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In this paper, we propose a linear control model for gene intervention in a genetic regulatory network. At each time step, finite controls are allowed to drive the network states to some target states. The objective is to achieve a target state probability distribution with a minimal control cost. The model can be formulated as a minimization problem with integer variables and continuous variables. Our experimental results show that the control model and the algorithm are efficient for gene intervention problems in genetic networks.