scholarly journals Metabolic control analysis using transient metabolite concentrations. Determination of metabolite concentration control coefficients

1992 ◽  
Vol 285 (3) ◽  
pp. 965-972 ◽  
Author(s):  
J Delgado ◽  
J C Liao
Keyword(s):  
Theoretical Basis ◽  
Metabolic Control Analysis ◽  
Metabolite Concentration ◽  
Quasi Equilibrium ◽  
Control Analysis ◽  
Algebraic Equations ◽  
Metabolite Concentrations ◽  
Flux Control Coefficients ◽  
Control Coefficients

The methodology previously developed for determining the Flux Control Coefficients [Delgado & Liao (1992) Biochem. J. 282, 919-927] is extended to the calculation of metabolite Concentration Control Coefficients. It is shown that the transient metabolite concentrations are related by a few algebraic equations, attributed to mass balance, stoichiometric constraints, quasi-equilibrium or quasi-steady states, and kinetic regulations. The coefficients in these relations can be estimated using linear regression, and can be used to calculate the Control Coefficients. The theoretical basis and two examples are discussed. Although the methodology is derived based on the linear approximation of enzyme kinetics, it yields reasonably good estimates of the Control Coefficients for systems with non-linear kinetics.

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

1993 ◽  
Vol 292 (2) ◽  
pp. 351-360 ◽  
Author(s):  
S Thomas ◽  
D A Fell
Keyword(s):  
Computer Program ◽  
Metabolic Control ◽  
Matrix Method ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Point Distribution ◽  
Algebraic Expressions ◽  
The Matrix ◽  
Control Coefficients

A computer program (MetaCon) is described for the evaluation of flux control, concentration control and branch-point distribution control coefficients of a metabolic pathway. Requiring only the reaction scheme as input, the program produces algebraic expressions for the control coefficients in terms of elasticity coefficients, metabolite concentrations and pathway fluxes. Any of these variables can be substituted by numeric or simple algebraic expressions; the expressions will then be automatically rearranged in terms of the remaining unknown variables. When all variables have been substituted, numeric values will be obtained for the control coefficients. The program is a computerized implementation of the matrix method for the determination of control coefficients. The features of MetaCon are compared with those of other programs available to workers in Metabolic Control Analysis. Potential benefits of, and methods of using, MetaCon are discussed. The mathematical background and validity of the matrix method rules are discussed, and the algorithm used by MetaCon is described. The matrix method is shown to be a specific case of a previously described general formalism for calculating control coefficients.

scholarly journals Influence of experimental errors on the determination of flux control coefficients from transient metabolite concentrations

1996 ◽  
Vol 313 (3) ◽  
pp. 721-727 ◽  
Author(s):  
Magnus EHLDE ◽  
Guido ZACCHI
Keyword(s):  
Ad Hoc ◽  
Flux Control ◽  
Mean Values ◽  
Metabolite Concentrations ◽  
Experimental Errors ◽  
Flux Control Coefficients ◽  
Standard Deviations ◽  
High Degree ◽  
Control Coefficients

The influence of experimental errors on the determination of flux control coefficients from transient metabolite concentrations with the method proposed by Delgado and Liao [(1992) Biochem. J. 282, 919–927] has been investigated by using Monte Carlo simulations. The method requires least-squares fitting of the transient metabolite concentrations. Three different fitting methods have been evaluated. Simulated metabolite concentrations of a fictive metabolic pathway were scattered randomly, emulating experimental errors, before performing the fits. This was repeated a large number of times; the mean values and standard deviations of the resulting control coefficients are reported. The results show that the proposed method for determining control coefficients is too sensitive to experimental errors to be practicable, with theoretically justified fitting methods. This is in particular due to the high degree of correlation between the concentrations. An alternative ad hoc fitting method produced biased mean values of the estimates of the control coefficients, but with remarkably low standard deviations.

scholarly journals Determination of Flux Control Coefficients from transient metabolite concentrations

1992 ◽  
Vol 282 (3) ◽  
pp. 919-927 ◽  
Author(s):  
J Delgado ◽  
J C Liao
Keyword(s):  
Metabolic Regulation ◽  
Flux Control ◽  
Initial Substrate ◽  
Metabolite Concentrations ◽  
Flux Control Coefficients ◽  
The Individual ◽  
Control Coefficients

