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An important preliminary goal in learning biological network models from experimental data is to study the plausibility of different types of regulatory mechanisms in living organisms. In addition to providing important biological insight, the knowledge of abundance of some specific regulatory rules in nature helps the computational problems by restricting the space of possible models to be learned. In this paper, we assess the plausibility of canalizing functions and certain post (closed) function classes by examining the feasibility of generating networks containing one class at a time that satisfies some predefined network constraints. The results suggest that these two families of regulatory rules are plausible in the light of currently available experimental data.