Advances in Metabolic Control Analysis

Abstract

A methodology for characterizing metabolic systems using the response, control, and elasticity coefficients has emerged since the early 1970s. This methodology, termed metabolic control analysis, aims to characterize the sensitivity of metabolic responses with respect to changes in enzyme activities or parameters without the use of full mathematical models. It takes advantage of several relationships among the coefficients at the steady state. These relationships facilitate the experimental determination of the coefficients and serve as a consistency test when the coefficients are determined independently. Both the theoretical and experimental aspects of this analysis are reviewed, with an emphasis on recent developments and issues commonly discussed in the literature.

Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

“…By using these reconstructed networks, detailed analyses of specific biological functions and system properties have been performed (11,12,21,25,27,31). In addition, numerous different in silico approaches have been developed and are available to analyze the properties of metabolic networks (11,16,24,28,34,36). While the rationales underlying the various methods are becoming widely accepted, there still has been limited prospective experimental verification of genome-scale in silico models with regard to their abilities to interpret and predict complex biological processes, such as adaptive evolution.…”

Metabolic engineering and control analysis for production of aromatics: Role of transaldolase

“…Therefore, the flux control coefficient of AroG fbr in this condition is close to zero. Since the flux control coefficients of all the enzymes in the pathway should sum up to one (Heinrich and Rapoport, 1974;Kacser and Burns, 1973;Liao and Delgado, 1993), it is tempting to conclude that at low AroG fbr levels, this enzyme is the most significant flux controlling enzyme for DAHP production, and that at high AroG fbr levels Tal and TktA account for about 25% of the flux control. However, the pathway from glucose to DAHP contains several diverging branches, which tend to exhibit negative flux control with respect to DAHP production.…”

“…Therefore, increasing the level of Tal without changing the level of TktA may or may not increase the flux to DAHP. From the theory of Metabolic Control Analysis (Heinrich and Rapoport, 1974;Kacser and Burns, 1973;Liao and Delgado, 1993), however, it is very common to have several enzymes sharing the control of flux through a pathway. Furthermore, linear analysis predicts that if overexpression of either Tal or TktA has a positive effect on DAHP production, then overexpression of both Tal and TktA should have an additional positive effect, as shown in the following equation:…”

“…The terms in parentheses on the right side of the above equation are the flux control coefficients (Heinrich and Rapoport, 1974;Kacser and Burns, 1973;Liao and Delgado, 1993), and they quantify the effect of changing enzyme levels on the change in flux. The control coefficients can be estimated by changing the expression level of a single enzyme (e 1 or e 2 ).…”

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

“…In parallel, Stephanopoulos and Vallino were applying branch point analysis techniques to promote the overproduction of specific metabolites (13), which would be evolved and applied in further endeavors (14,15). Moreover, the introduction of already available mathematical frameworks, such as metabolic control analysis, into the ME arena only reinforced Bailey's views (16,17). ME was also more attractive to the industry and led to less reluctance to deal with complementary approaches, like the ones provided by fields such as systems biology, since it was thought from the beginning to embrace knowledge from multiple disciplines.…”