scholarly journals Integration of temporal analysis and control analysis of metabolic systems

1990 ◽  
Vol 269 (1) ◽  
pp. 255-259 ◽  
Author(s):  
J S Easterby
Keyword(s):  
Steady State ◽  
Transient Response ◽  
Temporal Analysis ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Control Coefficient ◽  
Temporal Control ◽  
Transient Time ◽  
Metabolic Systems ◽  
Control Coefficients

A theory is developed that integrates approaches to the analysis of pathway transient response and metabolic control analysis. A Temporal Control Coefficient is defined that is a measure of the system's transient response to modulation of enzyme activity or concentration. The approach allows for the analysis of the establishment of a steady state from rest, of the system's ‘agility’ of response to minor perturbations of a pre-existing steady state and of the macroscopic transition between steady states. In the last-mentioned case it is shown that, like the transient time itself, the control of transient response retains the property of independence from the mechanism of the transition. In consequence, the Temporal Control Coefficient can be defined in terms of the control properties of the initial and final states alone without reference to the mechanism of transition. A summation property is shown to apply to the Temporal Control Coefficients in each case. Connectivity relationships between elasticities and Temporal Control Coefficients are also established.

Regulation analysis of energy metabolism.

1997 ◽  
Vol 200 (2) ◽  
pp. 193-202 ◽  
Author(s):  
M D Brand
Keyword(s):  
Steady State ◽  
Energy Metabolism ◽  
Metabolic Control Analysis ◽  
Steady States ◽  
Control Analysis ◽  
Top Down ◽  
Down Regulation ◽  
Metabolic Systems ◽  
And Control ◽  
Control Coefficients

This paper reviews top-down regulation analysis, a part of metabolic control analysis, and shows how it can be used to analyse steady states, regulation and homeostasis in complex systems such as energy metabolism in mitochondria, cells and tissues. A steady state is maintained by the variables in a system; regulation is the way the steady state is changed by external effectors. We can exploit the properties of the steady state to measure the kinetic responses (elasticities) of reactions to the concentrations of intermediates and effectors. We can reduce the complexity of the system under investigation by grouping reactions into large blocks connected by a small number of explicit intermediates-this is the top-down approach to control analysis. Simple titrations then yield all the values of elasticities and control coefficients within the system. We can use these values to quantify the relative strengths of different internal pathways that act to keep an intermediate or a rate constant in the steady state. We can also use them to quantify the relative strengths of different primary actions of an external effector and the different internal pathways that transmit its effects through the system, to describe regulation and homeostasis. This top-down regulation analysis has been used to analyse steady states of energy metabolism in mitochondria, cells and tissues, and to analyse regulation of energy metabolism by cadmium, an external effector, in mitochondria. The combination of relatively simple experiments and new theoretical structures for presenting and interpreting the results means that top-down regulation analysis provides a novel and effective way to analyse steady states, regulation and homeostasis in intricate metabolic systems.

scholarly journals Control analysis of transition times in metabolic systems

1990 ◽  
Vol 265 (1) ◽  
pp. 195-202 ◽  
Author(s):  
E Meléndez-Hevia ◽  
N V Torres ◽  
J Sicilia ◽  
H Kacser
Keyword(s):  
Steady State ◽  
Temporal Characteristic ◽  
Negative Control ◽  
Control Analysis ◽  
Metabolic System ◽  
Steady State Flux ◽  
Metabolic Systems ◽  
Summation Theorem ◽  
Transition Times ◽  
Control Coefficients

The transition time, tau, of a metabolic system is defined as the ratio of the metabolite concentrations in the system, sigma, to the steady-state flux, J. Its value reflects a temporal characteristic of the system as it relaxes towards the steady state. Like other systemic properties, the value of tau will be a function of the enzyme activities in the system. The influence of a particular enzyme activity on tau can be quantified by a Control Coefficient, C tau ei. We show that it is possible to derive a Summation Theorem sigma ni = 1 C tau ei = -1 and a Connectivity Theorem sigma ni = 1 C tau ei.epsilon viSk = -Sk/sigma. We establish a ‘sign rule’ that predicts the order of positive and negative Control Coefficients in a sequence.

scholarly journals Tissue variation in the control of oxidative phosphorylation: implication for mitochondrial diseases

2000 ◽  
Vol 347 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Rodrigue ROSSIGNOL ◽  
Thierry LETELLIER ◽  
Monique MALGAT ◽  
Christophe ROCHER ◽  
Jean-Pierre MAZAT
Keyword(s):  
Oxidative Phosphorylation ◽  
Mitochondrial Diseases ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Control Coefficient ◽  
Experimental Conditions ◽  
Mitochondrial Cytopathies ◽  
Coefficient Distribution ◽  
Control Coefficients ◽  
Different Tissues

