Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Method of conditional moments (MCM) for the chemical master equation: A unified framework for the method of moments and hybrid stochastic-deterministic models.
J. Math. Biol. 69, 687-735 (2014)
DOI Verlagsversion bestellen
The time-evolution of continuous-time discrete-state biochemical processes is governed by the Chemical Master Equation (CME), which describes the probability of the molecular counts of each chemical species. As the corresponding number of discrete states is, for most processes, large, a direct numerical simulation of the CME is in general infeasible. In this paper we introduce the method of conditional moments (MCM), a novel approximation method for the solution of the CME. The MCM employs a discrete stochastic description for low-copy number species and a moment-based description for medium/high-copy number species. The moments of the medium/high-copy number species are conditioned on the state of the low abundance species, which allows us to capture complex correlation structures arising, e.g., for multi-attractor and oscillatory systems. We prove that the MCM provides a generalization of previous approximations of the CME based on hybrid modeling and moment-based methods. Furthermore, it improves upon these existing methods, as we illustrate using a model for the dynamics of stochastic single-gene expression. This application example shows that due to the more general structure, the MCM allows for the approximation of multi-modal distributions.
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Continuous-time Discrete-state Markov Process ; Chemical Master Equation ; Method Of Moments ; Hybrid Stochastic-determinstic Models ; Differential Algebraic Equations ; Gene Expression; Differential-algebraic Systems; Stochastic Gene-expression; Uniformization; Distributions; Dynamics; Models; Noise
ISSN (print) / ISBN 0303-6812
Zeitschrift Journal of Mathematical Biology
Quellenangaben Band: 69, Heft: 3, Seiten: 687-735
Institut(e) Institute of Computational Biology (ICB)