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J Infect Dis. 2017 Feb 15;215(suppl_1):S37-S43. doi: 10.1093/infdis/jiw465.

Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1.

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Mathematics and Computer Science Division, Argonne National Laboratory, Argonne.
Department of Microbiology-Immunology.
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois.
Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, and.


Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.


Biolog; Klebsiella pneumoniae KPPR1; bacteria; flux balance analysis; gap filling; metabolic model; resistance; transposon insertion sequencing

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