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Reconstructing networks from (partial) incomplete data is a general problem in biology. We use an evolutionary approach in an artificial network creation and reconstruction framework to investigate limitations of gene expression network inference from simulated microarray data. For this, the simulated dynamics of the evolved networks are optimized to fit the target dynamics. Evolving networks with similar dynamics is not as difficult as comparing the resulting network topologies to the original network to be reconstructed.