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Updated: Oct 19, 2025

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Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model.

Bin Yang1, Wenzheng Bao2, Wei Zhang1

  • 1School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China.

BMC Bioinformatics
|September 21, 2021
PubMed
Summary
This summary is machine-generated.

A new complex-valued ordinary differential equation (CVODE) model improves gene expression prediction by 20-50%. This advanced model enhances the accuracy of gene regulatory network identification, uncovering more true regulatory relationships.

Keywords:
Complex-valued ordinary differential equationFirefly algorithmGene regulatory networkGrammar-guided genetic programming

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Molecular biology research highlights complex genomic interactions.
  • Understanding gene and protein interactions is crucial for deciphering biological mechanisms.
  • Identifying gene regulatory networks is a key research area.

Purpose of the Study:

  • To forecast gene expression data more accurately.
  • To identify more precise gene regulatory networks.
  • To introduce an optimized complex-valued ordinary differential equation (CVODE) model.

Main Methods:

  • Development of a complex-valued ordinary differential equation (CVODE) model.
  • Optimization of the CVODE model using a complex-valued hybrid evolutionary method.
  • Application of Grammar-guided genetic programming and a complex-valued firefly algorithm for optimization.

Main Results:

  • The CVODE model demonstrated a 20-50% improvement in gene expression data prediction accuracy.
  • The model successfully inferred more true-positive regulatory relationships compared to standard ordinary differential equations.
  • The CVODE model reduced the number of false-positive regulations.

Conclusions:

  • The proposed CVODE model significantly enhances the prediction accuracy of gene expression data.
  • This approach provides a more reliable method for inferring gene regulatory relationships.
  • Experimental validation on E. coli and Human Cell datasets confirms the model's effectiveness.