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Reverse Engineering Galactose Regulation in Yeast through Model Selection

Author

Listed:
  • Thorsson Vesteinn

    (Institute for Systems Biology, first two authors contributed equally)

  • Hörnquist Michael

    (Linköping University, first two authors contributed equally)

  • Siegel Andrew F

    (University of Washington)

  • Hood Leroy

    (Institute for Systems Biology)

Abstract

We examine the application of statistical model selection methods to reverse-engineering the control of galactose utilization in yeast from DNA microarray experiment data. In these experiments, relationships among gene expression values are revealed through modifications of galactose sugar level and genetic perturbations through knockouts. For each gene variable, we select predictors using a variety of methods, taking into account the variance in each measurement. These methods include maximization of log-likelihood with Cp, AIC, and BIC penalties, bootstrap and cross-validation error estimation, and coefficient shrinkage via the Lasso.

Suggested Citation

  • Thorsson Vesteinn & Hörnquist Michael & Siegel Andrew F & Hood Leroy, 2005. "Reverse Engineering Galactose Regulation in Yeast through Model Selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-24, September.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:28
    DOI: 10.2202/1544-6115.1118
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