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

2015 Impact Factor Available summer 2016
2014 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
    ​ green

Publications in this journal

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was [Formula: see text] times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.
    PLoS Computational Biology 01/2015; 11(1):e1003968. DOI:10.1371/journal.pcbi.1003968.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The knowledge of multiple conformational states is a prerequisite to understand the function of membrane transport proteins. Unfortunately, the determination of detailed atomic structures for all these conformational states with conventional high-resolution approaches is often difficult and sometimes unsuccessful. As an alternative, biophysical and biochemical approaches can provide a wealth of complementary structural information that can be exploited, with the help of advanced computational methods, to derive structural models of some of these conformational states. In particular, functional and spectroscopic measurements in combination with site-directed mutations constitute one important source of information for these mixed-resolution structural models. A very common problem with this strategy, however, is the difficulty to simultaneously integrate all the information from multiple disparate experiments (e.g., involving a large number of different mutations or chemical labels) in order to derive a unique structural model that is consistent with the data. To circumvent the problem, the experimental constraints are often represented simplified unrealistically as naive “through-space” interatomic distance restraints, leading to an unsatisfactory loss of important chemical details. To resolve this issue, a novel restrained molecular dynamics structural refinement method is developed to simultaneously incorporate multiple experimentally determined constraints (e.g., engineered metal bridges or spin-labels), each treated as an individual molecular fragment with all atomic details. In the method, the internal structure of each of the molecular fragments is treated realistically while there is no interaction between the different molecular fragments associated with separate constraints to avoid unphysical steric clashes. The method is illustrated by refining the structure of the voltage-sensing domain (VSD) of the Kv1.2 potassium channel in the resting state and exploring the distance histograms between spin-labels attached to T4 lysozyme. The resulting VSD structures are in good agreement with the consensus model of the resting state VSD and the spin-spin distance histograms from ESR/DEER experiments on T4 lysozyme are accurately reproduced.
    PLoS Computational Biology 01/2015;