Bioinformatics Advance Access originally published online on October 14, 2004
Bioinformatics 2005 21(6):765-773; doi:10.1093/bioinformatics/bti064
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Reconstructing biological networks using conditional correlation analysis
Computational Biology Center, IBM T.J. Watson Research Center PO Box 218, Yorktown Heights, NY 10598, USA
*To whom correspondence should be addressed.
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 networks of influences, physical or statistical, between cellular components. We propose an experimental method for 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 proposed 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 investigated 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 as follows: (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 Escherichia coli with a reasonably low error rate.
Contact: gustavo{at}us.ibm.com
Supplementary information: Available from our website: www.research.ibm.com/FunGen
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  Ning Sun and Hongyu Zhao Reconstructing transcriptional regulatory networks through genomics data Statistical Methods in Medical Research, December 1, 2009; 18(6): 595 - 617. [Abstract] [PDF]
|
|
|
|
 |
|
 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]
|
|
|
|
|
|
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]
|
|