Genome-Scale Reconstruction of Escherichia coli's Transcriptional and Translational Machinery: A Knowledge Base, Its Mathematical Formulation, and Its Functional Characterization

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

Poor Patient Comprehension of Abnormal Mammography Results

BACKGROUND: Screening mammography for women 50 to 69 years of age may lead to 50% having an abnormal study. We set out to determine the proportion of women who understand their abnormal mammogram results and the factors that predict understanding. METHODS: We surveyed 970 women age 40 to 80 years identified with abnormal mammograms from 4 clinical sites. We collected information on demographic factors, language of interview, consultation with a primary care physician, receipt of follow-up tests, and method of notification of index mammogram result. This study examines the following outcomes: the participant's report of understanding of her physician's explanation of results of the index mammogram, and a comparison of the radiology report to the participant's report of her index mammogram result. Multivariate models controlled for age, education, income, insurance status, and clinical site. RESULTS: The majority (70%) reported a “full understanding” of their physician's explanation of their abnormal mammogram, but a significant minority (30%) reported less than a full understanding (somewhat, not at all, did not explain). Among women of Asian ethnicity, only 63% reported full understanding. Asian ethnicity was a negative predictor (odds ratio [OR], 0.4; 95% confidence interval [CI], 0.3 to 0.7), and consultation with a primary care physician was a positive predictor (OR, 2.3; 95% CI, 1.7 to 3.3) of reported full understanding. Of the 304 women with a suspicious abnormality, only 51% understood their result to be abnormal. Women notified in person or by telephone were more likely than women notified in writing to understand their result to be abnormal (OR, 2.3; 95% CI, 1.2 to 4.8). CONCLUSION: Almost half of women with the most suspicious mammograms did not understand that their result was abnormal. Our data suggest that direct communication with a clinician in person or by phone improves comprehension