Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization.
Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization.
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DOI：
10.1371/journal.pcbi.1000312
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发表时间：
2009-03
影响因子：
4.3
通讯作者：
Palsson BØ
中科院分区：
文献类型：
--
作者：
Thiele I;Jamshidi N;Fleming RM;Palsson BØ
代谢网络重建代表有价值的支架'组学'数据集成，并用于计算询问网络属性。然而，它们没有明确地解释大分子的合成（即，蛋白质和RNA）。在这里，我们提出了第一个基因组规模的，精细的重建大肠杆菌的转录和翻译机器，它产生423个功能基因产物的序列特异性的方式和帐户的所有必要的化学转化。回顾了来自500多篇出版物和三个数据库的遗留数据，并考虑了许多途径，包括稳定的RNA成熟和修饰，蛋白质复合物的形成和铁硫簇生物发生。这种重建代表了E.大肠杆菌，其范围是独一无二的。此外，它被转换成数学模型，并用于：（1）定量整合基因表达数据作为反应约束，（2）计算功能网络状态，并与报道的实验数据进行比较。例如，该模型准确地预测了核糖体的产生，而没有任何参数化。此外，计算机模拟rRNA操纵子缺失表明，需要剩余rRNA操纵子上的高RNA聚合酶密度来重现所报告的实验核糖体数量。此外，功能蛋白质模块被确定，许多被发现含有来自多个子系统的基因产物，突出了这些蛋白质的功能相互作用。本研究对E.大肠杆菌的转录和翻译机制是系统生物学中的一个里程碑，因为它将使“组学”数据集的定量整合成为可能，从而研究基因型-表型关系的机制原则。系统生物学旨在以系统的方式了解细胞成分的相互作用。数学建模对于在概念和机械层面上集成和分析这些组件至关重要。到目前为止，详细的基因组规模的重建代谢已成为越来越多的生物体。虽然代谢在细胞中起着重要作用，但也需要考虑其他细胞功能，如信号传导，调节和大分子合成。例如，RNA和蛋白质合成所需的细胞机器由一组复杂的蛋白质组成。在这里，我们表明，可以收集所有必要的信息，为原核生物创造一个基因特异性，细粒度的大分子合成机制的代表。E.选择大肠杆菌作为模式生物是因为其具有丰富的可用信息。在质量平衡网络方面的转录和翻译的明确表示，使详细的，定量会计的蛋白质合成能力的E。coli in silico.因此，这项研究证明了构建非常大的网络的可行性，也代表了建立细胞生长模型的关键一步，该模型可以在基因组规模上以化学计量的方式解释基因特异性蛋白质的产生。
Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship. Systems biology aims to understand the interactions of cellular components in a systemic manner. Mathematical modeling is critical to the integration and analysis of these components on a conceptual as well as mechanistic level. To date, detailed genome-scale reconstructions of metabolism have become available for a growing number of organisms. Although metabolism has an important role in cells, other cellular functions need to be considered as well, such as signaling, regulation, and macromolecular synthesis. For instance, the cellular machinery required for RNA and protein synthesis consists of a complex set of proteins. Here, we show that one can collect all of the necessary information for a prokaryotic organism to create a gene-specific, fine-grained representation of the macromolecular synthesis machinery. E. coli was chosen as a model organism because of the wealth of available information. The explicit representation of transcription and translation in terms of a mass-balanced network enables a detailed, quantitative accounting of the protein synthesis capabilities of E. coli in silico. Hence, this study demonstrates the feasibility of constructing very large networks and also represents a critical step toward building cellular models of growth that can account for gene-specific protein production in a stoichiometric fashion on the genome scale.
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DOI：
10.1038/nrmicro1949
发表时间：
2009-02
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.6
作者：
Deuerling, E;Patzelt, H;Bukau, B
通讯作者：
Bukau, B
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
2.7
作者：
Barber, CB;Dobkin, DP;Huhdanpaa, H
通讯作者：
Huhdanpaa, H
影响因子：
11.4
作者：
CONDON, C;FRENCH, S;SQUIRES, CL
通讯作者：
SQUIRES, CL