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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...

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...

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...

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However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...

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.

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks.

Francesco Zito¹, Vincenzo Cutello¹, Mario Pavone¹

¹Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy.

Entropy (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces a novel machine learning method for simulating gene expression and inferring gene regulatory networks. The approach models gene interactions and responses to perturbations, advancing genetic research.

Keywords:

complex network gene regulatory network machine learning metaheuristic reverse engineering time-series forecasting

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Area of Science:

Computational Biology
Genetics
Bioinformatics

Background:

Gene expression simulation and gene regulatory network inference are crucial for understanding biological mechanisms.
Machine learning offers powerful tools for analyzing complex gene expression data.
Existing methods may have limitations in accurately modeling gene interactions and responses to perturbations.

Purpose of the Study:

To present a novel computational framework for simulating gene expression regulation and inferring gene regulatory networks.
To enable the modeling of gene expression in response to various alterations or perturbations.
To evaluate the effectiveness and advantages of the proposed method compared to existing approaches.

Main Methods:

Development of a novel machine learning-based framework for gene expression simulation.
Modeling of mutual interactions and regulatory dynamics among a group of genes.
Empirical evaluation using both artificial and real biological benchmark datasets.

Main Results:

The proposed method demonstrates effectiveness in simulating gene expression regulation.
Comparative analysis highlights the advantages and disadvantages of the new approach.
Validation on benchmarks confirms the methodology's capability to capture gene expression dynamics.

Conclusions:

The novel framework shows significant potential for advancing gene expression simulation and gene regulatory network inference.
This work contributes to a deeper understanding of gene regulatory mechanisms and their responses to environmental factors.
Future improvements are proposed to further enhance the method's performance and applicability in genetics.