PLoS Computational Biology (PLOS COMPUT BIOL)

Publisher: Public Library of Science; International Society for Computational Biology, Public Library of Science

Journal description

PLoS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.

Current impact factor: 4.62

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 4.62
2011 Impact Factor 5.215
2010 Impact Factor 5.515
2009 Impact Factor 5.759
2008 Impact Factor 6.236
2007 Impact Factor 6.236
2006 Impact Factor 4.914
2005 Impact Factor

Impact factor over time

Impact factor

Additional details

5-year impact 5.28
Cited half-life 4.30
Immediacy index 0.81
Eigenfactor 0.08
Article influence 2.34
Website PLoS Computational Biology website
Other titles PLOS computational biology (Online), PLOS computational biology, Public Library of Science computational biology, Computational biology
ISSN 1553-734X
OCLC 57176679
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Public Library of Science

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Creative Commons Attribution License
    • Eligible UK authors may deposit in OpenDepot
    • Publisher's version/PDF may be used
    • All titles are open access journals
  • Classification

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Hydrogen peroxide (H2O2) is used by phagocytic cells of the innate immune response to kill engulfed bacteria. H2O2 diffuses freely into bacteria, where it can wreak havoc on sensitive biomolecules if it is not rapidly detoxified. Accordingly, bacteria have evolved numerous systems to defend themselves against H2O2, and the importance of these systems to pathogenesis has been substantiated by the many bacteria that require them to establish or sustain infections. The kinetic competition for H2O2 within bacteria is complex, which suggests that quantitative models will improve interpretation and prediction of network behavior. To date, such models have been of limited scope, and this inspired us to construct a quantitative, systems-level model of H2O2 detoxification in Escherichia coli that includes detoxification enzymes, H2O2-dependent transcriptional regulation, enzyme degradation, the Fenton reaction and damage caused by •OH, oxidation of biomolecules by H2O2, and repair processes. After using an iterative computational and experimental procedure to train the model, we leveraged it to predict how H2O2 detoxification would change in response to an environmental perturbation that pathogens encounter within host phagosomes, carbon source deprivation, which leads to translational inhibition and limited availability of NADH. We found that the model accurately predicted that NADH depletion would delay clearance at low H2O2 concentrations and that detoxification at higher concentrations would resemble that of carbon-replete conditions. These results suggest that protein synthesis during bolus H2O2 stress does not affect clearance dynamics and that access to catabolites only matters at low H2O2 concentrations. We anticipate that this model will serve as a computational tool for the quantitative exploration and dissection of oxidative stress in bacteria, and that the model and methods used to develop it will provide important templates for the generation of comparable models for other bacterial species.
    No preview · Article · Nov 2015 · PLoS Computational Biology