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Regulation of Expression at Multiple Steps

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...

Constitutive and Regulated Gene Expression

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Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...

Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

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.
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Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

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Related Experiment Video

Updated: Mar 3, 2026

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

MODELING DEPENDENT GENE EXPRESSION.

Donatello Telesca¹, Peter Müller², Giovanni Parmigiani³

¹Department of Biostatistics, UCLA School of Public Health, Los Angeles, California 90095-1772, USA.

Annals of Statistics

|May 6, 2017

Summary

This summary is machine-generated.

This study introduces a Bayesian method for analyzing gene expression dependence, incorporating pathway knowledge to improve inferences about gene expression differences across phenotypes and explore variations in gene dependence in ovarian cancer.

Keywords:

Conditional independence microarray data probability of expression probit models reciprocal graphs reversible jumps MCMC

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Last Updated: Mar 3, 2026

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

High-throughput gene expression data analysis requires robust methods to model complex dependencies.
Understanding gene expression dependence is crucial for identifying biological pathways and disease mechanisms.

Purpose of the Study:

To develop a Bayesian approach for inferring gene expression dependence using prior pathway knowledge.
To account for gene dependence when inferring differential gene expression across phenotypes.
To investigate differences in gene dependence patterns between phenotypes.

Main Methods:

A Bayesian framework for modeling gene expression as an ordinal outcome.
Incorporation of prior knowledge about biological pathways to structure dependence inference.
A flexible probability model for the structure and strength of gene dependencies.

Main Results:

The proposed Bayesian method effectively models gene expression dependence, leveraging pathway information.
The approach allows for accurate inference of differential gene expression while accounting for dependencies.
Analysis of ovarian cancer data revealed differences in gene dependence within the Complement and Coagulation Cascade pathway.

Conclusions:

The developed Bayesian approach provides a powerful tool for analyzing gene expression dependence in high-throughput studies.
Integrating prior biological knowledge enhances the interpretability and accuracy of genomic analyses.
This method offers new insights into pathway-specific gene interactions in ovarian cancer.