title: A stochastic differential equation model for transcriptional regulatory networks.
abstract: BACKGROUND
  This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution.
  
  
  RESULTS
  We show that a beta sigmoid function that keeps track of temporal parameters is a novel prototype of a regulatory function, with the effect of improving the performance of the profile prediction. The stochastic differential equation model follows well the dynamic of the gene expression levels.
  
  
  CONCLUSIONS
  When adapted to biological hypotheses and combined with a promoter analysis, the method proposed here leads to improved models of the transcriptional regulatory networks.
authors.affiliation: Laboratoire Biologie, Informatique, Mathématiques, Institute de Recherche en Technologies et Sciences pour le Vivant CEA, Grenoble, France. adriana.climescu@cea.fr
authors.forename: Adriana
authors.initials: A
authors.lastname: Climescu-Haulica
authors.forename: Michelle D
authors.initials: MD
authors.lastname: Quirk
mesh_terms: D015870:Gene Expression; D053263:Gene Regulatory Networks; D008954:Models, Biological; D012333:RNA, Messenger / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D013269:Stochastic Processes; D013997:Time Factors
chemical_list: D012333:RNA, Messenger