Statistical Reconstruction of Transcription Factor Activity Using Michaelis–Menten Kinetics

R. Khanin, V. Vinciotti, V. Mersinias, C.P. Smith, Ernst Wit

Research output: Contribution to journalArticleAcademic

23 Citations (Scopus)

Abstract

The basic building block of a gene regulatory network consists of a gene encoding a transcription factor (TF) and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine TFs and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the TF provides only limited information about the activity of the TF, which can also be controlled posttranscriptionally. In the absence of a reliable technology to routinely measure the activity of regulators, it is of great importance to understand whether this activity can be inferred from gene expression data. We here develop a statistical framework to reconstruct the activity of a TF from gene expression data of the target genes in its regulatory module. The novelty of our approach is that we embed the deterministic Michaelis–Menten model of gene regulation in this statistical framework. The kinetic parameters of the gene regulation model are inferred together with the profile of the TF regulator. We also obtain a goodness-of-fit test to verify the fit of the model. The model is applied to a time series involving the Streptomyces coelicolor bacterium. We focus on the transcriptional activator cdaR, which is partly responsible for the production of a particular type of antibiotic. The aim is to reconstruct the activity profile of this regulator. Our approach can be extended to include more complex regulatory relationships, such as multiple regulatory factors, competition, and cooperativity.
Original languageEnglish
Pages (from-to)816-823
Number of pages8
JournalBiometrics
Volume63
Publication statusPublished - Sep-2007

Keywords

  • Streptomyces coelicolor
  • Michaelis–Menten kinetics
  • Maximum likelihood estimation
  • Gene regulation

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