Selection of thermodynamic models for combinatorial control of multiple transcription factors in early differentiation of embryonic stem cells

Abstract

Background: 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.

The PLETHORA Gene Regulatory Network Guides Growth and Cell Differentiation in Arabidopsis Roots

“…Both options can be tested through binding site mutation and its effect on gene regulation. The observed region-specific target gene activation by PLTs may be achieved by different threshold activation levels mediated by sequence context or position of binding sites (Chen et al, 2008;Levo et al, 2015), by cooperative interactions with tissue-specific transcription factors (Jolma et al, 2015), by indirect effects such as the local production and directed transport of auxin (this study), and by regulation of transcription factors implicated in growth and differentiation of specific tissues.…”

Analysis of Combinatorial Gene Regulation with Thermodynamic Models

“…The first component is Interaction-Identifier [15], which is developed to identify a thermodynamic model that best describes the form of TF-TF interaction among a set of TFs for every target gene. The second component is Network Identifier [14], which enables to further infer gene regulatory networks from multiple time course gene expression data, based on Interaction-Identifier.…”

“…However, the bridge connecting from the binding probability of RNAP to the gene expression levels is still missing. Thus, we further use a kinetic model to analyze the dynamics of gene expression over times [15].…”

“…Thus, based on the principle of thermodynamic models that the transcription rate is proportional to the binding probability of RNAP, an ordinary differential equation was proposed to mimic the dynamics of gene expressions in the following [15].…”

“…and W.H.W). We restrict the analysis to the regulatory relationships among 747 genes that are annotated by Gene Ontology term, Transcription The identified regulatory network among Oct4, Sox2 and Nanog [15]. Apply the Interaction-identification algorithm to the expression data of Oct4, Sox2, and Nanog from time course microarray data.…”