Skip Navigation



Bioinformatics Advance Access published online on October 14, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti064
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/6/765    most recent
bti064v1
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 Rice, J. J.
Right arrow Articles by Stolovitzky, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Rice, J. J.
Right arrow Articles by Stolovitzky, G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received February 4, 2004
Revised August 3, 2004
Accepted August 23, 2004

Article

Reconstructing biological networks using conditional correlation analysis

John Jeremy Rice 1, Yuhai Tu 1, and Gustavo Stolovitzky 1*

1 Computational Biology Center, IBM T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA

* To whom correspondence should be addressed. E-mail: gustavo{at}us.ibm.com.


  Abstract

Motivation: One of the present challenges in biological research is the organization of the data originating from high throughput technologies. One way in which this information can be organized is in the form of pathways of interactions, physical or statistical, between cellular components. We propose an experimental method of probing biological networks, analyzing the resulting data, and reconstructing the network architecture.

Methods: We use networks of known topology consisting of nodes (genes), directed edges (gene-gene interactions), and a dynamics for the genes' mRNA concentrations in terms of the gene-gene interactions. We propose a network reconstruction algorithm based on the conditional correlation of the mRNA equilibrium concentration between two genes given that one of them was knocked down. Using simulated gene expression data on networks of known connectivity, we investigate how the reconstruction error is affected by noise, network topology, size, sparseness, and dynamic parameters.

Results: Errors arise from correlation between nodes connected through intermediate nodes (false positives) and when the correlation between two directly connected nodes is obscured by noise, non-linearity, or multiple inputs to the target node (false negatives). Two critical components of the method are: 1) the choice of an optimal correlation threshold for predicting connections, and 2) the reduction of errors arising from indirect connections (for which a novel algorithm is proposed). With these improvements, we can reconstruct networks with the topology of the transcriptional regulatory network in E. coli with a reasonably low error rate.

Supplementary Information: Available from our web site: www.research.ibm.com/FunGen.


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. Ucar, I. Neuhaus, P. Ross-MacDonald, C. Tilford, S. Parthasarathy, N. Siemers, and R.-R. Ji
Construction of a reference gene association network from multiple profiling data: application to data analysis
Bioinformatics, October 15, 2007; 23(20): 2716 - 2724.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
K. Missal, M. A. Cross, and D. Drasdo
Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment
Bioinformatics, March 15, 2006; 22(6): 731 - 738.
[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.