Reconstructing genetic networks from time ordered gene expression data using Bayesian method with global search algorithm

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8 Scopus citations

Abstract

Different genes of an organism are expressed to different levels at different times during the life cycle and in response to various environmental stresses. Elucidating the network of gene-gene interactions responsible for the expression helps understand living processes. Microarray technology allows concurrent genomic scale measurement of an organism's mRNA levels. We describe a power-law formalism to model the combinatorial effect of regulators on gene transcription. The dynamic model allows delayed transcription. We employ a principled network reconstruction approach that accounts for the high noise and low replicate characteristics of present day microarray data. An important feature of our approach is that the detail of the reconstructed network is limited to the noise level of the data. We apply the methodology to a microarray dataset of yeast cells grown in glucose and experiencing a diauxic transition upon glucose depletion. The reconstructed transcriptional regulations of yeast glycolytic genes are consistent with published findings.

Original languageEnglish
Pages (from-to)441-458
Number of pages18
JournalJournal of Bioinformatics and Computational Biology
Volume2
Issue number3
DOIs
StatePublished - Sep 2004

Keywords

  • Genetic algorithm
  • Glycolysis
  • Power-law
  • Transcription

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