This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplemental material
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrowReprints and Permissions
Right arrow Copyright Information
Right arrow Books from ASM Press
Right arrow MicrobeWorld
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Oberhardt, M. A.
Right arrow Articles by Papin, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Oberhardt, M. A.
Right arrow Articles by Papin, J. A.

 Previous Article  |  Next Article 

Journal of Bacteriology, April 2008, p. 2790-2803, Vol. 190, No. 8
0021-9193/08/$08.00+0     doi:10.1128/JB.01583-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.

Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1{triangledown} ,{dagger}

Matthew A. Oberhardt,1,{ddagger} Jacek Puchalka,2,{ddagger} Kimberly E. Fryer,1 Vítor A. P. Martins dos Santos,2,§* and Jason A. Papin1,§*

Department of Biomedical Engineering, University of Virginia Health System, Box 800759, Charlottesville, Virginia 22908,1 Helmholtz Center for Infection Research (HZI), Inhoffenstrasse 7, D-38124 Braunschweig, Germany2

Received 30 September 2007/ Accepted 3 January 2008

Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.


* Corresponding author. Mailing address for Jason A. Papin: Department of Biomedical Engineering, University of Virginia Health System, Box 800759, Charlottesville, VA 22908. Phone: (434) 924-8195. Fax: (434) 982-3870. E-mail: papin{at}virginia.edu. Mailing address for Vítor A. P. Martins dos Santos: Helmholtz Center for Infection Research (HZI), Inhoffenstrasse 7, D-38124 Braunschweig, Germany. Phone: 49-531-6181-4008. Fax: 49-531-6181-5002. E-mail: vds{at}helmholtz-hzi.de

{triangledown} Published ahead of print on 11 January 2008.

{dagger} Supplemental material for this article may be found at http://jb.asm.org/.

{ddagger} M.A.O. and J.P. contributed equally to this study.

§ V.A.P.M.D.S. and J.A.P. contributed equally to this study.


Journal of Bacteriology, April 2008, p. 2790-2803, Vol. 190, No. 8
0021-9193/08/$08.00+0     doi:10.1128/JB.01583-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.




This article has been cited by other articles:

  • Raman, K., Chandra, N. (2009). Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 10: 435-449 [Abstract] [Full Text]  
  • Mazumdar, V., Snitkin, E. S., Amar, S., Segre, D. (2009). Metabolic Network Model of a Human Oral Pathogen. J. Bacteriol. 191: 74-90 [Abstract] [Full Text]