A computer program for the algebraic determination of control coefficients in Metabolic Control Analysis

Thomas, Simon; Fell, David

doi:10.1042/bj2920351

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…The control-and co-response coefficients for each step are simultaneously calculated from elasticity coefficients ('elasticities') [31,32]. These quantify how the rate, v, of each step responds to a change in concentration of each of its effectors, S; and so define the kinetic characteristics of each step in the system [6]:…”

Section: System Detailsmentioning

confidence: 99%

“…Flux and concentration control coefficients were calculated using the computer program MetaCon [32]. Each control coefficient is a quotient comprising a unique numerator divided by a common denominator, which is the sum of the numerators of the five flux control coefficients.…”

Section: Appendixmentioning

confidence: 99%

“…We have taken a relatively high starting value of 50 as a reasonable value for the content of muscle. Flux-and concentration-control coefficients were calculated using the computer program 'MetaCon' [32], which evaluates algebraic expressions for the control coefficients in terms of a system's variable properties (elasticities and conserved metabolites Ϫ in this case the adenine nucleotides). Co-response coefficients for the co-variation of glycolytic flux and Fru1,6P 2 were calculated by dividing the concentration control coefficients of enzyme i on Fru1,6P 2 by the corresponding flux-control flux coefficient (Eqn 1).…”

Section: System Detailsmentioning

confidence: 99%

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A control analysis exploration of the role of ATP utilisation in glycolytic‐flux control and glycolytic‐metabolite‐concentration regulation

Thomas

Fell

1998

European Journal of Biochemistry

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A theoretical metabolic-control-analysis approach has been used to study aspects of glycolytic-flux control and carbon-metabolite regulation, particularly the role of ATP demand (ATPase), in order to determine what general features of the regulation of energy metabolism would be consistent with good carbon-metabolite homeostasis in the face of large changes in carbon flux. On the basis of a semiquantitative control-analysis model, incorporating estimates of substrate, product and effector actions on the enzymes, the experimentally observed characteristics of glycolytic-flux changes prove to impose constraints on the feasible ranges of these estimates. This leads to the identification of several features of energy metabolism, each of which is necessary but not sufficient to explain the observations; although most of these have been advocated previously (such as AMP activation of phosphofructokinase (PFK), ADP inhibition of ATPase and the role of energy charge or ATP/ADP ratio), our analysis allows their relative importance to be assessed. In the model, the distribution of flux control depends primarily on ADP inhibition of ATPase, and on the activation of PFK by AMP; increase in ADP inhibition of ATPase increases the control on PFK; increase in AMP activation of PFK increases control on ATPase. PFK exerts greater flux control than does ATPase over approximately 50% of the ranges (parameter space) studied, but its control is sufficiently high to achieve sizeable flux increases over less than 20% of the space. Furthermore, control by alteration in PFK activity is shown to result in poor glycolytic metabolite homeostasis over the entire parameter space studied. However, over a large proportion of the parameter space, control by activation of ATPase can lead to large flux changes, i.e. high flux control, coupled with excellent glycolytic-metabolite homeostasis, similar to that observed in working muscle. As well as altering the relative degrees of flux control invested in PFK and ATPase, ADP inhibition of ATPase and AMP activation of PFK have pronounced effects on the homeostatic properties of the system. Stronger ADP inhibition of ATPase results in improved homeostasis of glycolytic metabolites, ATP and ADP in response to PFK activation, whereas stronger activation of PFK by AMP improves the homeostasis of these three quantities in response to ATPase activation. The results are further evidence of the potential for physiological ATP demand to exert control over glycolytic flux, but additionally show that the known effector interactions, in addition to their previously known role in ATP regulation, could contribute to the remarkable homeostasis of glycolytic-metabolite levels observed in vivo. They further indicate that quantitative characterisation of likely domains of behaviour of metabolic systems can be achieved by an algebraic analysis that is not highly dependent on a full and precise knowledge of the molecular details of the kinetic/regulatory properties of the enzymes, but that still allows an assessment of w...

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Section: System Detailsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

Section: System Detailsmentioning

confidence: 99%

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A control analysis exploration of the role of ATP utilisation in glycolytic‐flux control and glycolytic‐metabolite‐concentration regulation

Thomas

Fell

1998

European Journal of Biochemistry

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“…The columns of K span the null-space. Computation of this matrix can be performed by several biochemical simulation programs such as METACON [37], Jarnac (http://www. sys-bio.org) or METATOOL [24] (available from http:// www.bioinf.mdc-berlin.de/metabolic or ftp://mudshark.…”

Section: Exploring the Network Structure Of Metabolismmentioning

confidence: 99%

Use of network analysis of metabolic systems in bioengineering

Schuster¹,

Klamt²,

Weckwerth³

et al. 2002

Bioprocess and Biosystems Engineering

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Basic ideas and recent developments in network analysis of metabolic systems and various applications of this analysis in bioengineering are reviewed. Central concepts are the null-space to the stoichiometry matrix and the elementary flux modes. The applicability of elementary-modes analysis in biotechnology is illustrated by the synthesis of the cyclooctadepsipeptides PF1022 in the fungus Mycelia sterilia. Network analysis is also useful in metabolic flux analysis. In particular, a procedure for finding out which reaction rates can be uniquely calculated in underdetermined reaction networks is outlined. The concept of 'enzyme subsets' is explained and its use for analysing genetic regulation is demonstrated. In particular, the correlation between expression data concerning the diauxic shift in yeast and the enzyme subsets in yeast metabolism is discussed.

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“…Clarke, 1980Clarke, , 1988Thomas and Fell, 1993), Since in steady states, all intermediates have to be balanced with respect to inputs and outputs, the vector, V, of stationary reaction rates (called fluxes below) has to be situated in the null-space (kernel) of the stoichiometry matrix, N (cf.…”

Section: Introductionmentioning

confidence: 99%

Biomedical and Life Physics

Ghista¹

1996

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A computer program for the algebraic determination of control coefficients in Metabolic Control Analysis

Cited by 13 publications

References 25 publications

A control analysis exploration of the role of ATP utilisation in glycolytic‐flux control and glycolytic‐metabolite‐concentration regulation

A control analysis exploration of the role of ATP utilisation in glycolytic‐flux control and glycolytic‐metabolite‐concentration regulation

Use of network analysis of metabolic systems in bioengineering

Biomedical and Life Physics

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