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Probabilistic Boolean networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. The intervention strategies proposed in the context of Probabilistic Boolean networks assume perfect knowledge of the transition probability matrix of the PBN. This assumption cannot be satisfied in practice due to estimation errors or mismatch between the PBN model and the actual genetic regulatory network. Thus it is important to study the effect of modeling errors on the final outcome of an intervention strategy and the goal of this paper is to do precisely that when the uncertainties are in the entries of the transition probability matrix.