### Abstract

Gene transcription models must take account of intrinsic stochasticity. The Chemical Master Equation framework is based on modelling assumptions that are highly appropriate for this context, and the Stochastic Simulation Algorithm (also known as Gillespie's algorithm) allows for practical simulations to be performed. However, for large networks and/or fast reactions, such computations can be prohibitatively expensive. The Chemical Langevin regime replaces the massive ordinary dierential equation system with a small stochastic dierential equation system that is more amenable to computation. Although the transition from Chemical Master Equation to Chemical Langevin Equation can be justied rigorously in the large system size limit, there is very little guidance available about how closely the two models match for a xed system. Here, we consider a transcription model from the recent literature and show that it is possible to compare rst and second moments in the two stochastic settings. To analyse the Chemical Master Equation we use some recent work of Gadgil, Lee and Othmer, and to analyse the Chemical Langevin Equation we use
Ito's Lemma. We nd that there is a perfect match|both modelling regimes give the same means, variances and correlations for all components in the system. The
model that we analyse involves 'unimolecular reactions', and we nish with some numerical simulations involving dimerization to show that the means and variances
in the two regimes can also be close when more general 'bimolecular reactions' are involved.

Original language | English |
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Pages (from-to) | 31-40 |

Number of pages | 10 |

Journal | Theoretical Computer Science |

Volume | 408 |

Issue number | 1 |

DOIs | |

Publication status | Published - 17 Nov 2008 |

### Keywords

- chemical kinetics
- gene regulation
- Gillespie
- multi-scale
- moments
- noise
- stochastic simulation
- systems biology
- transcription
- translation

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## Cite this

Khanin, R., & Higham, D. J. (2008). Chemical master equation and Langevin regimes for a gene transcription model.

*Theoretical Computer Science*,*408*(1), 31-40. https://doi.org/10.1016/j.tcs.2008.07.007