of NADH and NADPH balances, which are subject to controversial debate. Transhydrogenation reactions that transfer reduction equivalents from NADH to NADPH or vice versa can usually not be included in the stoichiometric model, because they result in singularities in the stoichiometric matrix. However, it is the NADPH balance that, to a large extent, determines the calculated flux through the pentose phosphate pathway. Hence, wrong assumptions on the presence or activity of transhydrogenation reactions will result in wrong estimations of the intracellular flux distribution. Using 13C tracer experiments and NMR analysis, flux analysis can be performed on the basis of only well established stoichiometric equations and measurements of the labeling state of intracellular metabolites. Neither NADH/NADPH balancing nor assumptions on energy yields need to be included to determine the intracellular fluxes. Because metabolite balancing methods and the use of 13C labeling measurements are two different approaches to the determination of intracellular fluxes, both methods can be used to verify each other or to discuss the origin and significance of deviations in the results. Flux analysis based entirely on metabolite balancing and flux analysis, including labeling information, have been performed independently for a wild-type strain of Aspergillus oryzae producing alpha-amylase. Two different nitrogen sources, NH4+ and NO3-, have been used to investigate the influence of the NADPH requirements on the intracellular flux distribution. The two different approaches to the calculation of fluxes are compared and deviations in the results are discussed. Copyright 1998 John Wiley & Sons, Inc.

Journal Article↗
PubMed2003

A metabolic network analysis & NMR experiment design tool with user interface-driven model construction for depth-first search analysis.

A Windows program for metabolic engineering analysis and experimental design has been developed. A graphical user interface enables the pictorial, "on-screen" construction of a metabolic network. Once a model is composed, balance equations are automatically generated. Model construction, modification and information exchange between different users is thus considerably simplified. For a given model, the program can then be used to predict all the extreme point flux distributions that optimize an objective function while satisfying balances and constraints by using a depth-first search strategy. One can also find the minimum reaction set that satisfies different conditions. Based on the identified flux distributions or linear combinations, the user can simulate the NMR and GC/MS spectra of selected signal molecules. Alternately, spectra vectorization allows for the automated optimization of labeling experiments that are intended to distinguish between different, yet plausible flux extreme point distributions. The example provided entails predicting the flux distributions associated with deleting pyruvate kinase and designing 13C NMR experiments that can maximally discriminate between the flux distributions.

Computer Simulation↗
PubMed1996

Co-response analysis: a new experimental strategy for metabolic control analysis.

The formulation of the standard summation and connectivity relationships as a statement that the matrix of all the elasticities in a system is the inverse of the matrix of all the control coefficients is completely general, provided that only control coefficients for independent fluxes and concentrations are considered, and that the elasticity matrix is written to take account of the stoichiometry of the pathway and the implied dependences between concentrations. This generally implies that co-response analysis is also general, i.e. that all of the elasticities and all of the control coefficients in any system, regardless of branching, feedback effects, moiety conservation or other complications, can be determined by comparing the effects of perturbations of the enzyme activities on the steady-state fluxes and concentrations of the pathway. The approach requires no quantitative information about the magnitudes of the effects on the individual enzyme activities, and consequently no enzymes need to be studied in isolation from the pathway.

Animals↗
PubMed1987

Diacylglycerol metabolism in mast cells. Analysis of lipid metabolic pathways using molecular species analysis of intermediates.

These studies assess the metabolic source and fate of cellular 1,2-diacylglycerol (DAG), an intermediate that increases with physiologic stimulation, participates in the regulation of protein phosphorylation, and acts as a substrate for arachidonic acid release. The quantitation of the molecular species of DAG and one of its metabolic products, phosphatidic acid (PA), was assessed in the purified rat mast cell, a model system with marked quantitative constraints but with rapid and extensive secretion after receptor stimulation. Cellular DAG was extracted, partially purified, radioactively phosphorylated to form [32P]PA, and, after conversion to its dimethyl phosphoric acid ester, molecular species separations were undertaken using reversed phase HPLC and/or argentation TLC. Quantitation of 0.5 pmol of a single molecular species of cellular DAG was achieved, but HPLC was not alone sufficient to resolve all molecular species of interest. More importantly, comparison of mast cell DAG with [32P]PA generated in 32Pi-prelabeled cells revealed that the sub-classes that contained arachidonic acid species represent only 11% of the total DAG, while that of [32P]PA was 41% in resting cells. [32P]PA and, to a variable extent, DAG showed preferential increases in arachidonate-containing subclasses after stimulation (to 50.9 and 13.9%, respectively). These data suggest that a large portion of the increased mass of DAG seen during stimulation was probably not derived by phosphoinositide hydrolysis. This type of molecular species analysis of intermediates of important phospholipid metabolic pathways should help to establish the metabolic origin and fate of these and other compounds.

Animals↗
PubMed2003

Metabolic flux analysis as a tool for the elucidation of the metabolism of neurotransmitter glutamate.

