Logic programming for Boolean networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Finding a Periodic Attractor of a Boolean Network
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Oscillating behavior of logic programs
Correct Reasoning
Learning from interpretation transition
Machine Learning
Algebraic Representation of Asynchronous Multiple-Valued Networks and Its Dynamics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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This paper addresses the problem of finding attractors in synchronous Boolean networks. The existing Boolean decision diagram-based algorithms have limited capacity due to the excessive memory requirements of decision diagrams. The simulation-based algorithms can be applied to larger networks, however, they are incomplete. We present an algorithm, which uses a SAT-based bounded model checking to find all attractors in a Boolean network. The efficiency of the presented algorithm is evaluated by analyzing seven networks models of real biological processes, as well as 150,000 randomly generated Boolean networks of sizes between 100 and 7,000. The results show that our approach has a potential to handle an order of magnitude larger models than currently possible.