Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Efficient state classification of finite state Markov chains
DAC '98 Proceedings of the 35th annual Design Automation Conference
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
MOCHA: Modularity in Model Checking
CAV '98 Proceedings of the 10th International Conference on Computer Aided Verification
Markovian analysis of large finite state machines
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Algorithms for Inference, Analysis and Control of Boolean Networks
AB '08 Proceedings of the 3rd international conference on Algebraic Biology
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
An outlook on design technologies for future integrated systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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
Discrete causal model view of biological networks
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Petri net representation of multi-valued logical regulatory graphs
Natural Computing: an international journal
Algebraic Representation of Asynchronous Multiple-Valued Networks and Its Dynamics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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With the increasing availability of experimental data on gene-gene and protein-protein interactions, modeling of gene regulatory networks has gained a special attention lately. To have a better understanding of these networks it is necessary to capture their dynamical properties, by computing its steady states. Various methods have been proposed to compute steady states but almost all of them suffer from the state space explosion problem with the increasing size of the networks. Hence it becomes difficult to model even moderate sized networks using these techniques. In this paper, we present a new representation of gene regulatory networks, which facilitates the steady state computation of networks as large as 1200 nodes and 5000 edges. We benchmarked and validated our algorithm on the T helper model from [8] and performed in silico knock out experiments: showing both a reduction in computation time and correct steady state identification.