Solving stochastic gene expression models using queueing theory: a tutorial review
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913319261044736 |
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| author | Szavits-Nossan, Juraj Grima, Ramon |
| author_facet | Szavits-Nossan, Juraj Grima, Ramon |
| contents | 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 non-stationary distributions and/or moments of mRNA/protein numbers, and bounds on the Fano factor. This approach may enable the solution of complex models which have hitherto evaded analytical solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03253 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Solving stochastic gene expression models using queueing theory: a tutorial review Szavits-Nossan, Juraj Grima, Ramon Subcellular Processes 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 non-stationary distributions and/or moments of mRNA/protein numbers, and bounds on the Fano factor. This approach may enable the solution of complex models which have hitherto evaded analytical solution. |
| title | Solving stochastic gene expression models using queueing theory: a tutorial review |
| topic | Subcellular Processes |
| url | https://arxiv.org/abs/2307.03253 |