Abstract A new methodology is presented for the analysis of complex metabolic networks with the goal of overproduction of metabolites. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and also can be amplified without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. Guidelines are also provided for the accurate definition of reaction groups and their fluxes, which are critical for the accurate determination of the group control coefficients. The only measurements required are those of metabolic fluxes observable through the measurement of secreted metabolites. Through successive reaction groupings and calculation of the corresponding group control coefficients, the kinetic control of a metabolic network can be localized to the reaction step(s) comprising a single intermetabolite linkage in the metabolic network. The concepts of this method are illustrated through the simulation of a case study involving the aromatic amino-acid biosynthetic pathway. It is further demonstrated that the optimal strategy for the effective increase of network fluxes is through the coordinated amplification of a small number of steps in order to maintain maximum throughput while ensuring an uninterrupted supply of intermediate metabolites. This result is obtained from the solution of a constrained linear optimization problem that optimally balances the maximum impact of each reaction on the network flux with the need to maintain all intracellular metabolites within a reasonable range around the normal steady state of the network. The mathematical expression of this requirement invokes the concept of the concentration control coefficient, which emerges as a parameter of critical importance in the identification of feasible enzymatic modifications having maximal impact on the network flux.