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Quantitative inference of dynamic regulatory pathways via microarray data.

Wen-Chieh Chang1, Chang-Wei Li, Bor-Sen Chen

  • 1Lab. of System Biology, National Tsing Hua University, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan. cwli@moti.ee.nthu.edu.tw <cwli@moti.ee.nthu.edu.tw>

BMC Bioinformatics
|March 8, 2005
PubMed
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This study infers genetic regulatory pathways from gene expression data using dynamic modeling. The method quantitatively identifies gene interactions, regulatory abilities, and activation delays, confirming known pathways and discovering new ones.

Area of Science:

  • Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Cellular signaling pathways are crucial for understanding gene interactions and functional genomics.
  • Gene expression data from microarrays contain dynamic information about intracellular gene relationships.
  • Investigating causal relationships among genes is essential for deciphering complex biological networks.

Purpose of the Study:

  • To develop a dynamic modeling approach for exploring causal relationships among genes in cellular signaling pathways.
  • To quantitatively infer genetic regulatory pathways and gene interactions from gene-expression profiles.
  • To identify regulatory abilities and activation delays of upstream genes.

Main Methods:

  • A second-order dynamic model was developed to describe gene regulatory mechanisms.

Related Experiment Videos

  • Upstream regulatory functions were estimated from gene expression profiles and dynamic models.
  • Iterative tracing of regulatory genes allowed for genome-wide pathway construction.
  • The approach was validated using microarray data with randomized time ordering.

    • Main Results:

    • The method successfully infers genetic regulatory pathways from gene-expression profiles.
    • It can confirm previously suspected pathways and discover novel gene interactions.
    • The approach quantifies regulatory abilities and activation delays of causal genes.

    Conclusions:

    • The developed algorithm effectively reconstructs regulatory pathways using microarray data.
    • Applied to the circadian pathway in Arabidopsis thaliana, it identified known interactions and novel regulations.
    • In yeast, the method accurately detected causal relationships among enzymatic genes in the metabolic shift pathway.