Analytic Approaches to Stochastic Gene Expression in Multicellular Systems
Analytic Approaches to Stochastic Gene Expression in Multicellular Systems
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DOI：
10.1016/j.bpj.2013.10.033
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发表时间：
2013-12-17
影响因子：
3.4
通讯作者：
Boettiger, Alistair Nicol
中科院分区：
文献类型：
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作者：
Boettiger, Alistair Nicol
确定性热力学模型的复杂系统，控制基因表达的后生动物，帮助研究人员确定的基本主题，在转录调控。然而，定量单细胞研究越来越多地确定控制表达变异的调节机制。这种行为不能被确定性模型捕获，并且不适合依赖于连续近似的当代随机方法，如Langevin方法。幸运的是，转录建模的理论进展已经汇集了一些一般性的结果，这些结果可以很容易地应用于仅通过确定性方法探索的系统。在这里，我回顾了一些最近的实验证据的重要性，在胚胎发育过程中的遗传调控随机效应，并讨论了关键的结果，从马尔可夫理论，可用于模拟这种调节。然后，我讨论了几对监管机制最近通过马尔可夫研究的方法。在每种情况下，确定性处理预测机制之间没有差异，但统计学处理揭示了转录活性的显著不同分布的潜力。在这种情况下，基因调控的特点，似乎不必要的复杂的进化行李可能会赞赏他们的关键贡献的可靠性和精确的基因表达。
Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.