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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

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.
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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Ribosome Profiling

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Combinatorial Gene Control

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Related Experiment Video

Updated: May 16, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Compromise of multiple time-resolved transcriptomics experiments identifies tightly regulated functions.

Sebastian Klie¹, Camila Caldana, Zoran Nikoloski

¹Genes and Small Molecules Group, Max Planck Institute of Molecular Plant Physiology Potsdam, Germany.

Frontiers in Plant Science

|November 20, 2012

Summary

This summary is machine-generated.

STATIS and dual-STATIS methods integrate multiple biological data tables to find common patterns. These approaches identify key genes, proteins, and metabolites responding to perturbations, aiding in understanding plant systems.

Keywords:

Arabidopsis compromise of data tables multi-way data analysis transcriptomics time-series data

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Published on: March 7, 2018

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

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Published on: April 30, 2011

Area of Science:

Systems biology
Bioinformatics
Computational biology

Background:

High-throughput technologies generate multi-component biological data (genes, proteins, metabolites).
Analyzing multiple, time-resolved data tables from biological perturbations is crucial for hypothesis generation.
Existing methods struggle to integrate and interpret these complex datasets.

Purpose of the Study:

To develop a computational approach for integrating multiple, time-resolved biological data tables.
To identify a common 'compromise' table representing shared biological information.
To pinpoint biological components (genes, proteins, metabolites) driving these shared responses.

Main Methods:

Application of STATIS (Structure Amplifies, Transforms, and Integrates Similar Systems) and dual-STATIS.
These methods extend principal component analysis for multi-table data integration.
Subsequent application of classical analyses like clustering and term over-enrichment on the derived compromise table.

Main Results:

STATIS and dual-STATIS successfully derive a compromise table from multiple data tables.
These methods facilitate the interpretation of time-resolved transcriptomics data from Arabidopsis thaliana.
Identified biological components and pathways exhibiting coordinated responses to light and temperature changes.

Conclusions:

STATIS and dual-STATIS are effective for integrating and analyzing dynamic, multi-component biological data.
These techniques reveal coordinated biological responses to environmental perturbations.
The findings offer insights into regulatory mechanisms controlling plant systems.