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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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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.
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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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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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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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A tutorial on analysis and simulation of boolean gene regulatory network models.

Yufei Xiao1

  • 1Dept. of Epidemiology & Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.

Current Genomics
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

This tutorial introduces Boolean networks and probabilistic Boolean networks for understanding gene regulatory networks. It details analysis and simulation methods, highlighting their Markov chain properties for steady-state and dynamic analysis.

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Area of Science:

  • Genomic signal processing
  • Computational biology
  • Systems biology

Background:

  • Gene regulatory networks are crucial for understanding genomic functions.
  • Boolean networks and probabilistic Boolean networks are key rule-based dynamic models for gene interactions.

Purpose of the Study:

  • To provide an introductory tutorial on Boolean and probabilistic Boolean networks.
  • To present up-to-date analysis and simulation methods for these models.
  • To explore the relationship between Boolean models, Markov chains, and dynamic Bayesian networks.

Main Methods:

  • Markov chain analysis to study attractors and steady-state distributions.
  • Structural analysis of Boolean models.
  • Simulation techniques including state transition diagrams and dynamic simulations.
  • Algorithm for generating Boolean networks with specific attractors.

Main Results:

  • Boolean models can be represented as Markov chains, enabling steady-state analysis.
  • Established relationships between probabilistic Boolean networks and dynamic Bayesian networks via Markov analysis.
  • Presented comprehensive simulation methods for network dynamics and attractor identification.

Conclusions:

  • Markov chain analysis provides a foundational framework for understanding Boolean network dynamics and properties.
  • The presented methods facilitate the analysis and simulation of gene regulatory networks.
  • This work serves as a guide for researchers in genomic signal processing and computational biology.