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arXiv · 2108.10896

Stationarity and inference in multistate promoter models of stochastic gene expression via stick-breaking measures

William Lippitt·Sunder Sethuraman·Xueying Tang
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Abstract

In a general stochastic multistate promoter model of dynamic mRNA/protein interactions, we identify the stationary joint distribution of the promoter state, mRNA, and protein levels through an explicit `stick-breaking' construction of interest in itself. This derivation is a constructive advance over previous work where the stationary distribution is solved only in restricted cases. Moreover, the stick-breaking construction allows to sample directly from the stationary distribution, permitting inference procedures and model selection. In this context, we discuss numerical Bayesian experiments to illustrate the results.

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William Lippitt, Sunder Sethuraman, Xueying Tang. 2021-08-24. Stationarity and inference in multistate promoter models of stochastic gene expression via stick-breaking measures. https://arxiv.org/abs/2108.10896

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