Special function methods for bursty models of transcription.
Special function methods for bursty models of transcription.
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DOI：
10.1103/physreve.102.022409
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Gennady Gorin;L. Pachter
中科院分区：
文献类型：
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作者：
Gennady Gorin;L. Pachter
我们探讨了一个马尔可夫模型用于分析基因表达，涉及突发生产的前mRNA，其转换为成熟的mRNA，其随之而来的降解。我们证明，用于计算的随机系统的解决方案的集成可以近似的特殊功能的评价。此外，特殊函数解的形式推广到更广泛的一类突发分布。鉴于从转录组学数据推断生物物理参数的更广泛目标，我们将该方法应用于模拟数据，证明了对精度和运行时间的有效控制。最后，我们提出并验证了一个非贝叶斯方法的参数估计的基础上的目标联合分布的前mRNA和mRNA的特征函数。
We explore a Markov model used in the analysis of gene expression, involving the bursty production of pre-mRNA, its conversion to mature mRNA, and its consequent degradation. We demonstrate that the integration used to compute the solution of the stochastic system can be approximated by the evaluation of special functions. Furthermore, the form of the special function solution generalizes to a broader class of burst distributions. In light of the broader goal of biophysical parameter inference from transcriptomics data, we apply the method to simulated data, demonstrating effective control of precision and runtime. Finally, we propose and validate a non-Bayesian approach for parameter estimation based on the characteristic function of the target joint distribution of pre-mRNA and mRNA.