Fast adaptive uniformization of the chemical master equation. Didier, F., Henzinger, T., Mateescu, M., & Wolf, V. In HiBi09 - 2009 International Workshop on High Performance Computational Systems Biology, 2009. doi abstract bibtex 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. © 2009 IEEE.
@inproceedings{
title = {Fast adaptive uniformization of the chemical master equation},
type = {inproceedings},
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abstract = {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. © 2009 IEEE.},
bibtype = {inproceedings},
author = {Didier, F. and Henzinger, T.A. and Mateescu, M. and Wolf, V.},
doi = {10.1109/HiBi.2009.23},
booktitle = {HiBi09 - 2009 International Workshop on High Performance Computational Systems Biology}
}
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