A combined expression-interaction model for inferring the temporal activity of transcription factors

Yanxin Shi, Michael Klutstein, Itamar Simon, Tom Mitchell, Ziv Bar-Joseph*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Methods suggested for reconstructing regulatory networks can be divided into two sets based on how the activity level of transcription factors (TFs) is inferred. The first group of methods relies on the expression levels of TFs, assuming that the activity of a TF is highly correlated with its mRNA abundance. The second treats the activity level as unobserved and infers it from the expression of the genes that the TF regulates. While both types of methods were successfully applied, each suffers from drawbacks that limit their accuracy. For the first set, the assumption that mRNA levels are correlated with activity is violated for many TFs due to post-transcriptional modifications. For the second, the expression level of a TF which might be informative is completely ignored. Here we present the post-transcriptional modification model (PTMM) that, unlike previous methods, utilizes both sources of data concurrently. Our method uses a switching model to determine whether a TF is transcriptionally or post-transcriptionally regulated. This model is combined with a factorial HMM to reconstruct the interactions in a dynamic regulatory network. Using simulated and real data, we show that PTMM outperforms the other two approaches discussed above. Using real data, we also show that PTMM can recover meaningful TF activity levels and identify post-transcriptionally modified TFs, many of which are supported by other sources. Supporting website: www.sb.cs.cmu.edu/PTMM/PTMM. html

Original languageAmerican English
Pages (from-to)1035-1049
Number of pages15
JournalJournal of Computational Biology
Issue number8
StatePublished - 1 Aug 2009


  • Dynamic regulatory networks
  • Machine learning
  • Post-transcriptional modification


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