Ranked prediction of p53 targets using hidden variable dynamic modeling.
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.
Institute of Child Health, University College London, Guilford Street, London WC1N 1EH, UK.
Martino
M
Barenco
Daniela
D
Tomescu
Daniel
D
Brewer
Robin
R
Callard
Jaroslav
J
Stark
Michael
M
Hubank
D045744:Cell Line, Tumor; D005720:Gamma Rays; D020869:Gene Expression Profiling; D016158:Genes, p53; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics; D034622:RNA Interference; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
D014157:Transcription Factors; D016159:Tumor Suppressor Protein p53