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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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
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...

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Related Experiment Video

Updated: Jul 10, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles.

Shinn-Ying Ho, Chih-Hung Hsieh, Fu-Chieh Yu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 3, 2007
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer S-system models of genetic regulatory networks from gene expression data. The novel approach effectively reconstructs complex biological pathways, overcoming computational challenges.

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    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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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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    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
    07:09

    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

    Published on: May 28, 2021

    Area of Science:

    • Computational Biology
    • Systems Biology
    • Bioinformatics

    Background:

    • Reconstructing cellular dynamic regulatory networks from gene expression profiles is crucial for understanding molecular biology and pharmaceutics.
    • S-system models are effective for characterizing biochemical networks and analyzing regulatory system dynamics.
    • Inferring S-system models for N-gene networks involves optimizing a large number of parameters (2N(N+1)) in non-linear differential equations.

    Purpose of the Study:

    • To propose an intelligent two-stage evolutionary algorithm (iTEA) for efficient inference of S-system models from gene expression time-series data.
    • To address the curse of dimensionality in inferring complex genetic network models.

    Main Methods:

    • Developed a two-stage evolutionary algorithm (iTEA) employing a divide-and-conquer strategy to decompose the optimization problem.
    • Stage 1: Utilized a novel intelligent genetic algorithm (IGA) with orthogonal experimental design (OED)-based crossover to solve decomposed subproblems.
    • Stage 2: Refined solutions using an OED-based simulated annealing algorithm to handle noisy gene expression data.

    Main Results:

    • The intelligent genetic algorithm (IGA) demonstrated efficiency in solving subproblems.
    • IGA significantly outperformed the existing SPXGA method in performance.
    • The iTEA effectively inferred S-system models, proving valuable for dynamic pathway identification.

    Conclusions:

    • The proposed iTEA is an efficient and effective method for inferring S-system models of genetic networks from gene expression data.
    • iTEA successfully addresses the high dimensionality challenge in model inference.
    • This approach facilitates a deeper understanding of cellular regulatory mechanisms and aids in pharmaceutical applications.