Metabolic control network

Fig. 5-32 Metabolic control network for isoleucine synthesis by Rh. spheroides.

Intensified metabolic control, especially in case of diabetes, demands minimal-invasive or non-invasive methods of analytical measurement. For this goal, a method has been developed to measure the blood glucose content in vivo, in direct contact with the skin, by means of diffuse reflection near infrared (NIR) spectroscopy on the basis of multivariate calibration and neural networks (Muller et al. [1997] Fischbacher et al. [1997] Danzer et al. [1998]). Because no patients with any standard blood glucose value are available in principle, a method of indirect calibration has... 

After measuring the fluxes through the metabolic network, it is necessary to determine the extent to which each pathway or enzyme controls the net fluxes. Metabolic control analysis (MCA) is a technique used to elucidate how flux control is distributed in a metabolic network, thereby providing the information for identification of potential targets for metabolic engineering [8],... 

An early systematic approach to metabolism, developed in the late 1970s by Kacser and Burns [313], and Heinrich and Rapoport [314], is Metabolic Control Analysis (MCA). Anticipating systems biology, MCA is a quantitative framework to understand the systemic steady-state properties of a biochemical reaction network in terms of the properties of its component reactions. As emphasized by Kacser and Burns in their original work [313],... 

The utility and success of Metabolic Control Analysis is mostly due to a number of simple relationships that interconnect the various coefficients and that bridge between local and global properties of the network. First, the summation theorems relate to the structural properties of the network and are independent of kinetic parameters [96]. Using Eq. (90) and (91), it is straightforward to verify that... 

Although the importance of a systemic perspective on metabolism has only recently attained widespread attention, a formal frameworks for systemic analysis has already been developed since the late 1960s. Biochemical Systems Theory (BST), put forward by Savageau and others [142, 144 147], seeks to provide a unified framework for the analysis of cellular reaction networks. Predating Metabolic Control Analysis, BST emphasizes three main aspects in the analysis of metabolism [319] (i) the importance of the interconnections, rather than the components, for cellular function (ii) the nonlinearity of biochemical rate equations (iii) the need for a unified mathematical treatment. Similar to MCA, the achievements associated with BST would warrant a more elaborate treatment, here we will focus on BST solely as a tool for the approximation and numerical simulation of complex biochemical reaction networks. 

A. Goldbeter, D. Gonze, G. Houart, J. C. Leloup, J. Halloy, and G. Dupont, From simple to complex oscillatory behavior in metabolic and genetic control networks. Chaos 11, 247-260 (2001). 

Metabolic control analysis (MCA) is the application of steady-state enzyme networks to the problem of the control of metabolic flux (Fell, 1992 Kacser and Burns, 1995). Consider a pathway ... 

Bottom-up systems biology does not rely that heavily on Omics. It predates top-down systems biology and it developed out of the endeavors associated with the construction of the first mathematical models of metabolism in the 1960s [10, 11], the development of enzyme kinetics [12-15], metabolic control analysis [16, 17], biochemical systems theory [18], nonequilibrium thermodynamics [6, 19, 20], and the pioneering work on emergent aspects of networks by researchers such as Jacob, Monod, and Koshland [21-23]. 

Many methods have been developed for model analysis for instance, bifurcation and stability analysis [88, 89], parameter sensitivity analysis [90], metabolic control analysis [16, 17, 91] and biochemical systems analysis [18]. One highly important method for model analysis and especially for large models, such as many silicon cell models, is model reduction. Model reduction has a long history in the analysis of biochemical reaction networks and in the analysis of nonlinear dynamics (slow and fast manifolds) [92-104]. In all cases, the aim of model reduction is to derive a simplified model from a larger ancestral model that satisfies a number of criteria. In the following sections we describe a relatively new form of model reduction for biochemical reaction networks, such as metabolic, signaling, or genetic networks. 

