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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...

Reporter Genes

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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.

Ribosome Profiling

Ribosome Profiling

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What is Gene Expression?

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Constitutive and Regulated Gene Expression

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Related Experiment Video

Updated: Nov 10, 2025

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

A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling.

Muhamed Wael Farouq^1,2, Wadii Boulila^3,4, Zain Hussain⁵

¹Department of Statistics, Mathematics and Insurance, University of Ain Shams, Cairo 11566, Egypt.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This study introduces an explainable machine learning approach to understand cancer biomarkers in non-small cell lung cancer. The novel mathematical model analyzes gene expression, aiding in cancer diagnosis and treatment discovery.

Keywords:

coupled reaction PDE diffusion equation explainable machine learning gene expression non-small cell lung cancer

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Related Experiment Videos

Last Updated: Nov 10, 2025

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

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational biology
Bioinformatics
Cancer research

Background:

Machine learning (ML) aids cancer diagnosis but often functions as a 'black box'.
Understanding ML decision-making is crucial for clinical applications.
Cancer involves cellular dysregulation, necessitating study of its regulators.

Purpose of the Study:

To develop an explainable analysis of tumorigenesis biomarkers in non-small cell lung cancer (NSCLC).
To elucidate the reasons behind specific cancer phenotypic characteristics.
To enhance understanding of cancer biology for improved diagnostics and therapeutics.

Main Methods:

Novel mathematical formulation for mRNA, ncRNA, and coupled mRNA-ncRNA regulators.
Approximation of temporal gene expression profiles using coupled-reaction partial differential equations.
Simulation experiments to validate the model against oncogene population abundance.

Main Results:

The proposed mathematical model accurately represents gene-expression profiles related to oncogenes in NSCLC.
Demonstrated a best fit for population abundance of oncogenes.
Provided insights into the dynamics of gene regulation in tumorigenesis.

Conclusions:

The developed explainable analysis offers a new perspective on cancer biomarker identification.
This approach can lead to interpretable ML models for discovering gene regulatory dynamics.
Potential for advancing diagnostic, prognostic, and treatment strategies for various cancers.