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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Abstract
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.