Rule-based modeling of biochemical networks: Research Articles
Complexity - Understanding Complex Systems: Part II
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
A Bayesian Approach to Model Checking Biological Systems
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Hybrid numerical solution of the chemical master equation
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Probabilistic approximations of ODEs based bio-pathway dynamics
Theoretical Computer Science
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
The factored frontier algorithm for approximate inference in DBNs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Approximate probabilistic analysis of biopathway dynamics
Bioinformatics
Rule-based modelling of cellular signalling
CONCUR'07 Proceedings of the 18th international conference on Concurrency Theory
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The modeling and analysis of probabilistic dynamical systems is becoming a central topic in the formal methods community. Usually, Markov chains of various kinds serve as the core mathematical formalism in these studies. However, in many of these settings, the probabilistic graphical model called dynamic Bayesian networks (DBNs) [4] can be amore appropriate model to work with. This is so since a DBN is often a factored and succinct representation of an underlying Markov chain. Our goal here is to describe DBNs from this standpoint. After introducing the basic formalism, we discuss inferencing algorithms for DBNs. We then consider a simple probabilistic temporal logic and the associated model checking problem for DBNs with a finite time horizon. Finally, we describe how DBNs can be used to study the behavior of biochemical networks.