Hybrid numerical solution of the chemical master equation
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
A comparative study of stochastic analysis techniques
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Communications of the ACM
Propagation models for computing biochemical reaction networks
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
Efficient calculation of rare event probabilities in Markovian queueing networks
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
The Propagation Approach for Computing Biochemical Reaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MARCIE: model checking and reachability analysis done efficiently
PETRI NETS'13 Proceedings of the 34th international conference on Application and Theory of Petri Nets and Concurrency
Exploring parameter space of stochastic biochemical systems using quantitative model checking
CAV'13 Proceedings of the 25th international conference on Computer Aided Verification
Characterizing oscillatory and noisy periodic behavior in markov population models
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
Quantitative reactive modeling and verification
Computer Science - Research and Development
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Within systems biology there is an increasing interest in the stochastic behavior of biochemical reaction networks. An appropriate stochastic description is provided by the chemical master equation, which represents a continuous-time Markov chain (CTMC). Standard Uniformization (SU) is an efficient method for the transient analysis of CTMCs. For systems with very different time scales, such as biochemical reaction networks, SU is computationally expensive. In these cases, a variant of SU, called adaptive uniformization (AU), is known to reduce the large number of iterations needed by SU. The additional difficulty of AU is that it requires the solution of a birth process. In this paper we present an on-the-fly variant of AU, where we improve the original algorithm for AU at the cost of a small approximation error. By means of several examples, we show that our approach is particularly well-suited for biochemical reaction networks.