Reconstructing dynamic regulatory maps.

Reconstructing dynamic regulatory maps. Research Abstract Details 

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Reconstructing dynamic regulatory maps. Abstract Text:

Jason Ernst,Oded Vainas,Christopher T Harbison,Itamar Simon,Ziv Bar-Joseph,

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.

Reconstructing dynamic regulatory maps. Publishing Authors By Initials

J Ernst,O Vainas,CT Harbison,I Simon,Z Bar-Joseph,

For similar proteins: transcription factors research abstracts see: proteins: transcription factors research

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Reconstructing dynamic regulatory maps. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Molecular systems biology

VOLUME: 3

Page Numbers: 74

Journal Abbreviation: Mol. Syst. Biol.

ISSN: 1744-4292

DAY: 16

MONTH: 01

YEAR: 2007

Reconstructing dynamic regulatory maps. Information

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LANGUAGE: eng

NlmUniqueID: 101235389

Reconstructing dynamic regulatory maps. Keywords Mesh Terms:

KEYWORDS: Transcription Factors

MESH TERMS: metabolism

Chemical & Substance for Abstract: Reconstructing dynamic regulatory maps. Information

Substance Name: Transcription Factors

Registry Number: 0

Grant and Affiliation Information for Reconstructing dynamic regulatory maps.

AFFILIATION: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Country: England

AGENCY: United States NIAID

GRANT: N01 AI-5001

ACRONYM: AI

MEDLINETA: Mol Syst Biol

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