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Finding Attractors in Synchronous Multiple-Valued Networks Using SAT-based Bounded Model Checking
Elena Dubrova, Ming Liu and Maxim Teslenko

Synchronous multiple-valued networks are a discrete-space discrete-time model of gene regulatory networks. Their cycle of states, called attractors, are believed to give a good indication of the possible functional modes of the system. This motivates research on algorithms for finding attractors. Existing decision diagram-based approaches have limited capacity due to the excessive memory requirements of decision diagrams. Simulation-based approaches can be applied to larger networks, however, they are incomplete. We present an algorithm which uses a SAT-based bounded model checking approach to find all attractors in a multiple-valued network. The efficiency of the presented algorithm is evaluated by analyzing 30 network models of real biological processes as well as 35.000 randomly generated 4-valued networks. The results show that our algorithm has a potential to handle an order of magnitude larger models than currently possible.

Keywords: Bounded model checking, SAT, multiple-valued network, attractor, gene regulatory network.

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