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Microarray Analysis for Saccharomyces cerevisiae
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Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces

Kam D Dahlquist1, Ben G Fitzpatrick2, Erika T Camacho3

  • 1Department of Biology, Loyola Marymount University, 1 LMU Drive, MS 8888, Los Angeles, CA, 90045, USA. kdahlquist@lmu.edu.

Bulletin of Mathematical Biology
|October 1, 2015
PubMed
Summary

This study models the yeast cold shock gene regulatory network using ordinary differential equations. The model accurately predicts experimental data, revealing key transcription factor interactions and highlighting important regulators of the early cold shock response.

Keywords:
Dynamic network modelPenalized least squares

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Area of Science:

  • Systems biology
  • Molecular biology
  • Computational biology

Background:

  • Gene regulatory networks (GRNs) control cellular responses to environmental stimuli.
  • The cold shock response in Saccharomyces cerevisiae involves complex transcriptional regulation.
  • Understanding GRN dynamics is crucial for deciphering cellular adaptation mechanisms.

Purpose of the Study:

  • To investigate the dynamics of a gene regulatory network governing the cold shock response in budding yeast.
  • To develop and validate a mathematical model for predicting GRN behavior.
  • To identify key transcription factors and regulatory relationships involved in yeast cold shock adaptation.

Main Methods:

  • Modeled a 21-transcription factor network using mass balance ordinary differential equations with sigmoidal production functions.
  • Employed a penalized nonlinear least squares approach to fit the model parameters to published microarray data.
  • Performed sensitivity analysis to identify critical network components and parameters.

Main Results:

  • Model predictions showed good agreement with experimental microarray data (within 95% confidence intervals).
  • Identified specific activation and repression relationships among transcription factors.
  • Sensitivity analysis highlighted Yap1, Rox1, and Yap6 as central to the network's core.
  • Newly suggested roles for Rap1, Fhl1, Msn4, Rph1, and Hsf1 in the early cold shock response.

Conclusions:

  • Successfully estimated numerous parameters for a nonlinear dynamic GRN using sparse, noisy microarray data.
  • The developed model provides a robust framework for understanding yeast cold shock response dynamics.
  • The findings offer new insights into the regulatory logic underlying yeast adaptation to cold stress.