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PMID: 16584535 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Ranked prediction of p53 targets using hidden variable dynamic modeling.

Genome biology ·Vol. 7 ·No. 3 ·2006-00-00 ·Pages R25

Barenco M, Tomescu D, Brewer D, Callard R, Stark J, Hubank M

Abstract

Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively.

MeSH Terms
Cell Line, Tumor Gamma Rays Gene Expression Profiling Genes, p53 Genetic Variation Humans Models, Genetic Models, Theoretical Oligonucleotide Array Sequence Analysis Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics RNA Interference Transcription Factors/genetics,metabolism Transcription, Genetic Tumor Suppressor Protein p53/genetics
Chemicals
Transcription Factors Tumor Suppressor Protein p53
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Barenco Martino
Institute of Child Health, University College London, Guilford Street, London WC1N 1EH, UK.
Tomescu Daniela
Brewer Daniel
Callard Robin
Stark Jaroslav
Hubank Michael
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Article Info
Journal
Genome biology
Abbr.
Genome Biol
ISSN
1474-760X
DOI
10.1186/gb-2006-7-3-r25
Published
2006-00-00
Epub
2006-00-31
Pages
R25
Language
English
Region
England
NLM ID
100960660
PMCID
PMC1557743
Subset
IM
Grants
Biotechnology and Biological Sciences Research Council · BB/E008488/1 · United Kingdom
External Links
PubMed source DOI: 10.1186/gb-2006-7-3-r25 PMC Full Text View journal details
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