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Genetic Network Inference: From Co-Expression Clustering to Reverse EngineeringAdvances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.
Document ID
20010002832
Document Type
Reprint (Version printed in journal)
Authors
Dhaeseleer, Patrik (New Mexico Univ. Albuquerque, NM United States)
Liang, Shoudan (NASA Ames Research Center Moffett Field, CA United States)
Somogyi, Roland (Incyte Pharmaceuticals, Inc. Palo Alto, CA United States)
Date Acquired
August 20, 2013
Publication Date
January 1, 2000
Publication Information
Publication: Bioinformatics
Volume: 16
Issue: 8
Subject Category
Life Sciences (General)
Funding Number(s)
CONTRACT_GRANT: N00014-99-I-0417
CONTRACT_GRANT: NSF IRI-97-11199
CONTRACT_GRANT: NCC2-794
Distribution Limits
Public
Copyright
Other