Skip Navigation


Bioinformatics Advance Access originally published online on August 22, 2006
Bioinformatics 2006 22(21):2674-2680; doi:10.1093/bioinformatics/btl440
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
22/21/2674    most recent
btl440v1
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 arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Radde, N.
Right arrow Articles by Forst, C. V.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Radde, N.
Right arrow Articles by Forst, C. V.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Systematic component selection for gene-network refinement

Nicole Radde 1,*,{dagger}, Jutta Gebert 1,*,{dagger} and Christian V. Forst 2,*

1 Center for Applied Computer Science, University of Cologne Weyertal 80, 50931 Cologne, Germany
2 Los Alamos National Laboratory PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA

*To whom correspondence should be addressed.

Motivation: A quantitative description of interactions between cell components is a major challenge in Computational Biology. As a method of choice, differential equations are used for this purpose, because they provide a detailed insight into the dynamic behavior of the system. In most cases, the number of time points of experimental time series is usually too small to estimate the parameters of a model of a whole gene regulatory network based on differential equations, such that one needs to focus on subnetworks consisting of only a few components. For most approaches, the set of components of the subsystem is given in advance and only the structure has to be estimated. However, the set of components that influence the system significantly are not always known in advance, making a method desirable that determines both, the components that are included into the model and the parameters.

Results: We have developed a method that uses gene expression data as well as interaction data between cell components to define a set of genes that we use for our modeling. In a subsequent step, we estimate the parameters of our model of piecewise linear differential equations and evaluate the results simulating the behavior of the system with our model.

We have applied our method to the DNA repair system of Mycobacterium tuberculosis. Our analysis predicts that the gene Rv2719c plays an important role in this system.

Contact: {radde.gebert}{at}zpr.uni-koeln.de, chris{at}lanl.gov


Received on June 2, 2006; revised on August 9, 2006; accepted on August 10, 2006

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]



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.