A flux analysis model for the metabolism of neurotransmitter glutamate is constructed, in order to study functional aspects of its metabolism. This work is based on the potassium [K(+)] evoked neurotransmitter glutamate released, as measured in a series of experiments of superfused rat or mouse brain preparations. These measurements are combined with data reported, concerning the metabolism of glutamate and its precursors, glutamine and glucose in rat cerebral cells in vivo. The proposed stoichiometry of the specific reaction network renders the model solvable. The classification procedure establishes that the measured fluxes are all balanceable and all non-measured fluxes can be calculated. The system is well posed with a condition number of 7.8536. The results emphasize the importance of phosphate activated glutaminase and aspartate aminotransferase in the metabolism of neurotransmitter glutamate. Reported data on the rate of the malate-aspartate shuttle, as well as the anaplerotic flux of the glial pyruvate carboxylase reaction are in agreement with the estimations calculated from the proposed model.

Animals↗
PubMed1999

Metabolic flux analysis: a powerful tool for monitoring tissue function.

In recent years, metabolic flux analysis has been widely used in bioprocess engineering to monitor cell viability and improve strain activity. Metabolic flux analysis refers to a methodology for investigating cellular metabolism whereby intracellular fluxes are calculated using a stoichiometric model for the major intracellular reactions and applying mass balances around intracellular metabolites. A powerful feature of this methodology is its ability to consider cellular biochemistry in terms of reaction networks. By considering the stoichiometry of biochemical reactions, it is possible to estimate the degree of engagement of each pathway participating in overall cellular activity, and hence obtain a comprehensive view of a cell s metabolic state. Given the potential impact of cellular energy metabolism on the function of engineered tissues, such comprehensive analysis of metabolic activity can be an extremely useful tool for tissue engineers. Estimates of intracellular fluxes under various environmental conditions could be used to optimize function in vivo as well as culture conditions in vitro. In this review, we provide a brief theoretical background of metabolic flux analysis and summarize the most widely used experimental approaches to obtain flux data. This review is intended as an overview of the field and as a starting point for tissue engineers wishing to learn about and eventually employ this methodology.

Animals↗
PubMed1990

Metabolic control analysis by computer: progress and prospects.

In metabolic control analysis, the set of rules called the matrix method simplifies writing the matrix equation that relates the control coefficients to elasticities of enzymes, and a subset of the velocities and substrate concentrations in a metabolic system. However, since the process of writing and solving these equations can be clearly defined, a computer program is being produced that will write the equations for the control coefficients given only the reactions of the metabolic system. In this way, the results of metabolic control analysis can be made available to all those who can use them, even if they do not know the theory in detail. The computational strategies underlying such a program, and the similarities to a biochemical simulation program developed previously are described.

Algorithms↗
PubMed2001

Automated metabolic gas analysis systems: a review.

The use of automated metabolic gas analysis systems or metabolic measurement carts (MMC) in exercise studies is common throughout the industrialised world. They have become essential tools for diagnosing many hospital patients, especially those with cardiorespiratory disease. Moreover, the measurement of maximal oxygen uptake (VO2max) is routine for many athletes in fitness laboratories and has become a defacto standard in spite of its limitations. The development of metabolic carts has also facilitated the noninvasive determination of the lactate threshold and cardiac output, respiratory gas exchange kinetics, as well as studies of outdoor activities via small portable systems that often use telemetry. Although the fundamental principles behind the measurement of oxygen uptake (VO2) and carbon dioxide production (VCO2) have not changed, the techniques used have, and indeed, some have almost turned through a full circle. Early scientists often employed a manual Douglas bag method together with separate chemical analyses, but the need for faster and more efficient techniques fuelled the development of semi- and full-automated systems by private and commercial institutions. Yet, recently some scientists are returning back to the traditional Douglas bag or Tissot-spirometer methods, or are using less complex automated systems to not only save capital costs, but also to have greater control over the measurement process. Over the last 40 years, a considerable number of automated systems have been developed, with over a dozen commercial manufacturers producing in excess of 20 different automated systems. The validity and reliability of all these different systems is not well known, with relatively few independent studies having been published in this area. For comparative studies to be possible and to facilitate greater consistency of measurements in test-retest or longitudinal studies of individuals, further knowledge about the performance characteristics of these systems is needed. Such information, along with the costs and the common features associated with these systems, may aid physicians and scientists to select a system that is best suited to their requirements and may also improve the quality of these frequently-reported physiological measures.

Automation↗
PubMed1996

Regulation and control in complex, dynamic metabolic systems: experimental application of the top-down approaches of metabolic control analysis to fatty acid oxidation and ketogenesis.

