Gene Lethality Detection and Characterization via Topological Analysis of Regulatory Networks

Recent engineering approaches to the analysis of biomolecular systems have shown a connection between the topological centrality of a protein in an interaction network and importance of that protein's underlying gene in the survival of the system. The recognized reason for this connection is th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems. 1, Fundamental theory and applications Fundamental theory and applications, 2006-11, Vol.53 (11), p.2438-2443
Main Authors: Alterovitz, G., Muralidhar, V., Ramoni, M.F.
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Biological information theory
Biomedical informatics
Control
deterministic algorithms
E coli
Genes
Genetics
Genomics
graph theory
Hospitals
Humans
Joints
Lethality
Networks
Organisms
Pediatrics
Protein engineering
Proteins
Studies
Survival
system analysis and design
Topology
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recent engineering approaches to the analysis of biomolecular systems have shown a connection between the topological centrality of a protein in an interaction network and importance of that protein's underlying gene in the survival of the system. The recognized reason for this connection is the scale-free nature of these networks, which induces the presence of highly connected nodes (hubs) in the network. This approach would also be expected to identify these critical genes-called lethal because their removal would kill the cell- in the E. coli transcriptional regulation network, a prototypical scale-free network. However, this method does little better than simple random selection. Here we introduce a new approach, based on random walks, that identifies the critical nodes in the network on the basis of global properties of the network rather than the local connectivity of each node; we show that it significantly outperforms the standard local approach. We also find that lethal genes are more likely to belong to certain functional categories. The success of the our approach suggests that computational lethal gene detection can be effective and that scale free networks-and their biological instances-may enjoy more global properties than the one identified so far
ISSN:1549-8328
1057-7122
1558-0806
DOI:10.1109/TCSI.2006.884458