Simple distillation columns with ideal vapor-liquid equilibrium may display mul-

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New high-throughput experimental technologies, i.e. for monitoring the expression levels of large gene sets and the concentrations of metabolites, are evolving rapidly. These data sets contain the information required to uncover the organization of biological systems on a genetic, proteomic, and metabolic level. However, in order to realize the translation of data into a system level understanding of cell functions, methods that can construct quantitative mathematical models from data are needed. In particular, determination of the quantitative interactions between the components within and across these levels is an important issue. These interactions lead to the notion of networks that can be represented by weighted, New technologies enable acquisition of large data-sets containing genomic, proteomic and metabolic information that describe the state of a cell. These data-sets call for systematic methods enabling relevant information about the inner workings of the cell to be extracted. One important issue at hand is the understanding of the functional interactions between genes, proteins and metabolites. We here present a method for identifying the dynamic interactions between biochemical components within the cell, in the vicinity of a steady-state. Key features of the proposed method are that it can deal with data obtained under perturbations of any system parameter, not only concentrations of specific components, and that the direct effect of the perturbations does not need to be known. This is important as concentration perturbations are often difficult to perform in biochemical systems and the specific effects of general type perturbations are usually highly uncertain, or unknown. The basis of the method is a linear leastsquares estimation, using time-series measurements of concentrations and expression profiles, in which system states and parameter perturbations are estimated simultaneously. An important side-effect of also employing estimation of the parameter perturbations is that knowledge of the system's steady-state concentrations, or activities, is not required and that deviations from steady-state prior to the perturbation can be dealt with. Time derivatives are computed using a zero-order hold discretization, shown to yield significant improvements over the widely used Euler approximation. We also show how network interactions with dynamics that are too fast to be captured within the available sampling time can be determined and excluded from the network identification. Known and unknown moiety conservation relationships can be processed in the same manner. The method requires that the number of samples equals at least the number of network components and, hence, is at present restricted to relatively smallscale networks. We demonstrate herein the performance of the method on two small-scale in silico genetic networks.