Ranked prediction of p53 targets using hidden variable dynamic modeling

Martino Barenco, Daniela Tomescu, Daniel Brewer, Robin Callard, Jaroslav Stark, Michael Hubank

Research output: Contribution to journalArticlepeer-review

115 Citations (Scopus)


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.
Original languageEnglish
Article numberR25
JournalGenome Biology
Issue number3
Publication statusPublished - 31 Mar 2006


  • 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
  • RNA Interference
  • Transcription Factors
  • Transcription, Genetic
  • Tumor Suppressor Protein p53

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