Metabolic Turnover


Metabolic turnover is the sum of all biochemical reactions – the metabolic network – within a living cell that are involved in the conversion of substrates, particularly carbon, nitrogen and energy sources into Gibbs free energy, secreted metabolites and cell mass. Analysis of metabolic turnover involves identification of which reactions are active in a cell, and of quantifying the fluxes carried by the branches of the network and elucidating how these fluxes are controlled. Control of fluxes takes place at many levels, but the usual distinction is between global and local control.

Keywords: metabolic flux analysis; metabolic control analysis; biochemical pathways; metabolism; enzyme kinetics; enzyme regulation

Figure 1.

Different levels of regulation of metabolic turnover in the cell. The metabolic turnover is determined by the in vivo activity of the individual enzymes involved in the biochemical reactions converting nutrients (or substrates) to metabolic end products. The in vivo activities of the enzymes are determined by gene expression, mRNA stability, translation of mRNAs to proteins, protein–protein interactions, allosteric regulation of enzymes, and finally by the degree of saturation of the enzymes by the metabolites.

Figure 2.

Identification of biochemical reactions based on measurement of the labelling pattern of metabolites. After feeding with a 13C labelled carbon source, e.g. glucose labelled specifically in the C‐1 position, the individual carbon atoms within the different metabolites in the cells will attain labelling. The degree of labelling depends on the activity of the different biochemical reactions in the metabolic network as illustrated by this simple example. The enrichment of carbon atoms 1 and 2 with 13C (the so‐called summed fractional labelling) of threonine, serine and glycine has been measured using GC‐MS. The labelled carbon atoms measured by the GC‐MS method are indicated by an asterisk. If glycine was derived solely from serine, the summed fractional labelling of these two amino acids should be identical, but it is found that the carbon atoms 1 and 2 of glycine are labelled to a higher degree than the carbon atoms 1 and 2 of serine. This points to another source for glycine. Through analysis of threonine it is found that the summed fractional labelling of the carbon atoms 1 and 2 of this amino acid is higher, and through activity of threonine aldolase glycine may be derived from threonine. The labelling pattern of the metabolites thereby leads to identification of activity of threonine aldolase in the metabolic network. Furthermore, from a simple balance it is possible to calculate the relative contribution of the serine hydroxymethyltransferase and threonine aldolase to the synthesis of glycine.

Figure 3.

Calculation of metabolic fluxes based on metabolite balancing. The intracellular concentration of most metabolites is controlled at a constant level, and after a drastic perturbation in the metabolism there is a rapid adjustment of these levels. The metabolite concentrations are therefore in a pseudo steady state, which means that the material balance for the metabolite X can be specified as: v1 = v2 + v3. For a more complex metabolic network a material balance can be set up for all the metabolites, and this gives a number of constraints for the fluxes in the system. If a few fluxes are measured, e.g. the uptake of substrates and secretion of metabolites, the other fluxes may be calculated using the material balances.

Figure 4.

The pyruvate dehydrogenase bypass in S. cerevisiae. The bypass represents two reaction paths for the conversion of pyruvate to acetyl‐CoA in the mitochondria. Acetyl‐CoA in the mitochondria enters the tricarboxylic acid (TCA) cycle. Part of the acetyl‐CoA formed in the cytosol is used for lipid biosynthesis. The enzymes are (PDH); (PDC); cytosolic (ALD); (ACS); (CAT).

Figure 5.

Global control through supply and consumption of cofactors. Catabolism and the anabolism is connected through formation and consumption of ATP, NADPH and several precursor metabolites. OAA, oxaloacetate; AcCoA, acetyl‐CoA; α‐KG, alpha ketoglutarate; Suc‐CoA, succinate‐CoA.



Christensen B and Nielsen J (1999) Isotopomer analysis using GC‐MS. Metabolic Engineering 1: 282–290.

Edwards JS and Palsson BO (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proceedings of the National Academy of Sciences of the USA 97: 5528–5533.

Klein CJL, Olsson L and Nielsen J (1998) Glucose control in Saccharomyces cerevisiae: the role of MIG1 in metabolic functions. Microbiology (UK) 144: 13–24.

Nielsen J (2001) Metabolic Engineering. Applied Microbiology and Biotechnology 55: 263–283.

Schuster S, Dandekar T and Fell DA (1999) Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology 17: 53–60.

Szyperski T (1998) 13C‐NMR, MS and metabolic flux balancing in biotechnology research. Quarterly Reviews of Biophysics 31: 41–106.

Further Reading

Christensen B and Nielsen J (1999) Metabolic network analysis. Advances in Biochemical Engineering/Biotechnology 66: 209–231.

Fell DA (1997) Understanding the Control of Metabolism. London: Portland Press.

Neidhardt FC, Ingraham JL and Schaechter M (1990) Physiology of the Bacterial Cell. A Molecular Approach. Sunderland, MA: Sinnauer Associates.

Stephanopoulos G, Aristidou A and Nielsen J (1998) Metabolic Engineering. Principles and Methodologies. San Diego: Academic Press.

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Nielsen, J(Jul 2001) Metabolic Turnover. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1038/npg.els.0000635]