Linked Life Data

19327819

Source:http://linkedlifedata.com/resource/pubmed/id/19327819

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-4-6
pubmed:abstractText
Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows "de novo" construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo "benchmarking" of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1097-4172
pubmed:author
pubmed:issnType
Electronic
pubmed:day
3
pubmed:volume
137
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
172-81
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.
pubmed:affiliation
Telethon Institute of Genetics and Medicine, Naples 80131, Italy.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't