Flux Control Coefficients have been used in the analysis of metabolic regulation for quantifying the effect of an enzyme on the overall steady-state flux. However, the experimental determination of these coefficients is very time-consuming, involving either determining the individual enzyme kinetics or perturbing the enzyme activity by genetic or other means. We developed a methodology that enables the determination of the Flux Control Coefficients from transient metabolite concentrations without knowing kinetic parameters. The transient states can be generated by changing the incubation conditions or adding the initial substrate. This approach is suitable for investigating metabolic regulation in vivo or multiple enzyme systems in vitro. It is particularly helpful if used in conjunction with n.m.r. measurements. The approach is based on a relationship between transient metabolite concentrations and the Flux Control Coefficients. The methodology has been improved from our previous results, and it is illustrated by three examples with simple pathway topologies.

Control analysis of photosynthetic sucrose synthesis: assignment of elasticity coefficients and flux-control coefficients to the cytosolic fructose 1,6-bisphosphatase and sucrose phosphate synthase

1989 ◽  
Vol 323 (1216) ◽  
pp. 327-338 ◽  
Keyword(s):  
Sucrose Phosphate Synthase ◽  
Control Analysis ◽  
Sucrose Synthesis ◽  
Flux Control ◽  
Fructose 1,6 Bisphosphatase ◽  
Flux Control Coefficients ◽  
Sucrose Phosphate ◽  
Control Coefficients ◽  
Phosphate Synthase

The use of elasticity coefficients and flux-control coefficients in a quantitative treatment of control is discussed, with photosynthetic sucrose synthesis as an example. Experimental values for elasticities for the cytosolic fructose 1,6-bisphosphatase and sucrose phosphate synthase are derived from their in vitro properties, and from an analysis of the in vivo relation between fluxes and metabolite levels. An empirical factor α , describing the response of the fructose 2,6-bisphosphate regulator cycle to fructose 6-phosphate is described, and an expression is derived relating α to the elasticities of the enzymes involved in this regulator cycle. The in vivo values for elasticities and α are then used in a modified form of the connectivity theorem to estimate the flux control coefficients of the cytosolic fructose 1,6-bisphosphatase and sucrose phosphate synthase during rapid photosynthetic sucrose synthesis.

scholarly journals Metabolic control analysis of biochemical pathways based on a thermokinetic description of reaction rates

1997 ◽  
Vol 321 (1) ◽  
pp. 133-138 ◽  
Author(s):  
Jens NIELSEN
Keyword(s):  
Metabolic Control ◽  
Reaction Rate ◽  
Reaction Rates ◽  
Rate Function ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Flux Control ◽  
Biochemical Pathways ◽  
Flux Control Coefficients

Metabolic control analysis is a powerful technique for the evaluation of flux control within biochemical pathways. Its foundation is the elasticity coefficients and the flux control coefficients (FCCs). On the basis of a thermokinetic description of reaction rates it is here shown that the elasticity coefficients can be calculated directly from the pool levels of metabolites at steady state. The only requirement is that one thermodynamic parameter be known, namely the reaction affinity at the intercept of the tangent in the inflection point of the curve of reaction rate against reaction affinity. This parameter can often be determined from experiments in vitro. The methodology is applicable only to the analysis of simple two-step pathways, but in many cases larger pathways can be lumped into two overall conversions. In cases where this cannot be done it is necessary to apply an extension of the thermokinetic description of reaction rates to include the influence of effectors. Here the reaction rate is written as a linear function of the logarithm of the metabolite concentrations. With this type of rate function it is shown that the approach of Delgado and Liao [Biochem. J. (1992) 282, 919–927] can be much more widely applied, although it was originally based on linearized kinetics. The methodology of determining elasticity coefficients directly from pool levels is illustrated with an analysis of the first two steps of the biosynthetic pathway of penicillin. The results compare well with previous findings based on a kinetic analysis.

scholarly journals Metabolic control analysis of L-tryptophan producing Escherichia coli applying targeted perturbation with shikimate

2021 ◽  
Author(s):  
Kristin Schoppel ◽  
Natalia Trachtmann ◽  
Fabian Mittermeier ◽  
Georg A. Sprenger ◽  
Dirk Weuster-Botz
Keyword(s):  
Escherichia Coli ◽  
Production Process ◽  
Metabolic Control ◽  
Target Genes ◽  
Estimation Error ◽  
Metabolic Control Analysis ◽  
Flux Analysis ◽  
Control Analysis ◽  
Intracellular Metabolite ◽  
Metabolite Concentrations