Metabolic control analysis has often been used for quantitative studies of the regulation of mitochondrial oxidative phosphorylations (OXPHOS). The main contribution of this work has been to show that the control of mitochondrial metabolic fluxes can be shared among several steps of the oxidative phosphorylation process, and that this distribution can vary according to the steady state and the tissue. However, these studies do not show whether this observed variation in the OXPHOS control is due to the experimental conditions or to the nature of the mitochondria. To find out if there actually exists a tissue variation in the distribution of OXPHOS control coefficients, we determined the control coefficients of seven OXPHOS complexes on the oxygen-consumption flux in rat mitochondria isolated from five different tissues under identical experimental conditions. Thus in this work, only the nature of the mitochondria can be responsible for any variation detected in the control coefficient values between different tissues. The analysis of control coefficient distribution shows two tissue groups: (i) the muscle and the heart, controlled essentially at the level of the respiratory chain; and (ii) the liver, the kidney and the brain, controlled mainly at the phosphorylation level by ATP synthase and the phosphate carrier. We propose that this variation in control coefficient according to the tissue origin of the mitochondria can explain part of the tissue specificity observed in mitochondrial cytopathies.

scholarly journals Metabolic Control Analysis of Exponential Growth and Product Formation

2018 ◽  
Author(s):  
David Andrew Fell
Keyword(s):  
Steady State ◽  
Metabolic Control ◽  
Product Yield ◽  
Product Formation ◽  
Metabolic Control Analysis ◽  
Control Analysis ◽  
Control Coefficient ◽  
Flux Control ◽  
Flux Control Coefficient ◽  
Metabolic Fluxes

Metabolic Control Analysis defines the relationships between the change in activity of an enzyme and the resulting impacts on metabolic fluxes and metabolite concentrations at steady state. In many biotechnological applications of metabolic engineering, however, the goal is to alter the product yield. In this case, although metabolism may be at a pseudo-steady state, the amount of biomass catalysing the metabolism can be growing exponentially. Here, expressions are derived that relate the change in activity of an enzyme and its flux control coefficient to the change in yield from an exponentially growing system. Conversely, the expressions allow estimation of an enzyme's flux control coefficient over the pathway generating the product from measurements of the changes in enzyme activity and yield.

scholarly journals Control analysis applied to the whole body: control by body organs over plasma concentrations and organ fluxes of substances in the blood

1994 ◽  
Vol 297 (1) ◽  
pp. 115-122 ◽  
Author(s):  
G C Brown
Keyword(s):  
Steady State ◽  
Plasma Concentrations ◽  
Metabolic Control Analysis ◽  
The Body ◽  
Whole Body ◽  
Control Analysis ◽  
Physiological Control ◽  
Using Data ◽  
Control Coefficients

Metabolic control analysis is adapted as a method for describing and analysing the control by organs in the body over the fluxes and concentrations of substances carried in the blood. This physiological control analysis can most usefully be applied to substances with fluxes into and out of organs that are uniquely dependent only on their plasma concentrations. The organ flux of a substance is defined as the steady-state net flux of a substance into a particular organ. The organ flux control coefficients quantify the extent to which a particular organ controls the flux of a substance into the same or another particular organ. Organ concentration control coefficients quantify the extent to which an organ controls the steady-state concentration of a substance in the blood. The control coefficients are additive and obey summation, connectivity and branching theorems. Thus the control coefficients can be determined experimentally by measuring the sensitivities (elasticities) of organ fluxes to the plasma concentration of the substance. As an example of the application of these concepts, the control of ketone-body metabolism in vivo is analysed using data from the literature.

scholarly journals From genomes to systems: the path with yeast

2006 ◽  
Vol 361 (1467) ◽  
pp. 477-482 ◽  
Author(s):  
Stephen G Oliver
Keyword(s):  
Eukaryotic Cell ◽  
Metabolic Control Analysis ◽  
Coarse Grained ◽  
Control Analysis ◽  
Individual Gene ◽  
Metabolic Systems ◽  
Genes Encoding ◽  
Balance Analysis ◽  
Definition Of ◽  
The Impact

Metabolic Control Analysis (MCA) is a conceptual and mathematical formalism that models the relative contributions of individual effectors in a pathway to both the flux through the pathway and the concentrations of individual intermediates within it. To exploit MCA in an initial Systems Biology analysis of the eukaryotic cell, two categories of experiments are required. In category 1 experiments, flux is changed and the impact on the levels of the direct and indirect products of gene action is measured. We have measured the impact of changing the flux on the transcriptome, proteome and metabolome of Saccharomyces cerevisiae . In this whole-cell analysis, flux equates to growth rate. In category 2 experiments, the levels of individual gene products are altered, and the impact on the flux is measured. We have used competition analyses between the complete set of heterozygous yeast deletion mutants to reveal genes encoding proteins with high flux control coefficients. These genes may be exploited, in a top-down analysis, to build a coarse-grained model of the eukaryotic cell, as exemplified by yeast. More detailed modelling requires that ‘natural’ biological systems be identified. The combination of flux balance analysis with both genetics and metabolomics in the definition of metabolic systems is discussed.

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.

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