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

Bayesian error analysis model for reconstructing transcriptional regulatory networks.

Ning Sun1, Raymond J Carroll, Hongyu Zhao

  • 1Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA.

Proceedings of the National Academy of Sciences of the United States of America
|May 17, 2006
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

This study introduces a Bayesian model to reconstruct transcriptional regulatory networks by integrating protein-DNA binding and gene expression data, accounting for measurement errors to improve understanding of gene regulation.

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Transcription regulation is crucial but complex, with experimental limitations hindering full understanding.
  • High-throughput technologies generate vast genomic data (DNA sequences, protein-DNA binding, gene expression) valuable for studying gene regulation.
  • Existing models often struggle with the inherent complexity and data limitations in transcription regulation.

Purpose of the Study:

  • To develop a novel Bayesian error analysis model for reconstructing transcriptional regulatory networks.
  • To integrate diverse genomic data, specifically protein-DNA binding and gene expression data.
  • To address limitations in current understanding by explicitly modeling measurement errors.

Main Methods:

  • A Bayesian hierarchical model framework is proposed.

Related Experiment Videos

  • Transcription is modeled as biochemical reactions using a linear system model.
  • Measurement errors in protein-DNA binding and gene expression data are explicitly incorporated.
  • Markov chain Monte Carlo methods are used for parameter inference.

    • Main Results:

    • The proposed model successfully integrates protein-DNA binding and gene expression data.
    • The model explicitly accounts for measurement errors in both data types.
    • Application to yeast cell cycle data demonstrates effective reconstruction of transcriptional regulatory networks.

    Conclusions:

    • The Bayesian error analysis model provides a robust framework for inferring transcriptional regulatory networks.
    • Explicitly modeling measurement errors enhances the accuracy of network reconstruction.
    • This approach offers valuable insights into complex gene regulatory mechanisms, as shown in the yeast cell cycle.