The probabilistic nature of the biological chemical reactions is caused by the low copy number of the gene, low abundance of the mRNA and some proteins. Stochastic simulation algorithm can describe such fluctuation in gene expression level. We will introduce the Gillespie algorithm [1], which is a comprehensive sampling method without approximations. Converting concentrations and rate constants to particle numbers and reaction propensity will be covered.

 

The chemical Langevin equation [2] provided the theoretical ground for improving the efficiency of stochastic simulation. Based on that, the Langevin algorithm increases the time step, thus, it is an efficient and versatile means to simulate biological systems with huge abundance of components. Additionally, the burst Langevin algorithm [3] will be introduced for systems with either or both mRNA and protein burst productions. Burst productions of mRNA (protein) are the main source of biological noise. In the three-state gene expression model, the burst Langevin algorithm can neglect the fast-fluctuating upstream reactants tracking and introduces a parameter for the noise size of the downstream. Therefore, we can modify the noise size for the downstream gene expression. Overall, we demonstrate stochastic simulation algorithms for analysis of genetic regulation networks with noise effects.

 

References:

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