The Ray and Stephanie Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
J R Soc Interface. 2012 Jul 7;9(72):1589-98. doi: 10.1098/rsif.2011.0757. Epub 2012 Jan 11.
Recent analyses with high-resolution single-molecule experimental methods have shown highly irregular and variable bursting of mRNA in a wide range of organisms. Noise in gene expression is thought to be beneficial in cell fate specifications, as it can lay a foundation for phenotypic diversification of isogenetic cells in the homogeneous environment. However, because the stability of proteins is, in many cases, higher than that of mRNAs, noise from transcriptional bursting can be considerably buffered at the protein level, limiting the effect of noisy mRNAs at a more global regulation level. This raises a question as to what constructive role noisy mRNAs can play in the system-level dynamics. In this study, we have addressed this question using the computational models that extend the conventional transcriptional bursting model with a post-transcriptional regulation step. Surprisingly, by comparing this stochastic model with the corresponding deterministic model, we find that intrinsic fluctuations can substantially increase the expected mRNA level. Because effects of a higher mRNA level can be transmitted to the protein level even with slow protein degradation rates, this finding suggests that an increase in the protein level is another potential effect of transcriptional bursting. Here, we show that this striking steady state increase is caused by the asynchronous nature of molecular reactions, which allows the transcriptional regulation model to create additional modes of qualitatively distinct dynamics. Our results illustrate non-intuitive effects of reaction asynchronicity on system dynamics that cannot be captured by the traditional deterministic framework. Because molecular reactions are intrinsically stochastic and asynchronous, these findings may have broad implications in modelling and understanding complex biological systems.
最近利用高分辨率单分子实验方法进行的分析表明，在广泛的生物中，mRNA 的爆发式释放具有高度不规则和可变性。人们认为基因表达的噪声在细胞命运特化中是有益的，因为它可以为同质环境中同基因细胞的表型多样化奠定基础。然而，由于蛋白质的稳定性在许多情况下高于 mRNA，转录爆发产生的噪声可以在蛋白质水平上得到相当大的缓冲，从而限制了嘈杂的 mRNA 在更全局的调控水平上的影响。这就提出了一个问题，即嘈杂的 mRNA 在系统级动力学中可以发挥什么建设性作用。在这项研究中，我们使用了扩展传统转录爆发模型的计算模型来解决这个问题，该模型增加了一个转录后调控步骤。令人惊讶的是，通过将这个随机模型与相应的确定性模型进行比较，我们发现内在波动可以显著增加预期的 mRNA 水平。因为更高的 mRNA 水平的影响可以通过缓慢的蛋白质降解率传递到蛋白质水平，所以这一发现表明，蛋白质水平的增加是转录爆发的另一个潜在影响。在这里，我们表明这种显著的稳态增加是由分子反应的异步性质引起的，这使得转录调控模型能够创建具有定性不同动力学的额外模式。我们的结果说明了反应异步性对系统动力学的非直观影响，而这是传统的确定性框架无法捕捉到的。由于分子反应本质上是随机和异步的，这些发现可能对复杂生物系统的建模和理解具有广泛的意义。
