Dissection of a complex transcriptional response using genome-wide transcriptional modelling

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

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Modern genomics technologies generate huge data sets creating a demand for systems level, experimentally verified, analysis techniques. We examined the transcriptional response to DNA damage in a human T cell line (MOLT4) using microarrays. By measuring both mRNA accumulation and degradation over a short time course, we were able to construct a mechanistic model of the transcriptional response. The model predicted three dominant transcriptional activity profiles-an early response controlled by NFkappaB and c-Jun, a delayed response controlled by p53, and a late response related to cell cycle re-entry. The method also identified, with defined confidence limits, the transcriptional targets associated with each activity. Experimental inhibition of NFkappaB, c-Jun and p53 confirmed that target predictions were accurate. Model predictions directly explained 70% of the 200 most significantly upregulated genes in the DNA-damage response. Genome-wide transcriptional modelling (GWTM) requires no prior knowledge of either transcription factors or their targets. GWTM is an economical and effective method for identifying the main transcriptional activators in a complex response and confidently predicting their targets.
Original languageEnglish
Pages (from-to)327
JournalMolecular Systems Biology
Publication statusPublished - 2009


  • Cell Line
  • Cluster Analysis
  • Computational Biology
  • DNA Damage
  • Gene Expression Profiling
  • Genome, Human
  • Humans
  • Models, Genetic
  • Oligonucleotide Array Sequence Analysis
  • RNA Stability
  • RNA, Messenger
  • Radiation, Ionizing
  • Reproducibility of Results
  • Time Factors
  • Transcription Factors
  • Transcription, Genetic
  • Tumor Suppressor Protein p53
  • Up-Regulation

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