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.
Highlights
High-throughput experimental technologies enable the production of heterogeneous data, such as expression profiles and proteomic data, for almost any organism of interest
A detailed mathematical representation of the in vivo cellular network is required to obtain a holistic understanding of cellular processes from these data sets and to quantitatively integrate them into a biological context. One such approach is the bottom-up network reconstruction, which builds manually networks in a brick-by-brick manner using genome annotation and component-specific information [1,2]. This reconstruction procedure is well established for metabolic reaction networks and has been applied to many organisms, including Human [3], Saccharomyces cerevisiae [4,5], Leishmani major [6], Escherichia coli [7], Helicobacter pylori [8], Pseudomonas aeruginosa [9], and Pseudomonas putida [10,11]
These bottom-up metabolic networks differ from other network reconstructions as they are tailored to the genomic content of the target organism and built manually using biochemical, physiological, and other experimental information in addition to the genome annotation
Summary
High-throughput experimental technologies enable the production of heterogeneous data, such as expression profiles and proteomic data, for almost any organism of interest. A detailed mathematical representation of the in vivo cellular network is required to obtain a holistic understanding of cellular processes from these data sets and to quantitatively integrate them into a biological context One such approach is the bottom-up network reconstruction, which builds manually networks in a brick-by-brick manner using genome annotation and component-specific information (e.g., biochemical characterization of enzymes) [1,2]. This reconstruction procedure is well established for metabolic reaction networks and has been applied to many organisms, including Human [3], Saccharomyces cerevisiae [4,5], Leishmani major [6], Escherichia coli [7], Helicobacter pylori [8], Pseudomonas aeruginosa [9], and Pseudomonas putida [10,11] (see http://systemsbiology.ucsd.edu/ for an continually updated table of metabolic reconstructions). Metabolic network reconstruction have demonstrated to be useful in at least 5 areas of applications [2]: (i) biological discovery
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