Large-scale inference and graph-theoretical analysis of gene-regulatory networks in B. Subtilis

Claire Christensen, Anshuman Gupta, Costas D. Maranas, Réka Albert

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph-theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [A. Gupta, J.D. Varner, C.D. Maranas, Comput. Chem. Eng. 29 (2005) 565], we are able to identify trends in graph-theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.

Original languageEnglish (US)
Pages (from-to)796-810
Number of pages15
JournalPhysica A: Statistical Mechanics and its Applications
Volume373
DOIs
StatePublished - Jan 1 2007

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics

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