AbstractL-tryptophan production from glycerol with Escherichia coli was analysed by perturbation studies and metabolic control analysis. The insertion of a non-natural shikimate transporter into the genome of an Escherichia coli L-tryptophan production strain enabled targeted perturbation within the product pathway with shikimate during parallelised short-term perturbation experiments with cells withdrawn from a 15 L fed-batch production process. Expression of the shikimate/H+-symporter gene (shiA) from Corynebacterium glutamicum did not alter process performance within the estimation error. Metabolic analyses and subsequent extensive data evaluation were performed based on the data of the parallel analysis reactors and the production process. Extracellular rates and intracellular metabolite concentrations displayed evident deflections in cell metabolism and particularly in chorismate biosynthesis due to the perturbations with shikimate. Intracellular flux distributions were estimated using a thermodynamics-based flux analysis method, which integrates thermodynamic constraints and intracellular metabolite concentrations to restrain the solution space. Feasible flux distributions, Gibbs reaction energies and concentration ranges were computed simultaneously for the genome-wide metabolic model, with minimum bias in relation to the direction of metabolic reactions. Metabolic control analysis was applied to estimate elasticities and flux control coefficients, predicting controlling sites for L-tryptophan biosynthesis. The addition of shikimate led to enhanced deviations in chorismate biosynthesis, revealing a so far not observed control of 3-dehydroquinate synthase on L-tryptophan formation. The relative expression of the identified target genes was analysed with RT-qPCR. Transcriptome analysis revealed disparities in gene expression and the localisation of target genes to further improve the microbial L-tryptophan producer by metabolic engineering.

Quantification of the control of carbohydrate catabolism in the tapeworm Hymenolepis diminuta

1993 ◽  
Vol 71 (7-8) ◽  
pp. 315-323 ◽  
Author(s):  
Wendy Y. Precious ◽  
John Barrett
Keyword(s):  
Phosphoenolpyruvate Carboxykinase ◽  
Glycogen Synthase ◽  
Hymenolepis Diminuta ◽  
Rate Equations ◽  
Control Analysis ◽  
Control Coefficient ◽  
Flux Control ◽  
Flux Control Coefficients ◽  
Carbohydrate Catabolism ◽  
Control Coefficients

The elasticities for the different steps of carbohydrate catabolism in the tapeworm Hymenolepis diminuta were estimated from perturbation experiments. These data were then used to calculate flux and metabolite control coefficients. Enzyme elasticities were also calculated from the rate equations and an independent estimate of the flux control coefficients for phosphoenolpyruvate carboxykinase was made by inhibitor titration. The values obtained for the flux control coefficients for carbohydrate breakdown in H. diminuta are consistent with how the pathway is thought to be controlled in vivo. A sensitivity analysis of the flux control coefficients of the important regulatory enzymes in the pathway shows that for hexokinase, phosphofructokinase, pyruvate kinase, and phosphoenolpyruvate carboxykinase there are three or four key elasticities which have a significant effect on the coefficient. For glycogen synthase, the major factor in determining the magnitude of the flux control coefficient is the relative flux through the branch.Key words: Hymenolepis diminuta, metabolic control analysis, control coefficient, enzyme elasticity.

scholarly journals Metabolic control analysis. An application of signal flow graphs

1990 ◽  
Vol 269 (1) ◽  
pp. 141-147 ◽  
Author(s):  
A K Sen
Keyword(s):  
Feedback Inhibition ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Signal Flow Graph ◽  
Flow Graph ◽  
Signal Flow ◽  
Signal Flow Graphs ◽  
The Individual ◽  
Flow Graphs ◽  
Control Coefficients

In this paper the method of signal-flow graphs is used for calculating the Control Coefficients of metabolic pathways in terms of enzyme elasticities. The method is applied to an unbranched pathway (a) without feedback or feedforward regulation and (b) with feedback inhibition of the first enzyme by the last variable metabolite. It is shown that, by using a signal-flow graph, the control structure of a metabolic pathway can be represented in a graphical manner directly from the configuration of the pathway, without the necessity of writing the governing equations in a matrix form. From a signal-flow graph the various Control Coefficients can be evaluated in an easy and straightforward fashion without recourse to matrix inversion or other algebraic techniques. A signal-flow graph also provides a visual framework for analysing the cause-effect relationships of the individual enzymes.

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