The kinetic data on sugarcane (Saccharum spp. hybrids) sucrose synthase (SuSy, UDP-glucose: D-fructose 2-a-Dglucosyltransferase, EC 2.4.1.13) are limited. We characterized kinetically a SuSy activity partially purified from sugarcane variety N19 leaf roll tissue. Primary plot analysis and product inhibition studies showed that a compulsory order ternary complex mechanism is followed, with UDP binding first and UDP-glucose dissociating last from the enzyme. Product inhibition studies showed that UDP-glucose is a competitive inhibitor with respect to UDP and a mixed inhibitor with respect to sucrose. Fructose is a mixed inhibitor with regard to both sucrose and UDP. Kinetic constants are as follows: K m values (mM, ± SE) were, for sucrose, 35.9 ± 2.3; for UDP, 0.00191 ± 0.00019; for UDP-glucose, 0.234 ± 0.025 and for fructose, 6.49 ± 0.61. K S i values were, for sucrose, 227 mM; for UDP, 0.086 mM; for UDP-glucose, 0.104; and for fructose, 2.23 mM.Replacing estimated kinetic parameters of SuSy in a kinetic model of sucrose accumulation with experimentally determined parameters of the partially purified isoform had significant effects on model outputs, with a 41% increase in sucrose concentration and 7.5-fold reduction in fructose the most notable. Of the metabolites included in the model, fructose concentration was most affected by changes in SuSy activity: doubling and halving of SuSy activity reduced and increased the steady-state fructose concentration by about 42 and 140%, respectively. It is concluded that different isoforms of SuSy could have significant differential effects on metabolite concentrations in vivo, therefore impacting on metabolic regulation.Keywords: metabolic control analysis; sugarcane; sucrose synthase; kinetic modelling.The kinetic parameters of enzymes provide important information about their interactions with substrates, products and effectors. Typically, substrate K m values are interpreted to give an indication of the affinity of enzymes for their substrates, and conclusions about enzymes' physiological roles are often based on these values. However, the kinetic parameters of individual enzymes do not by themselves provide much insight into the behaviour of an intact, functioning metabolic pathway. Cellular network models, such as those applied in the approach of computational systems biology, extend the usefulness of kinetic data on individual enzymes immensely and can have both explanatory and predictive value.Several papers that give an overview of different approaches for studying and modelling metabolism, such as metabolic flux analysis, metabolic control analysis (MCA) and positional isotopic labelling combined with NMR or MS, have been published recently [1][2][3]. Of these approaches, MCA [4,5] is particularly useful in studies of metabolic pathways, as it quantifies the degree of control of individual reaction steps on the steady-state pathway flux or metabolite concentrations. Hence, MCA can be a great help in determining potential target steps for metabolic engineering, beca...