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 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.

Determination of Force Constants of Planar XY3 and Tetrahedral XY4 Molecules by the GF Matrix Method

2005 ◽  
Vol 60 (3) ◽  
pp. 183-186 ◽  
Author(s):  
Vesile Güçlü ◽  
Fatih Ucun
Keyword(s):  
Computer Program ◽  
Matrix Method ◽  
Force Constants ◽  
Internal Coordinates ◽  
The Matrix ◽  
Newton Raphson ◽  
Raphson Method

The force constants of the internal coordinates of planar XY3 and tetrahedral XY4 molecules were calculated using the GF matrix method. The matrix solutions were carried out by means of a computer program built relative to the Newton-Raphson method, and the calculations were listed in tables. For tetrahedral XY4 molecules having the same Y atom it was found that the force constants decrease with the increasing mass of the X atom, and this was attributed to the slowing of the molecule with increasing mass of the centre X atom.

scholarly journals Metabolic Control Analysis of glycolysis in tuber tissue of potato (Solanum tuberosum): explanation for the low control coefficient of phosphofructokinase over respiratory flux

1997 ◽  
Vol 322 (1) ◽  
pp. 119-127 ◽  
Author(s):  
Simon THOMAS ◽  
Peter J. F. MOONEY ◽  
Michael M. BURRELL ◽  
David A. FELL
Keyword(s):  
Potato Tuber ◽  
Metabolic Control ◽  
Kinetic Data ◽  
Metabolic Control Analysis ◽  
Glycolytic Flux ◽  
Control Analysis ◽  
Tuber Tissue ◽  
Flux Control ◽  
Calculated Concentration ◽  
Control Coefficients

We have applied Metabolic Control Analysis (MCA) in an attempt to determine the distribution of glycolytic flux control between the steps of glycolysis in aged disks of potato tuber under aerobic conditions, using concentrations of glycolytic metabolites in tuber tissue from a range of transgenic potato plants and published enzyme kinetic data. We modelled the substrate and effector kinetics of potato tuber phosphofructokinase (PFK) by reanalysing published results. Despite the scarcity of reliable kinetic data, our results are in agreement with experimental findings namely that, under the conditions described, PFK has little control over glycolytic flux. Furthermore our analysis predicts that under these conditions far more control lies in the dephosphorylation of phosphoenolpyruvate and/or in the steps beyond. We have validated the results of our analysis in two ways. First, predictions based on calculated concentration control coefficients from the analysis show generally good agreement with observed metabolite deviation indices discussed in the preceding paper [Thomas, Mooney, Burrell, and Fell (1997) Biochem. J.322, 111Ő117]. Second, sensitivity analysis of our results shows that the calculated control coefficients are robust to errors in the elasticities used in the analysis, of which relatively few need to be known accurately. Experimental and control analysis results agree with previous predictions of MCA that strong co-operative feedback inhibition of enzymes serves to move flux control downstream of the inhibiting metabolite. We conclude that MCA can successfully model the outcome of experiments in the genetic manipulation of enzyme amounts.

Use of metabolic control analysis in lactation biology

2008 ◽  
Vol 146 (3) ◽  
pp. 267-273 ◽  
Author(s):  
T. C. WRIGHT ◽  
J. P. CANT ◽  
B. W. MCBRIDE
Keyword(s):  
Sensitivity Analysis ◽  
Fatty Acid ◽  
Metabolic Control ◽  
Fatty Acid Synthesis ◽  
Acid Synthesis ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Acetyl Coa Carboxylase ◽  
Rate Limiting ◽  
Control Coefficients

SUMMARYSensitivity analysis is routinely carried out in the evaluation of simulation models to identify the degree to which parameters influence model outputs. This type of sensitivity analysis is much less frequently applied to real systems, but a technique called metabolic control analysis (MCA) was developed in the 1970s for the purpose of experimentally identifying the degree to which individual enzymes in a metabolic pathway influence flux through the pathway. MCA is applied to the results of inhibition, activation or genetic manipulation of enzymatic steps in a biochemical pathway. Flux control coefficients for each enzyme are defined as the fractional change in steady-state flux through the entire pathway for an infinitesimal change in the activity of that one enzyme. The sum of control coefficients in a linear, non-branching pathway is equal to one. It is a common finding in MCA that the control, or sensitivity, is distributed over multiple enzymes and not in a single rate-limiting enzyme. The fundamental principles of MCA are reviewed and an overview of experimental methods to measure control coefficients is provided, with the objective of introducing this approach to the fields of agricultural biochemistry and modelling, where it is little known. The application of MCA to the study of glucose metabolism and fatty acid synthesis in bovine mammary tissue are reviewed. The analyses indicated that mammary hexokinase activity exerts more control than transmembrane transport of glucose over lactose synthesis, and that control of cytosolic fatty acid synthesis is shared between acetyl-CoA carboxylase and fatty acid synthase, contrary to the widely held view that acetyl CoA carboxylase is the rate-limiting enzyme. It is suggested that MCA could be a valuable aid in the integration of proteomic and metabolomic data with metabolic flux measurements to engineer desired changes in the composition of milk from dairy animals.

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