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It is currently attracting the interest of theoretical biologists, biochemicists and experimentalists to attempt to deduce the structure of biochemical networks "ab initio" from routinely available experimental data. The recent advances in systems biology have been driven by the methods that generate in vivo time-course data characterizing biochemical network interactions. Such data can be used for inferring a model structure and its parameters in order to examine the dynamic behavior of biological processes on a systemic level. We present here a new correlation-based approach to network inference, whose most attractive feature is that information can be extracted from the observed data with little a priori knowledge of the underlying mechanisms. Our method introduces a new correlation metric based on a Voronoi tessellation of the variable space and infers correlations among stationary time series data of reactant concentrations. These correlations can be used to reveal dependencies between variables, as well as connectivity between species. The method has been applied to a real case study: the binding kinetics of the enzyme inhibitor kappa B kinase to its substrate inhibitor kappa B alpha, whose interaction is an integral part of the transduction of signals in the NF-kappa B signalling pathway.