Reverse engineering gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum

Kuang Lin, Dirk Husmeier, Frank Dondelinger, Claus D. Mayer, Hui Liu, Leighton Pritchard, George P. C. Salmond, Ian K. Toth, Paul R. J. Birch

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

The objective of the project reported in the present chapter was the reverse engineering of gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum from micorarray gene expression profiles, obtained from the wild-type and eight knockout strains. To this end, we have applied various recent methods from multivariate statistics and machine learning: graphical Gaussian models, sparse Bayesian regression, LASSO (least absolute shrinkage and selection operator), Bayesian networks, and nested effects models. We have investigated the degree of similarity between the predictions obtained with the different approaches, and we have assessed the consistency of the reconstructed networks in terms of global topological network properties, based on the node degree distribution. The chapter concludes with a biological evaluation of the predicted network structures.

Original languageEnglish
Title of host publicationComputational Biology
EditorsDavid Fenyö
Place of PublicationNew York
PublisherSpringer
Chapter17
Pages253-281
Number of pages29
ISBN (Print)9781607618416, 9781607618423
DOIs
Publication statusPublished - 23 Sep 2010

Publication series

NameMethods in Molecular Biology
PublisherSpringer Verlag
Volume673
ISSN (Print)1064-3745

Keywords

  • Pectobacterium atrosepticum
  • quorum sensing
  • transposon mutagenesis
  • microarrays
  • graphical Gaussian models
  • sparse Bayesian regression
  • Bayesian networks
  • nested effects models
  • gene ontologies

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