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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

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 Steps02:24

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 Steps02:24

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...
DNA Microarrays02:34

DNA Microarrays

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...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

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Reverse engineering gene regulatory network from microarray data using linear time-variant model.

Mitra Kabir1, Nasimul Noman, Hitoshi Iba

  • 1Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh. mitrakabir@gmail.com

BMC Bioinformatics
|February 4, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for inferring gene regulatory networks from gene expression data. The approach accurately reconstructs network topology and regulatory parameters, offering efficient and scalable solutions for biological systems analysis.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Gene regulatory networks (GRNs) model gene interactions within cells, aiding in predicting organism behavior for diagnostics and therapeutics.
  • DNA microarrays provide essential time-series gene expression data for inferring GRNs.
  • Accurate GRN inference is crucial for understanding cellular mechanisms and developing novel treatments.

Purpose of the Study:

  • To develop and validate a new approach for inferring gene regulatory networks using time-series gene expression data.
  • To assess the accuracy and efficiency of the proposed method in reconstructing both synthetic and real biological networks.
  • To establish a computationally efficient method suitable for large-scale network reconstruction.

Main Methods:

  • Utilized a linear time-variant model for gene regulatory network inference.
  • Employed Self-Adaptive Differential Evolution, an Evolutionary Algorithm, as the learning paradigm.
  • Applied the method to synthetic, simulated (cAMP oscillations in Dictyostelium discoideum), and real (SOS DNA repair in Escherichia coli) gene expression datasets.

Main Results:

  • The proposed method accurately reconstructed synthetic network topology and regulatory parameters from both noise-free and noisy time-series data.
  • Validation on simulated and real biological datasets demonstrated the approach's strength in identifying correct gene regulations.
  • Achieved higher accuracy and more reasonable regulations compared to existing methods for the SOS DNA repair system.

Conclusions:

  • The developed approach efficiently infers gene interaction networks from various data types, including synthetic, simulated, and real biological expression data.
  • The method offers considerably reduced computational time, making it highly suitable for reconstructing larger gene regulatory networks.
  • This work provides a foundation for future research in gene regulatory network inference and analysis.