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State space modeling of yeast gene expression dynamics.

Olli Haavisto1, Heikki Hyötyniemi, Christophe Roos

  • 1Control Engineering Laboratory, Helsinki University of Technology, PO Box 5500, FI-02015 TKK, Finland. olli.haavisto@tkk.fi

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This study introduces a subspace identification method for modeling complex gene expression networks in yeast. The approach effectively captures genome-wide stress responses, advancing systems biology insights.

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Area of Science:

  • Systems Biology
  • Genomics
  • Computational Biology

Background:

  • Gene interactions form cellular functional systems, making gene expression network modeling crucial.
  • High-dimensional and complex gene expression data pose significant challenges for traditional modeling methods, especially for dynamic models.

Purpose of the Study:

  • To develop and apply a robust data-based modeling approach for high-dimensional gene expression networks.
  • To address the underdetermined case in system identification with limited data samples.

Main Methods:

  • Utilized a subspace identification approach, including a modified version for underdetermined systems.
  • Applied the algorithm to public stress-response datasets from yeast (Saccharomyces cerevisiae).
  • Validated the dynamic state-space model by comparing simulation results with experimental data.

Main Results:

  • The identified dynamic state-space model accurately describes genome-wide stress-related gene expression changes in yeast.
  • The subspace identification method proved effective for high-dimensional biological systems.
  • The model demonstrated a good fit for the dynamics of yeast stress responses.

Conclusions:

  • Subspace identification is a suitable method for modeling complex, high-dimensional gene expression dynamics.
  • While effective, precise whole-genome dynamic modeling necessitates specifically designed experiments.
  • The study advances the understanding of cellular systems biology through advanced computational modeling.