As briefly outlined in Section 6.3, one of the theoretical frameworks in quantitative analysis of metabolic networks is metabolic control analysis. In metabolic control analysis, the enzyme elasticity coefficients provide empirical constraints between the metabolites concentrations and the reaction fluxes. These constraints can be considered in concert with the interdependencies in the J and c spaces that are imposed by the network stoichiometry. If the coefficients elk = (c / Ji)dJi/dck are known, then these values bind the fluxes and concentrations to a hyperplane in the (J, c) space. 

Ingalls, B.P. and Sauro, H.M. (2003) Sensitivity analysis of stoichiometric networks an extension of metabolic control analysis to non-steady state trajectories. J. Theor. Biol. 222, 23-36. 

It is evident that the complex network of metabolic reactions must be rigorously regulated. At the same time, metabolic control must be flexible, to adjust metabolic activity to the constantly changing external environments of cells. Metabolism is regulated through control of (1) the amounts oj enzymes, (2) their catalytic activities, and (3) the accessibility of substrates. 

Aside from the inordinately dominant light of molecular genetics, the new wave in biochemistry today is, what has come to be called, metabolic control analysis (MCA) (Comish-Bowden and Cardenas, 1990). The impetus behind this wave is the desire to achieve a holistic view of the control of metabolic systems, with emphasis on the notion of system. The classical, singular focus on individual, feedback-modulated (e.g., allosteric), rate-limiting enzymes entails a naive and myopic view of metabolic regulation. It has become increasingly evident that control of metabolic pathways is distributive, rather than localized to one reaction. MCA places a given enzyme reaction into the kinetic context of the network of substrate-product connections, effector relationships, etc., as supposedly exist in situ, it shows that control of fluxes, metabolite concentrations, inter alia, is a systemic function and not an inherent property of individual enzymes. Such... 

The methods of FBA and elementary flux modes study interactions between different routes in a metabolic network and the quantification of flux distributions but do not evaluate how fluxes are controlled. In Metabolic Control Analysis (MCA), the control exerted by the rate of a reaction over a substrate flux or any other system parameter (e.g., metabolite concentration or cell proliferation) can be described quantitatively as a control coefficient. The control coefficient is a relative measure of how much a perturbation affects a system variable and is defined as the fractional change in the system property over the fractional change in the reaction rate [e.g., Bums et al. 1985],... 

Mees, A.I. P.E. Rapp. 1978. Periodic metabolic systems Oscillations in multiple-loop negative feedback biochemical control networks. J. Math. Biol. 5 99-114. 

All major network sciences seem to concur on the view that real networks are not random but are based on robust and strong organizational principles. As explained above, major metabohc network analysis techniques could be strongly influenced by the network reductionist approach, where the behavior of the network could be potentially predicted by its elementary constituents and their interactions alone. Therefore, the hierarchical and scale-free property of gene networks can effectively complement techniques like metabolic control analysis and become a vital tool in future gene network analysis (Almaas and Barabasi, 2006). 

Zak et al. have argued that inferring the GRN structure from expression data alone is impossible. However, promising results come from more recent work showing that properly designed perturbation experiments do permit network reconstruction (see Stark et al., Husmeier, de La Fuente et al., Kholodenko et al., and Gardner et al. ). Two papers " extended ideas from metabolic control analysis to suggest perturbation experiments... 

Reder C (1988) Metabolic control theory a structural approach. J Theor Biol 135 175-201 Reed JL, Palsson B0 (2004) Genome-scale in silico models of E. coli have multiple equivalent phenotypic states assessment of correlated reaction subsets that comprise network states. Genome Res 14 1797-1805... 

Molecular Biology Tools Metabolic Network Analysis Metabolic Control Analysis Tools from Functional Genomics Applications of Metabolic Engineering Future Directions... 

For quantification of flux control, the concept of metabolic control analysis (MCA) is useful. In MCA flux control is quantified in terms of the so-called flux control coefficients (FCCs). The FCCs quantify the relative increase in a given flux Jj within the network upon an increase in a given enzyme activity (Ei), and they are mathematically defined as... 

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