Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.
Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.
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强相互作用限制下非编码小 RNA 基因调控的随机建模。
DOI：
10.1016/j.bpj.2018.04.044
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发表时间：
2018
影响因子：
3.4
通讯作者：
Kulkarni,RahulV
中科院分区：
文献类型：
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作者：
Kumar,Niraj;Zarringhalam,Kourosh;Kulkarni,RahulV
众所周知，非编码小rna （sRNAs）在调节多种细胞过程中发挥关键作用，其失调与多种疾病（如癌症）有关。这类疾病还以表型异质性为特征，这通常是由基因表达的内在随机性驱动的。相应地，人们对开发定量模型非常感兴趣，该模型关注随机基因表达与sRNAs调控之间的相互作用。我们考虑了sRNAs调控随机基因表达的规范模型，其中组成表达的sRNAs和mrna之间的相互作用导致化学计量相互降解。考虑到sRNAs和mrna之间的非线性相互作用项，该模型的精确解在解析上是难以解决的，理论方法通常调用平均场近似。然而，在强相互作用和低丰度的限制下，平均场结果是不准确的；因此，需要替代的理论方法。在这项工作中，我们获得了sRNAs在强相互作用极限下调控随机基因表达的规范模型的分析结果。我们导出了mrna和sRNAs在强相互作用极限下联合分布的稳态生成函数的分析结果，并利用导出的结果获得了表征相应蛋白质稳态分布的解析表达式。所获得的结果可以作为分析涉及sRNAs的遗传电路的基础，并为sRNAs在强相互作用限制下调节随机基因表达的作用提供新的见解。
Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model of regulation of stochastic gene expression by sRNAs, wherein interaction between constitutively expressed sRNAs and mRNAs leads to stoichiometric mutual degradation. The exact solution of this model is analytically intractable given the nonlinear interaction term between sRNAs and mRNAs, and theoretical approaches typically invoke the mean-field approximation. However, mean-field results are inaccurate in the limit of strong interactions and low abundances; thus, alternative theoretical approaches are needed. In this work, we obtain analytical results for the canonical model of regulation of stochastic gene expression by sRNAs in the strong interaction limit. We derive analytical results for the steady-state generating function of the joint distribution of mRNAs and sRNAs in the limit of strong interactions and use the results derived to obtain analytical expressions characterizing the corresponding protein steady-state distribution. The results obtained can serve as building blocks for the analysis of genetic circuits involving sRNAs and provide new insights into the role of sRNAs in regulating stochastic gene expression in the limit of strong interactions.