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Related Concept Videos

Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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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...
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Related Experiment Video

Updated: May 18, 2026

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Architecture of the human regulatory network derived from ENCODE data.

Mark B Gerstein1,2,3, Anshul Kundaje4, Manoj Hariharan5

  • 1Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA.

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|September 8, 2012
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Summary

This study maps human transcription factor networks, revealing context-specific binding and hierarchical organization. These insights into gene regulation are key for understanding human biology and disease.

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Area of Science:

  • Genomics
  • Systems Biology
  • Molecular Biology

Background:

  • Gene regulation relies on transcription factors binding DNA in specific combinations.
  • Understanding the human transcriptional regulatory network is crucial for deciphering cellular function and disease.

Purpose of the Study:

  • To map the genomic binding of 119 human transcription factors.
  • To analyze the combinatorial binding patterns and network properties of these factors.

Main Methods:

  • Conducted over 450 experiments to determine transcription factor genomic binding information.
  • Organized binding data into a hierarchical network and integrated it with other genomic data, like microRNA regulation.

Main Results:

  • Identified context-specific combinatorial binding of transcription factors, differing near and far from genes.
  • Revealed a hierarchical network structure where top-level factors influence expression and middle-level factors mitigate bottlenecks.
  • Discovered enriched network motifs, such as noise-buffering feed-forward loops.
  • Found that highly connected network components are under stronger selection and show allele-specific activity.

Conclusions:

  • The human transcriptional regulatory network exhibits complex, context-dependent organization.
  • This detailed regulatory map provides a foundation for interpreting personal genomes and understanding human biology and disease.