Large-scale inference of the transcriptional regulation of Bacillus subtilis

  • Gupta A
  • Varner J
  • Maranas C
  • 23

    Readers

    Mendeley users who have this article in their library.
  • 16

    Citations

    Citations of this article.

Abstract

This paper addresses the inference of the transcriptional regulatory network of Bacillus subtilis. Two inference approaches, a linear, additive model and a non-linear power-law model, are used to analyze the expression of 747 genes from B. subtilis obtained using Affymetrix GeneChip® arrays under three different experimental conditions. A robustness analysis is introduced for identifying confidence levels for all inferred regulatory connections. Both the linear and non-linear methods produce candidate networks that share a scale-free or a "hub-and-spoke" topology with a small number of global regulator genes influencing the expression of a large number of target genes. The two computational approaches in tandem are able to identify known global regulators with a high level of confidence. The linear model is able to identify the interactions of highly expressed genes, particularly those involved in genetic information processing, energy metabolism and signal transduction. Conversely, the non-linear power-law approach tends to capture development regulation and specific carbon and nitrogen regulatory interactions. © 2004 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Bacillus subtilis
  • Signal transduction
  • Transcriptional regulation

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Costas MaranasThe Pennsylvania State University, University Park, Pennsylvania

    Follow
  • Anshuman Gupta

  • Jeffrey D. Varner

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free