Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Estimating dynamic models for gene regulation networks.

Jiguo Cao1, Hongyu Zhao

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada.

Bioinformatics (Oxford, England)
|May 29, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Understanding the determinants of public trust in the health care system in China: an analysis of a cross-sectional survey.

Journal of health services research & policy·2018
Same author

Adverse Childhood Experiences, Epigenetic Measures, and Obesity in Youth.

The Journal of pediatrics·2018
Same author

Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome.

Biometrics·2018
Same author

LncRNA UCA1 sponges miR-204-5p to promote migration, invasion and epithelial-mesenchymal transition of glioma cells via upregulation of ZEB1.

Pathology, research and practice·2018
Same author

International variations in trust in health care systems.

The International journal of health planning and management·2018
Same author

Toll-like receptor 9 negatively regulates pancreatic islet beta cell growth and function in a mouse model of type 1 diabetes.

Diabetologia·2018
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

This study presents a new method for estimating parameters in gene regulatory network models using ordinary differential equations (ODEs) and noisy gene expression data. The approach accurately models network dynamics and identifies gene regulatory networks.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Modeling gene regulation networks is crucial for understanding biological dynamics.
  • Network structure alone is insufficient; dynamic modeling using ordinary differential equations (ODEs) is necessary.
  • Parameter inference from observed data is essential due to unknown ODE parameters.

Purpose of the Study:

  • To introduce a generalized profiling method for estimating ODE parameters in gene regulatory networks.
  • To address the challenge of noisy microarray gene expression data.
  • To develop a method for identifying gene regulatory networks.

Main Methods:

  • Utilized the generalized profiling method for parameter estimation.
  • Applied penalized smoothing techniques to approximate ODE solutions efficiently.

Related Experiment Videos

  • Developed a goodness-of-fit test for dynamic models.

    • Main Results:

    • The generalized profiling method accurately estimates ODE parameters from noisy gene expression data.
    • Penalized smoothing provided fast and stable ODE solution approximations.
    • The developed ODE solutions with estimated parameters showed good data fit.
    • A goodness-of-fit test was successfully developed to identify gene regulatory networks.

    Conclusions:

    • The generalized profiling method is effective for parameter estimation in gene regulatory networks.
    • The combination of penalized smoothing and generalized profiling offers an efficient approach to model gene regulation dynamics.
    • The developed goodness-of-fit test aids in identifying regulatory network structures from dynamic data.