Linked Life Data

21471013

Source:http://linkedlifedata.com/resource/pubmed/id/21471013

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2011-5-27
pubmed:abstractText
MOTIVATION: Accurate, context-specific regulation of gene expression is essential for all organisms. Accordingly, it is very important to understand the complex relations within cellular gene regulatory networks. A tool to describe and analyze the behavior of such networks are Boolean models. The reconstruction of a Boolean network from biological data requires identification of dependencies within the network. This task becomes increasingly computationally demanding with large amounts of data created by recent high-throughput technologies. Thus, we developed a method that is especially suited for network structure reconstruction from large-scale data. In our approach, we took advantage of the fact that a specific transcription factor often will consistently either activate or inhibit a specific target gene, and this kind of regulatory behavior can be modeled using monotone functions. RESULTS: To detect regulatory dependencies in a network, we examined how the expression of different genes correlates to successive network states. For this purpose, we used Pearson correlation as an elementary correlation measure. Given a Boolean network containing only monotone Boolean functions, we prove that the correlation of successive states can identify the dependencies in the network. This method not only finds dependencies in randomly created artificial networks to very high percentage, but also reconstructed large fractions of both a published Escherichia coli regulatory network from simulated data and a yeast cell cycle network from real microarray data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1529-36
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Inferring Boolean network structure via correlation.
pubmed:affiliation
Research group Bioinformatics and Systems Biology, Clinic for Internal Medicine I, University Medical Center Ulm, Ulm, Germany.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't