Expokit: a software package for computing matrix exponentials
ACM Transactions on Mathematical Software (TOMS)
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Journal of Computational Physics
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
A solver for the stochastic master equation applied to gene regulatory networks
Journal of Computational and Applied Mathematics
Analysis of stochastic reaction networks with Markov reward models
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
Product Form Approximation of Transient Probabilities in Stochastic Reaction Networks
Electronic Notes in Theoretical Computer Science (ENTCS)
Quasi product form approximation for markov models of reaction networks
Transactions on Computational Systems Biology XIV
The Propagation Approach for Computing Biochemical Reaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The chemical master equation is considered an accurate description of general chemical systems, and especially so for gene regulatory networks and protein-protein interaction networks. However, solving chemical master equations directly is considered computationally intensive. This paper discusses an efficient way of solving the chemical master equation for some prototypical problems in systems biology. Comparisons between this new approach and some traditional approaches, especially Monte-Carlo algorithms, are also presented, and show that under certain conditions the new approach performs better than Monte-Carlo algorithms.