A Combined Expression-Interaction Model for Inferring the Temporal Activity of Transcription Factors
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 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 fully 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.
KeywordsKalman Filter Recall Rate Dynamic Bayesian Network Hide Markov Chain Time Series Expression
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