DMCA
Computational and experimental approaches for modeling gene regulatory networks
| Venue: | Curr. Pharm. Design |
| Citations: | 10 - 0 self |
BibTeX
@ARTICLE{Goutsias_computationaland,
author = {J. Goutsias and N. H. Lee},
title = {Computational and experimental approaches for modeling gene regulatory networks},
journal = {Curr. Pharm. Design},
year = {},
pages = {2007}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract: To understand most cellular processes, one must understand how genetic information is processed. A formidable challenge is the dissection of gene regulatory networks to delineate how eukaryotic cells coordinate and govern patterns of gene expression that ultimately lead to a phenotype. In this paper, we review several approaches for modeling eukaryotic gene regulatory networks and for reverse engineering such networks from experimental observations. Since we are interested in elucidating the transcriptional regulatory mechanisms of colon cancer progression, we use this important biological problem to illustrate various aspects of modeling gene regulation. We discuss four important models: gene networks, transcriptional regulatory systems, Boolean networks, and dynamical Bayesian networks. We review state-of-the-art functional genomics techniques, such as gene expression profiling, cis-regulatory element identification, TF target gene identification, and gene silencing by RNA interference, which can be used to extract information about gene regulation. We can employ this information, in conjunction with appropriately designed reverse engineering algorithms, to construct a computational model of gene regulation that sufficiently predicts experimental observations. In the last part of this review, we focus on the problem of reverse engineering transcriptional regulatory networks by gene perturbations. We mathematically formulate this problem and discuss the role of experimental resolution in our ability to reconstruct accurate models of gene regulation. We conclude, by discussing a promising approach for inferring a transcriptional regulatory system from microarray data obtained by gene perturbations. 1.
Keyphrases
gene regulatory network gene regulation experimental approach gene perturbation experimental observation transcriptional regulatory system cellular process reverse engineering network tf target gene identification state-of-the-art functional genomics technique govern pattern genetic information experimental resolution cis-regulatory element identification microarray data transcriptional regulatory mechanism several approach formidable challenge reverse engineering transcriptional regulatory network gene expression reverse engineering algorithm important biological problem computational model last part boolean network rna interference gene expression profiling eukaryotic gene regulatory network promising approach important model various aspect dynamical bayesian network gene silencing gene network accurate model eukaryotic cell coordinate colon cancer progression
