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Related Concept Videos

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...
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...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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Related Experiment Video

Updated: May 22, 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 robust gene networks from expression data by a sensitivity-based incremental evolution method.

Yu-Ting Hsiao1, Wei-Po Lee

  • 1Department of Information Management, National Sun Yat-sen University, 70, Lienhai Road, Kaohsiung, Taiwan.

BMC Bioinformatics
|May 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an incremental evolution approach to reconstruct gene regulatory networks (GRNs). The method enhances network robustness and optimizes parameters for accurate system behavior inference.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Reconstructing gene regulatory networks (GRNs) from expression data is a key challenge in systems biology.
  • Existing computational methods for GRN reconstruction face difficulties in inferring robust networks with specific behaviors.
  • Network dynamics and modeling approaches have not been fully explored for GRN reconstruction.

Purpose of the Study:

  • To develop an incremental evolution approach for inferring gene regulatory networks (GRNs).
  • To incorporate network robustness and handle a large number of network parameters in the reconstruction process.
  • To evaluate the effectiveness of the proposed approach in inferring robust GRNs with desired system behaviors.

Main Methods:

  • An incremental evolution approach integrating sensitivity analysis and swarm intelligence for parameter optimization.
  • Sensitivity analysis to iteratively identify influential network parameters.
  • Swarm intelligence for efficient parameter optimization.

Main Results:

  • The proposed approach effectively infers gene regulatory networks (GRNs) that exhibit desired system behaviors.
  • Experimental evaluations confirm the robustness and effectiveness of the inferred networks.
  • The method successfully handles a large number of network parameters.

Conclusions:

  • Sensitivity analysis is vital for identifying critical parameters that influence network dynamics and for setting constraints in GRN reconstruction.
  • The developed approach successfully infers robust GRNs with predictable system behaviors.
  • This method advances the field of systems biology by providing a robust approach to GRN reconstruction.