Reverse-engineering transcription control networks

Timothy S. Gardner; Jeremiah J. Faith

doi:10.1016/j.plrev.2005.01.001

Reverse-engineering transcription control networks

Timothy S. Gardner, Jeremiah J. Faith

Research output: Contribution to journal › Review article › peer-review

183 Scopus citations

Abstract

Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each. We also discuss the practical considerations for developing and applying the various methods.

Original language	English
Pages (from-to)	65-88
Number of pages	24
Journal	Physics of Life Reviews
Volume	2
Issue number	1
DOIs	https://doi.org/10.1016/j.plrev.2005.01.001
State	Published - Mar 2005
Externally published	Yes

Keywords

Gene networks
Gene regulation
Machine learning
Reverse-engineering
Transcription control

Access to Document

10.1016/j.plrev.2005.01.001

Cite this

@article{498646289e184c11b93007fbab514d8a,

title = "Reverse-engineering transcription control networks",

abstract = "Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each. We also discuss the practical considerations for developing and applying the various methods.",

keywords = "Gene networks, Gene regulation, Machine learning, Reverse-engineering, Transcription control",

author = "Gardner, {Timothy S.} and Faith, {Jeremiah J.}",

note = "Funding Information: This work was supported the Department of Energy, the Pharmaceutical Research and Manufacturers of America Foundation, the Army Research Office, and the Whitaker Foundation. We also thank Diego di Bernardo, David Byrne, and Michael Thompson for their comments on the manuscript.",

year = "2005",

month = mar,

doi = "10.1016/j.plrev.2005.01.001",

language = "English",

volume = "2",

pages = "65--88",

journal = "Physics of Life Reviews",

issn = "1571-0645",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - Reverse-engineering transcription control networks

AU - Gardner, Timothy S.

AU - Faith, Jeremiah J.

N1 - Funding Information: This work was supported the Department of Energy, the Pharmaceutical Research and Manufacturers of America Foundation, the Army Research Office, and the Whitaker Foundation. We also thank Diego di Bernardo, David Byrne, and Michael Thompson for their comments on the manuscript.

PY - 2005/3

Y1 - 2005/3

N2 - Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each. We also discuss the practical considerations for developing and applying the various methods.

AB - Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each. We also discuss the practical considerations for developing and applying the various methods.

KW - Gene networks

KW - Gene regulation

KW - Machine learning

KW - Reverse-engineering

KW - Transcription control

UR - http://www.scopus.com/inward/record.url?scp=14844286390&partnerID=8YFLogxK

U2 - 10.1016/j.plrev.2005.01.001

DO - 10.1016/j.plrev.2005.01.001

M3 - Review article

AN - SCOPUS:14844286390

SN - 1571-0645

VL - 2

SP - 65

EP - 88

JO - Physics of Life Reviews

JF - Physics of Life Reviews

IS - 1

ER -

Reverse-engineering transcription control networks

Abstract

Keywords

Access to Document

Fingerprint

Cite this