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Related Concept Videos

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...
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...
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...
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...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

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Related Experiment Video

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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

Comparison of evolutionary algorithms in gene regulatory network model inference.

Alina Sîrbu1, Heather J Ruskin, Martin Crane

  • 1Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland. asirbu@computing.dcu.ie

BMC Bioinformatics
|January 29, 2010
PubMed
Summary

This study compares evolutionary algorithms for gene regulatory network (GRN) modeling using gene expression data. It identifies promising methods for robust, scalable quantitative analysis of noisy biological datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput gene expression technologies enable the inference of gene regulatory networks (GRNs).
  • Existing methods struggle with quantitative analysis of noisy and insufficient real-world microarray data.
  • Difficulty in reverse engineering GRNs hinders large-scale biological data analysis.

Purpose of the Study:

  • To analyze and compare evolutionary algorithms for quantitative GRN modeling.
  • To evaluate algorithms on both synthetic and real gene expression data.
  • To establish a framework for assessing algorithm performance.

Main Methods:

  • Analysis of existing evolutionary algorithms for GRN modeling.
  • Application of algorithms to synthetic and real DNA microarray gene expression data.
  • Assessment of biological behavior reproduction, scalability, and robustness to noise.

Main Results:

  • A comprehensive comparison of evolutionary algorithms under a common framework.
  • Evaluation of algorithm performance on noisy and insufficient biological datasets.
  • Identification of algorithms suitable for quantitative GRN inference.

Conclusions:

  • A comparison framework for evolutionary algorithms in GRN inference is presented.
  • Promising methods for quantitative GRN modeling were identified.
  • A platform for developing advanced GRN model formalisms has been established.