Metabolic flux

Metabolic pathways (or fluxes) are controlled by the interactions of metabolites with one or more regulatory steps of the pathway. A conve- 

In terms of metabolic control shorthand, this communication within the regulatory sequence may be written as follows 

One regulatory sequence is that of transmission, another is the feedback sequence, Ej Ei, by which a change in the rate of utilization of glucose increases the rate of 

Although the pathway discussed above is linear (i.e., it is unbranched) the conclusions apply to any system. For branched systems each branch can be represented by a flux which is generated at the flux-generating step for the overall system. For example, in the following simple branched system, 

In these quasi-Iinear fluxes the rate of the step before the branch is not equal to the rate of the actual chemical reaction, since it can be considered to be composed of two separate fluxes designated Ek and Ex(, ) for /, and 7b. respectively. (This is one reason for describing such systems as fluxes rather than pathways a pathway would imply a series of whole reactions.) Feedback control is an inherent part of these branch fluxes for example, an increased activity of E2 increases 7, by lowering the concentration of B and thus deflecting flux from /i, to 7 in this way an increased E2 is effectively increasing the part-reaction Ei(,) by decreasing the concentration of B. However, it must be stressed that this is not the same mechanism as feedback in a linear pathway, since B has no direct effect on reaction in this particular branched system. The possible metabolic importance of this indirect feedback produced by branching is discussed in Section V. 

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As mentioned in the Introduction, it is necessary but not sufficient for isotopically discriminating reactions to occur if isofractionation is to be observed in any particular body component. Whether the isofractionation incurred in a particular reaction in the pathway leads to a measurable effect in a body pool component depends on the pattern or topology of the metabolic fluxes. Three basic cases are considered here for illustrative purposes. 

Transgenic E. coli accumulate comparatively low levels of carotenoids " compared to microbial algae, yeasts, and bacteria. Many efforts ° have focused on increasing accumulation by manipulation of factors affecting metabolic flux and metabolite accnmnlation (listed and discnssed in Sections 5.3.1.1 and 5.3.1.3 A) and have been reviewed." - " In bacterial systems, approaches to control can be categorized as either infrastructural (plasmids, enzymes, strains) or ultrastructural (media and feeding, enviromnent, precursor pools, substrate flux). 

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SmoUce, C.D., Martin, V.J.J., and Keasling, J.D., Controlling the metabolic flux through the carotenoid pathway using directed mRNA processing and stabilization, Metabol. Eng. 3, 313, 2001. 

Metabolic Flux Analysis and Metabolic Control Analysis... 

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Signal transduction now ensures that information is selectively addressed to single cell types, where it is registered and transformed in a common manner - e. g., in the case of a human being which is composed of some trillions of cells [1]. Hereby metabolic fluxes or cell division are controlled efficiently. 

However, FBA in itself is not sufficient to uniquely determine intracellular fluxes. In addition to the ambiguities with respect to the choice of the objective function, flux balance analysis is not able to deal with the following rather common scenarios [248] (i) Parallel metabolic routes cannot be resovled. For example, in the simplest case of two enzymes mediating the same reaction, the optimization procedure can only assign the sum of a flux of both routes, but not the flux of each route, (ii) Reversible reaction steps can not be resolved, only the sum of both directions, that is, the net flux, (iii) Cyclic fluxes cannot be resolved as they have no impact on the overall network flux, (iv) Futile cycles, which are common in many organisms, are not present in the FBA solution, because they are usually not optimal with respect to any optimization criterion. These shortcomings necessitate a direct experimental approach to metabolic fluxes, as detailed in the next section. 

Given the inherent limits of a purely computational approach to obtain an estimate of the flux distribution of a metabolic system, an experimental determination of metabolic fluxes is paramount to the construction and validation... 

Figure 14. Principle for measuring bidirectional fluxes by 13C metabolic flux analysis. In a carbon labeling experiment, 1 13C glucose is provided in the medium, and the culture is grown until a steady state is reached. Glucose can either go directly via the hexose phosphate pool (Glu 6P and Fru 6P) into starch, resulting in labeling hexose units of starch only at the Cj position, or it can be cleaved to triose phosphates (DHAP and GAP), from which hexose phosphates can be resynthesized, which will result in 50% labeling at both the Ci and the C6 position (assuming equilibration of label by scrambling at the level of triose phosphates). From the label in the hexose units of starch, the steady state fluxes at the hexose phosphate branchpoint can be calculated for example, if we observe 75% label at the Ci and 25% at the C6 position, the ratio of vs to V7 must have been 1 to 1. All other fluxes can be derived if two of the fluxes of Vi, V6, and V7 are known (e.g., V2 vi V3 V5 + v6).

The first example of a dynamic flux analysis was a study performed in the 1960s [269]. In the yeast Candida utilis, the authors determined metabolic fluxes via the amino acid synthesis network by applying a pulse with 15N-labeled ammonia and chasing the label with unlabeled ammonia. Differential equations were then used to calculate the isotope abundance of intermediates in these pathways, with unknown rate values fitted to experimental data. In this way, the authors could show that only glutamic acid and glutamine-amide receive their nitrogen atoms directly from ammonia, to then pass it on to the other amino acids. 

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