Model checking and abstraction
ACM Transactions on Programming Languages and Systems (TOPLAS)
Simplification of non-deterministic multi-valued networks
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Classification of random boolean networks
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Random Multiple-Valued Networks: Theory and Applications
ISMVL '06 Proceedings of the 36th International Symposium on Multiple-Valued Logic
Controllability and observability of Boolean control networks
Automatica (Journal of IFAC)
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Decision diagrams for the representation and analysis of logical models of genetic networks
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Finding Attractors in Synchronous Multiple-Valued Networks Using SAT-Based Bounded Model Checking
ISMVL '10 Proceedings of the 2010 40th IEEE International Symposium on Multiple-Valued Logic
A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Brief paper: Controllability of probabilistic Boolean control networks
Automatica (Journal of IFAC)
Scalar equations for synchronous Boolean networks with biological applications
IEEE Transactions on Neural Networks
Observability of Boolean Control Networks With State Time Delays
IEEE Transactions on Neural Networks
Large-Scale Signaling Network Reconstruction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In this paper, dynamics of asynchronous multiple-valued networks (AMVNs) are investigated based on linear representation. By semitensor product of matrices, we convert AMVNs into the discrete-time linear representation. A general formula to calculate all of network transition matrices of a specific AMVN is achieved. A necessary and sufficient algebraic criterion to determine whether a given state belongs to loose attractors of length $(s)$ is proposed. Formulas for the numbers of attractors in AMVNs are provided. Finally, algorithms are presented to detect all of the attractors and basins. Examples are shown to demonstrate the feasibility of the proposed scheme.