title: An estimation method for a cellular-state-specific gene regulatory network along tree-structured gene expression profiles.
abstract: BACKGROUND
  Identifying the differences between gene regulatory networks under varying biological conditions or external stimuli is an important challenge in systems biology. Several methods have been developed to reverse-engineer a cellular system, called a gene regulatory network, from gene expression profiles in order to understand transcriptomic behavior under various conditions of interest. Conventional methods infer the gene regulatory network independently from each of the multiple gene expression profiles under varying conditions to find the important regulatory relations for understanding cellular behavior. However, the inferred networks with conventional methods include a large number of misleading relations, and the accuracy of the inference is low. This is because conventional methods do not consider other related conditions, and the results of conventional methods include considerable noise due to the limited number of observation points in each expression profile of interest.
  
  
  RESULTS
  We propose a more accurate method for estimating key gene regulatory networks for understanding cellular behavior under various conditions. Our method utilizes multiple gene expression profiles that compose a tree structure under varying conditions. The root represents the original cellular state, and the leaves represent the changed cellular states under various conditions. By using this tree-structured gene expression profiles, our method more powerfully estimates the networks that are key to understanding the cellular behavior of interest under varying conditions.
  
  
  CONCLUSIONS
  We confirmed that the proposed method in cell differentiation was more rigorous than the conventional method. The results show that our assumptions as to which relations are unimportant for understanding the differences of cellular states in cell differentiation are appropriate, and that our method can infer more accurately the core networks of the cell types.
authors.affiliation: Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka 565-0871, Japan.
authors.forename: Ryo
authors.initials: R
authors.lastname: Araki
authors.forename: Shigeto
authors.initials: S
authors.lastname: Seno
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Takenaka
authors.forename: Hideo
authors.initials: H
authors.lastname: Matsuda
mesh_terms: D017667:Adipocytes / Q000166:cytology / Q000502:physiology; D000465:Algorithms; D000818:Animals; D002454:Cell Differentiation / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D059630:Mesenchymal Stem Cells / Q000166:cytology / Q000502:physiology; D051379:Mice; D008957:Models, Genetic; D010006:Osteoblasts / Q000166:cytology / Q000502:physiology; D059467:Transcriptome