Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
Right arrow Alert me when this article is cited
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 Ong, I. M.
Right arrow Articles by Page, D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ong, I. M.
Right arrow Articles by Page, D.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 18 no. 90001 2002
Pages S241-S248
© 2002 Oxford University Press

Modelling regulatory pathways in E. coli from time series expression profiles

Irene M. Ong 1, Jeremy D. Glasner 3 and David Page 1,2

1 Department of Computer Sciences,
2 Department of Biostatistics & Medical Informatics
3 Department of Genetics, University of Wisconsin, Madison 53706, USA

Received on January 24, 2002 ; revised on April 1, 2002 ; accepted on April 1, 2002

Motivation: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data.

Results: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.

Contact: ong{at}cs.wisc.edu

Keywords: Dynamic Bayesian networks; regulatory pathways; time series gene expression; operon model.


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
 BioinformaticsHome page
D. Nam, S. H. Yoon, and J. F. Kim
Ensemble learning of genetic networks from time-series expression data
Bioinformatics, December 1, 2007; 23(23): 3225 - 3231.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
L. Y. T. Inoue, M. Neira, C. Nelson, M. Gleave, and R. Etzioni
Cluster-based network model for time-course gene expression data
Biostat., July 1, 2007; 8(3): 507 - 525.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Pan, T. Durfee, J. Bockhorst, and M. Craven
Connecting quantitative regulatory-network models to the genome
Bioinformatics, July 1, 2007; 23(13): i367 - i376.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Shi, T. Mitchell, and Z. Bar-Joseph
Inferring pairwise regulatory relationships from multiple time series datasets
Bioinformatics, March 15, 2007; 23(6): 755 - 763.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Z. Li, S. M. Shaw, M. J. Yedwabnick, and C. Chan
Using a state-space model with hidden variables to infer transcription factor activities
Bioinformatics, March 15, 2006; 22(6): 747 - 754.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
P. Larranaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armananzas, G. Santafe, A. Perez, et al.
Machine learning in bioinformatics
Brief Bioinform, March 1, 2006; 7(1): 86 - 112.
[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.