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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Evolution of New Traits in Microbes01:24

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...

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Related Experiment Video

Updated: Jul 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Inferring gene regulatory networks using differential evolution with local search heuristics.

Nasimul Noman1, Hitoshi Iba

  • 1Iba Laboratory, Graduate School of Frontier Sceinces, University of Tokyo, Tokyo, Japan. noman@iba.k.u-tokyo.ac.jp

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 3, 2007
PubMed
Summary

This study introduces a novel memetic algorithm for gene network inference, utilizing information criteria for improved accuracy in reconstructing biomolecular interactions and kinetic parameters from gene expression data.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Inferring gene regulatory networks is crucial for understanding cellular mechanisms.
  • Traditional methods often struggle with accuracy and parameter estimation.

Purpose of the Study:

  • To develop an advanced memetic algorithm for gene network structure evolution and parameter inference.
  • To enhance gene network model selection using Information Criteria over Mean Squared Error.

Main Methods:

  • A memetic algorithm incorporating hill-climbing local search was employed.
  • Decoupled S-system formalism was used for biomolecular interaction modeling.
  • Information Criteria replaced Mean Squared Error for fitness evaluation.

Main Results:

  • The algorithm accurately inferred gene network topology and regulatory parameters across various experimental conditions.
  • Performance was sensitive to data quantity and noise levels.
  • The Information Criteria-based fitness function outperformed conventional methods in accuracy.

Conclusions:

  • The proposed memetic algorithm offers a robust approach for gene network reconstruction.
  • This method enhances the identification of network topology and parameter estimation.
  • Applied to yeast cell-cycle data, it successfully reconstructed key regulatory networks.