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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...
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...
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...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...

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Integrating heterogeneous gene expression data for gene regulatory network modelling.

Alina Sîrbu1, Heather J Ruskin, Martin Crane

  • 1Centre for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland, asirbu@computing.dcu.ie.

Theory in Biosciences = Theorie in Den Biowissenschaften
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for inferring gene regulatory networks (GRNs) using multiple gene expression datasets. This integrative approach enhances model robustness and reduces noise, improving systems biology research.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding cellular function and protein levels.
  • Inferring GRNs from gene expression data is a key challenge in systems biology.
  • Existing methods struggle with large networks and limited time-series data, leading to under-determination problems.

Purpose of the Study:

  • To develop an integrative approach for robust GRN model inference.
  • To address the limitations of current methods in handling large-scale and noisy gene expression data.
  • To improve the accuracy and reliability of inferred gene regulatory network models.

Main Methods:

  • Utilizing multiple heterogeneous expression time series to infer a single GRN model.
  • Applying an integrative approach for model inference, not previously discussed.
  • Employing wavelet analysis to assess noise over-fitting within datasets.

Main Results:

  • The integrative approach yields more robust GRN models compared to single-dataset methods.
  • Models inferred using this method are less sensitive to noise and parameter perturbations.
  • Wavelet analysis confirmed limited noise over-fitting in the developed models.

Conclusions:

  • The proposed integrative method offers a more reliable way to infer gene regulatory networks.
  • This approach enhances the applicability of GRN modeling to real-world biological data.
  • The findings contribute to advancing systems biology by providing a robust tool for network inference.