Metabolic control analysis has provided experimental tools and a precise language to understand and to describe regulation and control quantitatively in complex, dynamic metabolic systems. The top-down approaches of control analysis reduce and simplify the experiments required to analyse: (1) the potential (group control coefficients) of system parameters (blocks of reactions) to control system variables (fluxes, metabolite concentrations); (2) the sensitivity (group elasticity coefficients) of those blocks of reactions to the action of direct and/or indirect effectors and (3) the regulation of system variables, fluxes through blocks of reactions (partial response coefficients) or through the entire pathway (group response coefficients), by internal or external effectors. Although metabolic control analysis expresses regulation and control in terms of response or control coefficients, which are mathematically derived and have numerical values, the two terms are mathematically indistinguishable. In this paper, we define regulation and control as a consequence of hierarchy and not of mathematical function and analyse a conceptually simplified experimental system to demonstrate our distinction.

Animals↗
PubMed2005

Genetically constrained metabolic flux analysis.

Significant progress has been made in using existing metabolic databases to estimate metabolic fluxes. Traditional metabolic flux analysis generally starts with a predetermined metabolic network. This approach has been employed successfully to analyze the behaviors of recombinant strains by manually adding or removing the corresponding pathway(s) in the metabolic map. The current work focuses on the development of a new framework that utilizes genomic and metabolic databases, including available genetic/regulatory network structures and gene chip expression data, to constrain metabolic flux analysis. The genetic network consisting of the sensing/regulatory circuits will activate or deactivate a specific set of genes in response to external stimulus. The activation and/or repression of this set of genes will result in different gene expression levels that will in turn change the structure of the metabolic map. Hence, the metabolic map will automatically "adapt" to the external stimulus as captured by the genetic network. This adaptation selects a subnetwork from the pool of feasible reactions and so performs what we term "environmentally driven dimensional reduction." The Escherichia coli oxygen and redox sensing/regulatory system, which controls the metabolic patterns connected to glycolysis and the TCA cycle, was used as a model system to illustrate the proposed approach.

Citric Acid Cycle↗
PubMed2006

NMR-based metabolic profiling of human hepatoma cells in relation to cell growth by culture media analysis.

Metabolic profiling is a metabolomic approach that allows the characterization of metabolic phenotypes under specific set of conditions. In the present paper we investigated the metabolism of sparse and high density cultures in relation to different cell growth phases. Changes in the metabolome were evaluated by using 1H-NMR spectroscopy, correlation map and Multivariate Data Analysis on the net balances of metabolites in the medium. This approach allowed us to identify two different metabolic profiles in relation to the cell growth phases in subconfluence and confluence cultures. The results have been interpreted on the basis of patterns of correlations obtained in the two physiological cell states. Cells almost arrested in G0/G1 phase by contact dependent growth inhibition underwent changes in the channeling of amino acids utilization from synthetic to energetic purpose and in anaplerosis/cataplerosis regulation of the TCA cycle.

Carcinoma, Hepatocellular↗
PubMed2002

Metabolic control analysis in drug discovery and disease.

Metabolic control analysis (MCA) provides a quantitative description of substrate flux in response to changes in system parameters of complex enzyme systems. Medical applications of the approach include the following: understanding the threshold effect in the manifestation of metabolic diseases; investigating the gene dose effect of aneuploidy in inducing phenotypic transformation in cancer; correlating the contributions of individual genes and phenotypic characteristics in metabolic disease (e.g., diabetes); identifying candidate enzymes in pathways suitable as targets for cancer therapy; and elucidating the function of "silent" genes by identifying metabolic features shared with genes of known pathways. MCA complements current studies of genomics and proteomics, providing a link between biochemistry and functional genomics that relates the expression of genes and gene products to cellular biochemical and physiological events. Thus, it is an important tool for the study of genotype-phenotype correlations. It allows genes to be ranked according to their importance in controlling and regulating cellular metabolic networks. We can expect that MCA will have an increasing impact on the choice of targets for intervention in drug discovery.

Aneuploidy↗
PubMed1999

Use and limitations of modular metabolic control analysis in medicine and biotechnology.

By Metabolic Control Analysis (MCA), it has been shown that control on flux is in most cases shared by several enzymes rather than concentrated on one "rate-limiting step." This analysis also allows the quantification of the control exerted by groups (modules) of enzymes. The modules may correspond to spatial compartments or to functional units. A brief outline of the modular approach to MCA is given. The criteria by which the system can be modularized and the concept of monofunctional unit are explained. Various studies in which control analysis was applied to biotechnological and medical issues are reviewed. In particular, MCA has turned out to be helpful in the assessment of the severity of enzyme deficiencies. Another application is the search for target enzymes or enzyme groups where pharmaceuticals can suppress the metabolism of pathogenic microorganisms most. In biotechnology, modular and "traditional" control analyses are valuable tools for choosing the most promising targets for genetic manipulation so as to increase a biosynthetic flux. As control coefficients are linear approximations, the effect of enhancing the activities of enzymes to a larger extent is often overestimated. Further limitations such as the restriction to stationary states, uncertainties due to spatial heterogeneities and the impact of experimental error are discussed.

Animals↗