This article is part of the supplement: The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07)
Selection of thermodynamic models for combinatorial control of multiple transcription factors in early differentiation of embryonic stem cells
1 Department of Bioengineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
2 Department of Computer Science, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
3 Department of Plant Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
4 Department of Statistics, University of Illinois at Urbana Champaign, Champaign, IL 61820, USA
5 Institute of Genomic Biology, University of Illinois at Urbana Champaign, IL 61801, USA
Citation and License
BMC Genomics 2008, 9(Suppl 1):S18 doi:10.1186/1471-2164-9-S1-S18Published: 20 March 2008
Transcription factors (TFs) have multiple combinatorial forms to regulate the transcription of a target gene. For example, one TF can help another TF to stabilize onto regulatory DNA sequence and the other TF may attract RNA polymerase (RNAP) to start transcription; alternatively, two TFs may both interact with both the DNA sequence and the RNAP. The different forms of TF-TF interaction have different effects on the probability of RNAP's binding onto the promoter sequence and therefore confer different transcriptional efficiencies.
We have developed an analytical method to identify the thermodynamic model that best describes the form of TF-TF interaction among a set of TF interactions for every target gene. In this method, time-course microarray data are used to estimate the steady state concentration of the transcript of a target gene, as well as the relative changes of the active concentration for each TF. These estimated concentrations and changes of concentrations are fed into an inference scheme to identify the most compatible thermodynamic model. Such a model represents a particular way of combinatorial control by multiple TFs on a target gene.
Applying this approach to a time-course microarray dataset of embryonic stem cells, we have inferred five interaction patterns among three regulators, Oct4, Sox2 and Nanog, on ten target genes.