Metabolic control analysis: a survey of its theoretical and experimental development

Fell, David

doi:10.1042/bj2860313

Cited by 734 publications

(568 citation statements)

References 141 publications

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“…These data are also in agreement with the more general finding that overexpression of an individual enzyme in any metabolic pathway is unlikely to have a significant effect on pathway flux [10,44]. This observation is predicted by Metabolic Control Analysis, which shows that only relatively small increases in flux can be achieved even by manifold overexpression of enzymes with relatively high flux-control coefficients.…”

Section: Discussionsupporting

confidence: 90%

Effects of overexpression of the liver subunit of 6-phosphofructo-1-kinase on the metabolism of a cultured mammalian cell line

Urbano

Gillham

Groner

et al. 2000

Biochemical Journal

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Overexpression of the liver subunit of 6-phosphofructo-1-kinase in Chinese hamster ovary K1 cells was shown to increase the steady-state level of the enzyme's product, fructose 1,6-bisphosphate, and to produce a small but significant decrease in the concentration of fructose 2,6-bisphosphate, which is an allosteric activator of the enzyme. However, overexpression of the enzyme had no effect on glycolytic flux under a variety of different substrate conditions. This latter observation is consistent with similar studies in fungi and in potato tubers which indicate that 6-phosphofructo-1-kinase has very little control over flux in glycolysis.

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Section: Discussionsupporting

confidence: 90%

Effects of overexpression of the liver subunit of 6-phosphofructo-1-kinase on the metabolism of a cultured mammalian cell line

Urbano

Gillham

Groner

et al. 2000

Biochemical Journal

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“…Metabolic control analysis and Biochemical Systems Theory have been great assets for the understanding of the control of intermediary metabolism, mitochondrial oxidative phosphorylation [reviews 39,62]. It has founded the notion that a total flux control of 100% tends to be distributed among the enzymes participating in the pathway.…”

Section: Discussionmentioning

confidence: 99%

Control theory of metabolic channelling

1995

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Various factors appear to control muscle energetics, often in conjunction. This calls for a quantitative approach of the type provided by Metabolic Control Analysis for intermediary metabolism and mitochondrial oxidative phosphorylation. To the extent that direct transfer of high energy phosphates and spatial organization plays a role in muscle energetics however, the standard Metabolic Control Theory does not apply, neither do its theorems regarding control. This paper develops the Control Theory that does apply to the muscle system. It shows that direct transfer of high energy phosphates bestows a system with enhanced control: the sum of the control exerted by the participating enzymes on the flux of free energy from the mitochondrial matrix to the actinomyosin may well exceed the 100% mandatory for ideal metabolic pathways. It is also shown how sequestration of high energy phosphates may allow for negative control on pathway flux. The new control theory gives methods functionally to diagnose the extent to which channelling and metabolite sequestration occur. (Mol Cell Biochem 143: 151-168, 1995)

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“…The logarithm ln p is normally distributed with the standard deviation s: if ln y(ln p) is linearised around ln p 0 , then ln y is also normal, and the standard deviation of ln y is sR, where R is the slope of the tangent. This expansion also works if p and y(p) are vectors, and the slopes of ln y i are the response coefficients defined in metabolic control analysis [17,29]. Let us briefly recapitulate a few definitions: the non-normalised response coefficients are defined as the derivatives…”

Section: Expansion Using Response Coefficientsmentioning

confidence: 99%

“…A lognormal random variable x is characterised by the mean m ln x ¼ kln xl and the variance s 2 ln x ¼ var(ln x). A list of symbols and the derivations of equations (20) - (22), (25), (29), (31), (45) and (46) can be found in the web supplement.…”

Section: Introductionmentioning

confidence: 99%

Biochemical networks with uncertain parameters

Liebermeister

Klipp

2005

IEE Proc. Syst. Biol.

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The modelling of biochemical networks becomes delicate if kinetic parameters are varying, uncertain or unknown. Facing this situation, we quantify uncertain knowledge or beliefs about parameters by probability distributions. We show how parameter distributions can be used to infer probabilistic statements about dynamic network properties, such as steady-state fluxes and concentrations, signal characteristics or control coefficients. The parameter distributions can also serve as priors in Bayesian statistical analysis. We propose a graphical scheme, the 'dependence graph', to bring out known dependencies between parameters, for instance, due to the equilibrium constants. If a parameter distribution is narrow, the resulting distribution of the variables can be computed by expanding them around a set of mean parameter values. We compute the distributions of concentrations, fluxes and probabilities for qualitative variables such as flux directions. The probabilistic framework allows the study of metabolic correlations, and it provides simple measures of variability and stochastic sensitivity. It also shows clearly how the variability of biological systems is related to the metabolic response coefficients. IntroductionCell simulations aspired to in systems biology [1] require knowledge of enzyme kinetic parameters. However, owing to a lack of measurements, measurement errors and biological variability, most of these parameters are still unknown or uncertain, which turns out to be a major obstacle in large-scale cell modelling. In this situation, a probabilistic description of the parameters can be helpful: to assess the effects of measurement errors, to find out which model results persist for a wide range of parameters, and to derive probabilities for different model outcomes. Besides this, parameter distributions can also be employed to study the natural variability and robustness of biological systems. The effects of temporal random fluctuations in gene expression [2,3] and metabolism [4,5] have been studied. Here, we focus on models with static yet uncertain parameters: the parameters are described by a probability distribution, and the standard deviation of the resulting variable distributions (their variability) reflects how strongly the variables respond to parameter variations. (In this paper, we use the term 'variable' quite generally for quantitative model results, such as concentrations or fluxes in steady state or as functions of time, or functions of them, such as signal amplitudes or durations [6].) Of course, this influence depends on the system, on the variable of interest and on the parameter: at bifurcation points, a small parameter change can even change the qualitative dynamic behaviour. In other cases, parameters can have a weak influence on the system behaviour. In fact, various biological systems seem to have evolved robustness, that is, low sensitivity and thus low variability, against a typical amount of parameter variation (see [7] and references therein) [8 -10].If the parameter v...

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Metabolic control analysis: a survey of its theoretical and experimental development

Cited by 734 publications

References 141 publications

Effects of overexpression of the liver subunit of 6-phosphofructo-1-kinase on the metabolism of a cultured mammalian cell line

Effects of overexpression of the liver subunit of 6-phosphofructo-1-kinase on the metabolism of a cultured mammalian cell line

Control theory of metabolic channelling

Biochemical networks with uncertain parameters

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