Skip Navigation



Bioinformatics Advance Access published online on December 5, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm607
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
24/6/833    most recent
btm607v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
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 Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Vyshemirsky, V.
Right arrow Articles by Girolami, M. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Vyshemirsky, V.
Right arrow Articles by Girolami, M. A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bayesian Ranking of Biochemical System Models

Vladislav Vyshemirsky 1,* and Mark A. Girolami 1

1Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ.

*To whom correspondence should be addressed. Dr. Vladislav Vyshemirsky, E-mail: vvv{at}dcs.gla.ac.uk


  Abstract

Motivation: There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model.

Bayes factors are employed as a measure of evidential preference for one model over another. Marginal likelihood is a key component of Bayes factors, however computing the marginal likelihood is a difficult problem, as it involves integration of nonlinear functions in multidimensional space. There are a number of methods available to compute the marginal likelihood approximately. A detailed investigation of such methods is required to find ones that perform appropriately for biochemical modelling.

Results: We assess four methods for estimation of the marginal likelihoods required for computing Bayes factors. The Prior Arithmetic Mean estimator, the Posterior Harmonic Mean estimator, the Annealed Importance Sampling and the Annealing-Melting Integration methods are investigated and compared on a typical case study in Systems Biology. This allows us to understand the stability of the analysis results and make reliable judgements in uncertain context. We investigate the variance of Bayes factor estimates, and highlight the stability of the Annealed Importance Sampling and the Annealing-Melting Integration methods for the purposes of comparing nonlinear models.

Availability: Models used in this study are available in SBML format as the supplementary material to this paper.

Contact: vvv{at}dcs.gla.ac.uk

Associate Editor: Dr. Limsoon Wong

Received on August 28, 2007; revised on October 26, 2007; accepted on December 3, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 J R Soc InterfaceHome page
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P.H Stumpf
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
J R Soc Interface, February 6, 2009; 6(31): 187 - 202.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
V. Vyshemirsky and M. Girolami
BioBayes: A software package for Bayesian inference in systems biology
Bioinformatics, September 1, 2008; 24(17): 1933 - 1934.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
P. Gao, A. Honkela, M. Rattray, and N. D. Lawrence
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities
Bioinformatics, August 15, 2008; 24(16): i70 - i75.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.