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What is Gene Expression?

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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Related Experiment Video

Updated: Sep 17, 2025

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

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

Published on: December 7, 2021

Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization.

Ehsan Saghapour^1,2, Rahul Sharma¹, Delower Hossain^2,3

¹Department of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, United States.

Frontiers in Bioinformatics

|July 3, 2025

Summary

This summary is machine-generated.

Temporal GeneTerrain visualizes gene expression dynamics over time, improving upon traditional methods like heatmaps. This advanced technique enhances the understanding of complex biological responses to drug treatments.

Keywords:

bioinformatics cancer cell lines data visualization drug screening gene expression precision medicine

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Last Updated: Sep 17, 2025

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

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

Published on: December 7, 2021

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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Published on: February 9, 2017

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Temporal gene expression dynamics are crucial for understanding biological responses, especially during drug treatment.
Conventional visualization methods (heatmaps, static clustering) struggle to capture complex temporal changes in large datasets.
Existing techniques often obscure fine-grained temporal transitions, reducing clarity and interpretability.

Purpose of the Study:

To introduce Temporal GeneTerrain, a novel visualization method for dynamic gene expression analysis.
To address the limitations of current methods in representing temporal gene expression patterns.
To enhance the interpretability of multidimensional transcriptomic data over time.

Main Methods:

Applied Temporal GeneTerrain to analyze transcriptomic data from LNCaP cells treated with mefloquine, tamoxifen, and withaferin A.
Utilized Z-score normalization and selected highly variable genes, followed by Pearson correlation to identify co-expressed genes (r ≥ 0.5).
Constructed a 2D protein-protein interaction network using Kamada-Kawai embedding and mapped gene expression as Gaussian density fields.

Main Results:

Temporal GeneTerrain revealed intricate temporal shifts and delayed pathway responses (e.g., NGF-stimulated transcription, unfolded protein response) under combined drug treatments.
The method demonstrated significantly improved resolution and interpretability compared to traditional heatmap visualizations.
Effectively captured the multidimensional and transient nature of gene expression patterns.

Conclusions:

Temporal GeneTerrain provides an intuitive and detailed representation of gene expression dynamics over time.
This method uniquely captures the transient nature of gene expression, outperforming heatmaps and static clustering.
Enhances interpretability of complex biological datasets, supporting research and therapeutic development.