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The ability to measure the transcriptional response of cells has drawn much attention to the underlying transcriptional networks. To untangle the network, numerous models with corresponding reverse engineering methods have been applied. In this work, we propose a non-linear model with adjustable degrees of complexity. The corresponding reverse engineering method uses a probabilistic scheme to reduce the reconstruction problem to subnetworks. Adequate models for gene regulatory networks must be anchored on sufficient biological knowledge. Here, the cI auto-inhibition circuit (cI circuit) is used to validate our reverse engineering method. Simulations of the cI circuit are used for the reconstruction, whereas a simplified cI circuit model assists the modeling phase. Several levels of complexity are evaluated, subsequently the reconstructed models show different properties. As a result, we reconstruct an abstract model, capturing the dynamic behavior of the cI circuit to a high degree.