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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 addition of a...

Regulation of Expression Occurs at Multiple Steps

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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 Steps

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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...

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...

Circadian Rhythms and Gene Regulation

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The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...

Circadian Rhythms and Gene Regulation

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The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...

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Related Experiment Video

Updated: Jun 3, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Functional mapping of expression quantitative trait loci that regulate oscillatory gene expression.

Arthur Berg¹, Ning Li, Chunfa Tong

¹Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|April 7, 2011

Summary

This study introduces a dynamic model to map expression quantitative trait loci (eQTLs) controlling gene expression oscillations. The model identifies how eQTLs influence gene kinetics and regulatory networks for periodic biological processes.

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Area of Science:

Genetics
Systems Biology
Bioinformatics

Background:

Gene expression oscillations are fundamental to biological processes like circadian rhythms and cell cycles.
Individual variations in oscillation frequency and amplitude are influenced by expression quantitative trait loci (eQ QTLs).

Purpose of the Study:

To develop a dynamic model integrating oscillatory dynamics and functional mapping for eQTL identification.
To determine how eQTLs regulate gene activation kinetics and expression dynamics.

Main Methods:

Developed a dynamic model incorporating Fourier series analysis for eQTL genotype effects.
Utilized an autoregressive moving-average (ARMA) process to model time-course gene expression data.
Employed the expectation-maximization (EM) algorithm for parameter estimation within a mixture model.

Main Results:

The model effectively estimates Fourier series parameters to assess eQTL regulation of gene expression dynamics.
Simulation studies validated the statistical properties and performance of the developed model.
The approach accommodates complex covariance structures in time-course expression data using ARMA(r,s) processes.

Conclusions:

The dynamic model offers a robust statistical framework for mapping eQTLs and their interactions in oscillatory gene expression.
This tool aids in constructing regulatory genetic networks for periodic biological phenomena.
Enhances understanding of genetic control over dynamic biological processes.