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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

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 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...

Protein Networks

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These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Mutual interaction in network motifs robustly sharpens gene expression in developmental processes.

Shuji Ishihara¹, Tatsuo Shibata

¹Department of Basic Science, University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan. shuji@complex.c.u-tokyo.ac.jp

Journal of Theoretical Biology

|March 18, 2008

Summary

This summary is machine-generated.

Mutual repression in gene regulatory networks enhances developmental pattern sensitivity and robustness. This finding, particularly in Drosophila melanogaster segmentation, clarifies the biological roles of these network motifs.

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Area of Science:

Developmental biology
Systems biology
Genetics

Background:

Gene regulatory networks (GRNs) orchestrate development through complex interactions.
Mutual repression motifs, involving two genes and a shared regulator, are prominent in GRNs.
These motifs are particularly observed in the segmentation process of Drosophila melanogaster.

Purpose of the Study:

To mathematically investigate the functional role of mutual repression in gene regulatory networks.
To analyze the response of regulated mutual loops with mutual repression to external stimuli.
To compare the importance of mutual repression against other regulatory mechanisms.

Main Methods:

Database analysis of Drosophila melanogaster gene regulatory networks.
Mathematical modeling of regulated mutual loops with mutual repression.
Analysis of network response sensitivity and transient dynamics.

Main Results:

Mutual repression enhances response sensitivity without altering the threshold for target gene activation.
This heightened sensitivity sharpens spatial domain patterns, ensuring developmental robustness.
Transient dynamics analysis revealed domain boundary shifts, consistent with experimental data.

Conclusions:

Mutual repression is crucial for precise spatial pattern formation during development.
The motif's sensitivity contributes to robust developmental processes by sharpening expression domains.
Mathematical modeling provides insights into the biological significance of specific regulatory motifs.