We present algorithms for finding control strategies in Boolean Networks (BN). Our approach uses symbolic techniques from the field of model checking. We show that despite recent hardnessresults for finding control policies, a model checking-based approach is often capable of scaling to extremely large and complex models. We demonstrate the effectiveness of our approach by applying it to a BN model of embryogenesis in D. melanogaster with 15,360 Boolean variables.

16 pages

*Department of Computer Science and Department of Biological Sciences, Carnegie Mellon University

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