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Related Experiment Videos

Continuous-time identification of gene expression models.

Daniel E Zak1, Ronald K Pearson, Rajanikanth Vadigepalli

  • 1Department of Chemical Engineering, University of Delaware, Newark, Delaware, USA.

Omics : a Journal of Integrative Biology
|December 20, 2003
PubMed
Summary
This summary is machine-generated.

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This study introduces a continuous-time approach for building gene expression models using ordinary differential equations (ODEs), improving upon discrete-time methods for systems biology. The new method accurately identifies model parameters from microarray data, even with noise.

Area of Science:

  • Systems Biology
  • Molecular Biology
  • Computational Biology

Background:

  • Systems biology aims to model transcriptional regulation networks for cellular processes.
  • Microarray data is commonly used to infer gene regulatory networks.
  • Existing discrete-time models have limitations and hinder integration with ODE-based models.

Purpose of the Study:

  • Develop a continuous-time approach for identifying gene expression models based on ordinary differential equations (ODEs).
  • Overcome limitations of discrete-time models in systems biology.
  • Facilitate integration of gene expression models with other biological process models.

Main Methods:

  • Utilized the modulating functions method for parameter identification.
  • Applied the continuous-time approach to simulated gene expression systems.

Related Experiment Videos

  • Tested on linear, autoregulatory, and nonlinear transcriptional network models.

    • Main Results:

    • The continuous-time approach accurately identified parameters in simulated gene expression models.
    • The method performed well with limited data samples and moderate experimental noise.
    • Case studies provided insights into gene expression modeling.

    Conclusions:

    • The developed continuous-time approach is effective for identifying gene expression dynamics.
    • This method enhances the predictive power of systems biology models.
    • It offers a robust alternative to discrete-time models for analyzing gene expression data.