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Solving stochastic gene-expression models using queueing theory: A tutorial review
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DOI：10.1016/j.bpj.2024.04.004.png)
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Abstract
En 中文
Stochastic models of gene expression are typically formulated using the chemical master equation, which can be solved exactly or approximately using a repertoire of analytical methods. Here, we provide a tutorial review of an alternative approach based on queueing theory that has rarely been used in the literature of gene expression. We discuss the interpretation of six types of infinite-server queues from the angle of stochastic single-cell biology and provide analytical expressions for the stationary and nonstationary distributions and/or moments of mRNA/protein numbers and bounds on the Fano factor. This approach may enable the solution of complex models that have hitherto evaded analytical solution.
Keywords:
INFINITE-SERVER QUEUES
DISTRIBUTIONS
PROMOTER
NOISE
TIME
CONSEQUENCES
FLUCTUATIONS
MECHANISMS
INITIATION
DYNAMICS
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