title: Reconstructing transcription factor activities in hierarchical transcription network motifs.
abstract: BACKGROUND
  A knowledge of the dynamics of transcription factors is fundamental to understand the transcriptional regulation mechanism. Nowadays, an experimental measure of transcription factor activities in vivo represents a challenge. Several methods have been developed to infer these activities from easily measurable quantities such as mRNA expression of target genes. A limitation of these methods is represented by the fact that they rely on very simple single-layer structures, typically consisting of one or more transcription factors regulating a number of target genes.
  
  
  RESULTS
  We present a novel statistical inference methodology to reverse engineer the dynamics of transcription factors in hierarchical network motifs such as feed-forward loops. The approach we present is based on a continuous time representation of the system where the high-level master transcription factor is represented as a two state Markov jump process driving a system of differential equations. We solve the inference problem using an efficient variational approach and demonstrate our method on simulated data and two real datasets. The results on real data show that the predictions of our approach can capture biological behaviours in a more effective way than single-layer models of transcription, and can lead to novel biological insights.
  
  
  BACKGROUND
  http://homepages.inf.ed.ac.uk/gsanguin/software.html
  
  
  BACKGROUND
  g.sanguinetti@ed.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
authors: Andrea
authors: A
authors: Ocone
authors: Guido
authors: G
authors: Sanguinetti
mesh_terms: D045744:Cell Line, Tumor; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D008390:Markov Chains; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors