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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...
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...
Global Regulatory Systems01:28

Global Regulatory Systems

Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
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...
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...
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Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...

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Related Experiment Video

Updated: Jul 13, 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

Connecting quantitative regulatory-network models to the genome.

Yue Pan1, Tim Durfee, Joseph Bockhorst

  • 1Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. ypan@cs.wisc.edu

Bioinformatics (Oxford, England)
|July 25, 2007
PubMed
Summary

This study enhances gene regulatory network inference by incorporating genomic sequence features into kinetic parameter modeling. The novel approach improves predictive accuracy and biological insight for gene expression regulation.

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Inferring gene regulatory networks is crucial for understanding cellular processes.
  • Existing methods model transcription rates and regulator activity but lack mechanistic detail.
  • Genomic sequence features can provide a more mechanistic basis for regulatory relationships.

Purpose of the Study:

  • To extend existing gene regulatory network inference methods.
  • To represent and learn kinetic parameters as functions of genomic sequence features.
  • To develop a more mechanistic model of gene regulation.

Main Methods:

  • Developed an extension to Nachman et al.'s approach.
  • Integrated genomic sequence features into kinetic parameter learning.
  • Applied the model to Escherichia coli gene-expression data sets.

Main Results:

  • Sequence-based models achieved superior predictive accuracy compared to non-sequence-based models.
  • The approach demonstrated substantially better performance than a simple baseline.
  • Models provided enhanced explanatory power and biological insight.

Conclusions:

  • Incorporating genomic sequence features into kinetic parameter modeling improves gene regulatory network inference.
  • This mechanistic approach offers a more detailed understanding of gene regulation.
  • The method is effective for analyzing gene expression data, particularly in response to environmental cues.