title: Modelling the influence of dimerisation sequence dissimilarities on the auxin signalling network.
abstract: BACKGROUND
  Auxin is a major phytohormone involved in many developmental processes by controlling gene expression through a network of transcriptional regulators. In Arabidopsis thaliana, the auxin signalling network is made of 52 potentially interacting transcriptional regulators, activating or repressing gene expression. All the possible interactions were tested in two-way yeast-2-hybrid experiments. Our objective was to characterise this auxin signalling network and to quantify the influence of the dimerisation sequence dissimilarities on the interaction between transcriptional regulators.
  
  
  RESULTS
  We applied model-based graph clustering methods relying on connectivity profiles between transcriptional regulators. Incorporating dimerisation sequence dissimilarities as explanatory variables, we modelled their influence on the auxin network topology using mixture of linear models for random graphs. Our results provide evidence that the network can be simplified into four groups, three of them being closely related to biological groups. We found that these groups behave differently, depending on their dimerisation sequence dissimilarities, and that the two dimerisation sub-domains might play different roles.
  
  
  CONCLUSIONS
  We propose here the first pipeline of statistical methods combining yeast-2-hybrid data and protein sequence dissimilarities for analysing protein-protein interactions. We unveil using this pipeline of analysis the transcriptional regulator interaction modes.
authors.affiliation: Laboratoire de Reproduction et Développement des Plantes, CNRS, INRA, ENS Lyon, UCBL, Université de Lyon, Lyon, 69364, France. jonathan.legrand@ens-lyon.fr.|CIRAD, UMR AGAP and Inria, Virtual Plants, Montpellier, 34095, France. jonathan.legrand@ens-lyon.fr.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Legrand
authors.affiliation: Mathématiques et Informatique Appliquées, AgroParisTech/INRA, Paris, 75231, France. jbleger@bordeaux.inra.fr.
authors.forename: Jean-Benoist
authors.initials: JB
authors.lastname: Léger
authors.affiliation: Mathématiques et Informatique Appliquées, AgroParisTech/INRA, Paris, 75231, France. stephane.robin@agroparistech.fr.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Robin
authors.affiliation: Laboratoire de Reproduction et Développement des Plantes, CNRS, INRA, ENS Lyon, UCBL, Université de Lyon, Lyon, 69364, France. teva.vernoux@ens-lyon.fr.
authors.forename: Teva
authors.initials: T
authors.lastname: Vernoux
authors.affiliation: CIRAD, UMR AGAP and Inria, Virtual Plants, Montpellier, 34095, France. yann.guedon@cirad.fr.
authors.forename: Yann
authors.initials: Y
authors.lastname: Guédon
mesh_terms: D017360:Arabidopsis / Q000166:cytology* / Q000235:genetics / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000737:chemistry* / Q000378:metabolism*; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D007210:Indoleacetic Acids / Q000378:metabolism*; D008954:Models, Biological; D016011:Normal Distribution; D055503:Protein Multimerization; D020836:Protein Structure, Quaternary; D015398:Signal Transduction; D014158:Transcription, Genetic
chemical_list: D029681:Arabidopsis Proteins; D007210:Indoleacetic Acids