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Regulation of Expression at Multiple Steps01:23

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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Uncovering gene regulatory networks from time-series microarray data with variational Bayesian structural expectation

Isabel Tienda Luna1, Yufei Huang, Yufang Yin

  • 1Department of Applied Physics, University of Granada, Granada, Spain.

EURASIP Journal on Bioinformatics & Systems Biology
|March 1, 2008
PubMed
Summary

This study introduces a novel variational Bayesian structural expectation maximization (VBSEM) algorithm for reverse engineering gene regulatory networks. The VBSEM algorithm infers network topology and parameters from time-series microarray data, enabling robust biological network analysis.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene regulatory networks (GRNs) control cellular functions.
  • Inferring GRNs from time-series microarray data is crucial for understanding biological processes like the cell cycle.
  • Existing methods often lack robust mechanisms for quantifying network topology uncertainty.

Purpose of the Study:

  • To develop a novel algorithm for reverse engineering gene regulatory networks from time-series microarray data.
  • To model cell cycle regulations using dynamic Bayesian networks (DBNs).
  • To provide a posteriori probabilities (APP) for network topology and enable Bayesian data integration.

Main Methods:

  • Application of dynamic Bayesian networks (DBNs) for modeling.
  • Development of a variational Bayesian structural expectation maximization (VBSEM) algorithm.
  • Utilizing a moving block bootstrap method for confidence evaluation.
  • Validation against the KEGG pathway map.

Main Results:

  • The VBSEM algorithm successfully learns the posterior distribution of network parameters and topology.
  • The inferred network topology provides a posteriori probabilities (APP).
  • Demonstrated utility of APPs in a Bayesian data integration strategy for combining microarray datasets.
  • Successful application and validation on yeast cell cycle data.

Conclusions:

  • The proposed VBSEM algorithm offers a robust approach for gene regulatory network inference from time-series data.
  • The method provides a posteriori probabilities for network topology, enhancing confidence in inferred networks.
  • The VBSEM algorithm facilitates Bayesian data integration, improving the analysis of multiple biological datasets.