Abstract
The stochastic nature of chemical reactions has resulted in an increasing research interest in discrete-state stochastic models and their analysis. A widely used approach is the description of the temporal evolution of such systems in terms of a chemical master equation (CME). In this paper we study two approaches for approximating the underlying probability distributions of the CME. The first approach is based on an integration of the statistical moments and the reconstruction of the distribution based on the maximum entropy principle. The second approach relies on an analytical approximation of the probability distribution of the CME using the system size expansion, considering higher order terms than the linear noise approximation. We consider gene expression networks with unimodal and multimodal protein distributions to compare the accuracy of the two approaches. We find that both methods provide accurate approximations to the distributions of the CME while having different benefits and limitations in applications.
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Notes
- 1.
Noncentral moments can be easily obtained from central ones. For instance, the second noncentral moment \(\mu ^{(2)}\) is obtained from the variance \(\sigma ^2\) and the mean \(\mu \) as \(\mu ^{(2)} = \sigma ^2 + \mu ^2\).
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PT acknowledges support from the Royal Commission for the Exhibition of 1851 in form of a Research Fellowship.
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Andreychenko, A., Bortolussi, L., Grima, R., Thomas, P., Wolf, V. (2017). Distribution Approximations for the Chemical Master Equation: Comparison of the Method of Moments and the System Size Expansion. In: Graw, F., Matthäus, F., Pahle, J. (eds) Modeling Cellular Systems. Contributions in Mathematical and Computational Sciences, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-45833-5_2
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