Reconciling kinetic and thermodynamic models of bacterial transcription.
Reconciling kinetic and thermodynamic models of bacterial transcription.
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DOI：
10.1371/journal.pcbi.1008572
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发表时间：
2021-01
影响因子：
4.3
通讯作者：
Phillips R
中科院分区：
文献类型：
--
作者：
Morrison M;Razo-Mejia M;Phillips R
转录的研究仍然是现代生物学的核心之一，其影响范围从发育到代谢，从进化到疾病。使用包括荧光和测序读数在内的许多不同技术进行精确测量，提高了定量理解转录调控的标准。特别是，我们对最简单的遗传回路的理解在实验和理论上都得到了充分的完善，因此有可能仔细区分这种调节系统如何工作的不同概念图片。这种调控基序最初由Jacob和Monod在20世纪60年代提出，由结合到启动子位点并抑制转录的单个转录阻遏物组成。在本文中，我们展示了如何七个不同的模型，这个所谓的简单的抑制基序，基于热力学和动力学的思想，可以用来推导出预测的基因表达水平，并揭示了过去的热力学模型往往令人惊讶的成功。这些不同的模型，然后调用面对各种不同的数据的平均值，方差和完整的基因表达分布，说明在何种程度上，这些模型可以和不能区分，并建议一个两个国家的模型与突发大小的分布作为最有力的七个简单的抑制基序。随着新技术的出现，我们可以越来越精确地查询生物活性，大量的定量生物数据需要定量模型。转录调控是我们理解从发育到疾病的各种背景下细胞控制的核心特征也不例外，单细胞和单分子技术被常规部署用于研究细胞决策。这些数据已经成为测试转录模型的肥沃试验场，主要有两种类型：热力学模型（基于平衡统计力学）和动力学模型（基于化学动力学）。在本文中，我们研究了这些理论框架之间的对应关系的背景下，简单的阻遏基序，一个共同的监管架构在原核生物中，一个阻遏物与一个单一的结合位点调节表达。我们探讨了转录中所涉及的分子步骤的不同水平的粗粒化的后果，发现在平均基因表达水平上，不同的模型无法区分。然后，我们研究更高的时刻的基因表达分布，这使我们能够放弃几个模型，不同意与实验数据和支持最小的动力学模型。
The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif. With the advent of new technologies allowing us to query biological activity with ever increasing precision, the deluge of quantitative biological data demands quantitative models. Transcriptional regulation—a feature that lies at the core of our understanding of cellular control in myriad context ranging from development to disease—is no exception, with single-cell and single-molecule techniques being routinely deployed to study cellular decision making. These data have served as a fertile proving ground to test models of transcription that mainly come in two flavors: thermodynamic models (based on equilibrium statistical mechanics) and kinetic models (based on chemical kinetics). In this paper we study the correspondence between these theoretical frameworks in the context of the simple repression motif, a common regulatory architecture in prokaryotes in which a repressor with a single binding site regulates expression. We explore the consequences of different levels of coarse-graining of the molecular steps involved in transcription, finding that, at the level of mean gene expression, the different models cannot be distinguished. We then study higher moments of the gene expression distribution which allows us to discard several of the models that disagree with experimental data and supporting a minimal kinetic model.
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影响因子：
4.3
作者：
Brewster RC;Jones DL;Phillips R
通讯作者：
Phillips R
影响因子：
30.8
作者：
Hammar P;Walldén M;Fange D;Persson F;Baltekin O;Ullman G;Leroy P;Elf J
通讯作者：
Elf J
影响因子：
64.8
作者：
Gertz, Jason;Siggia, Eric D.;Cohen, Barak A.
通讯作者：
Cohen, Barak A.
DOI：
10.1111/j.2044-8317.2011.02037.x
发表时间：
2013-02
期刊：
The British journal of mathematical and statistical psychology
影响因子：
--
作者：
Gelman A;Shalizi CR
通讯作者：
Shalizi CR
DOI：
10.1073/pnas.1907869116
发表时间：
2019-09-10
影响因子：
11.1
作者：
Chure, Griffin;Razo-Mejia, Manuel;Phillips, Rob
通讯作者：
Phillips, Rob