Metabolic Control Analysis (MCA) for Metabolic Engineering
Eearlier in this chapter, we discussed, how heterologous genes can be used for getting new products and how mutant genes may change the direction of metabolic flux and lead to improved productivity. However, to achieve success in metabolic engineering, one should realize that modification of genes or introduction of new genes can give altered enzymes or new enzymes, but it can not influence substrate availability or reaction kinetics, which control the behavious of well-controlled metabolic network as whole. It has also been shown that the metabolic control is often complex and non-linear in nature.

Therefore, the characteristics of a specific metabolic network need to be studied in detail for its efficient genetic manipulation, so that alteration of a single step may favourable affect the entire network. In view of this, metabolic networks have been analysed through the use of stiochiometric models (stiochiometry = rational numerical relationship between the relative quantities of substances in a reaction), which allow calculation of metabolic fluxes throughout  the network. This analysis makes use of the rates of uptake and secretion of metabolites and allows quantification of flux distributions around branch points. However, it will not tell us how fluxes are controlled. For a study of this control of fluxes, following two approaches were developed : (i) biochemical system theory (BST) and (ii) metabolic control analysis (MCA). These two approaches are similar, but MCA is preferred over BST, since it is easier to understand for non-mathematicians.

Specific enzyme inhibitors can be used to measure the fractional change as above. If the fractional change in system parameter and enzyme activity are exactly the same, the control coefficient is unity, and if there is no change at all in the system performance, despite a change at all in the system performance, despite a change in enzyme activity, the control coefficient will be zero. This is illustrated in, where effect of KCN (an enzyme inhibitor) on respiration is measured. In this example, oxygen consumption is used as the respiratory rate, and any change in this rate (measured as slope) is compared with corresponding change in the activity of the enzyme cytochrome oxidase. The value of control coefficient measured from the two fractional changes is 0.13, suggesting that the change in enzyme activity has only marginal effect on respiratory rate.