Learning global models of transcriptional regulatory networks from data.

Aviv Madar, Richard Bonneau

Research output: Contribution to journalArticle

Abstract

Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.

Original languageEnglish (US)
Pages (from-to)181
Number of pages1
JournalMethods in Molecular Biology
Volume541
StatePublished - 2009

Fingerprint

Gene Regulatory Networks
Learning
Gene Expression
Systems Biology
Transcription Factors
Genome
Oxygen
Weights and Measures

ASJC Scopus subject areas

  • Genetics
  • Molecular Biology

Cite this

Learning global models of transcriptional regulatory networks from data. / Madar, Aviv; Bonneau, Richard.

In: Methods in Molecular Biology, Vol. 541, 2009, p. 181.

Research output: Contribution to journalArticle

@article{c00c731a38404d27ab8d5ac21970d4f0,
title = "Learning global models of transcriptional regulatory networks from data.",
abstract = "Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.",
author = "Aviv Madar and Richard Bonneau",
year = "2009",
language = "English (US)",
volume = "541",
pages = "181",
journal = "Methods in Molecular Biology",
issn = "1064-3745",
publisher = "Humana Press",

}

TY - JOUR

T1 - Learning global models of transcriptional regulatory networks from data.

AU - Madar, Aviv

AU - Bonneau, Richard

PY - 2009

Y1 - 2009

N2 - Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.

AB - Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.

UR - http://www.scopus.com/inward/record.url?scp=67149141513&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67149141513&partnerID=8YFLogxK

M3 - Article

C2 - 19381524

AN - SCOPUS:67149141513

VL - 541

SP - 181

JO - Methods in Molecular Biology

JF - Methods in Molecular Biology

SN - 1064-3745

ER -