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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Using gene expression programming to infer gene regulatory networks from time-series data.

Yongqing Zhang1, Yifei Pu, Haisen Zhang

  • 1College of Computer Science, Sichuan University, Chengdu 610065, PR China.

Computational Biology and Chemistry
|October 22, 2013
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This study infers gene regulatory networks using an evolutionary model and time-series data. The proposed method enhances prediction accuracy for gene expression data.

Keywords:
Gene expression programmingGene regulatory networksLeast mean squareOrdinary differential equation

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene regulatory network inference is crucial in systems biology.
  • Accurate inference requires robust models for time-series gene expression data.
  • Existing methods face challenges with non-linear dynamics and noise.

Purpose of the Study:

  • To develop and validate an evolutionary model for inferring gene regulatory networks.
  • To improve the prediction accuracy of time-series microarray data.
  • To construct a gene regulatory network for 12 Yeast genes.

Main Methods:

  • Utilized a non-linear differential equation model.
  • Applied Gene Expression Programming (GEP) for model structure identification.
  • Employed Least Mean Square (LMS) optimization for ordinary differential equation (ODE) parameters.

Main Results:

  • Successfully inferred gene regulatory networks from both synthetic and real gene expression datasets.
  • Demonstrated improved prediction accuracy on noisy and noise-free time-series data.
  • Constructed a functional gene regulatory network involving 12 Yeast genes.

Conclusions:

  • The proposed evolutionary model effectively infers gene regulatory networks.
  • The method enhances the accuracy of microarray time-series data analysis.
  • This approach offers a valuable tool for systems biology research.