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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...
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...
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.
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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.
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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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Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional

Xiujun Zhang1, Xing-Ming Zhao, Kun He

  • 1Institute of Systems Biology, Shanghai University, Shanghai 200444, China.

Bioinformatics (Oxford, England)
|November 18, 2011
PubMed
Summary

This study introduces a new computational method, PCA-CMI, to infer gene regulatory networks (GRNs) by considering non-linear gene dependencies and network structure. The method accurately identifies direct gene interactions from expression data, outperforming existing approaches.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Reconstructing gene regulatory networks (GRNs) is crucial for understanding cellular mechanisms but remains computationally challenging.
  • Existing GRN inference methods often fail to simultaneously address key GRN properties like topological sparseness and non-linear dependencies.
  • A comprehensive approach is needed to accurately infer causal relationships within complex regulatory systems.

Purpose of the Study:

  • To develop a novel computational method for inferring GRNs from gene expression data.
  • To account for non-linear gene dependencies and the topological structure of GRNs.
  • To improve the accuracy and ability to distinguish direct interactions in GRN reconstruction.

Main Methods:

  • Developed a Path Consistency Algorithm (PCA) utilizing Conditional Mutual Information (CMI) to infer GRNs.
  • Represented gene conditional dependence using CMI, calculated via a concise formula involving covariance matrices under a Gaussian distribution assumption.
  • Employed benchmark GRNs from the DREAM challenge and the SOS DNA repair network for validation.

Main Results:

  • The proposed PCA-CMI method demonstrated high accuracy in inferring GRNs from gene expression data.
  • Cross-validation results showed significant outperformance compared to previous GRN inference methods.
  • The method effectively distinguishes direct (causal) gene interactions from indirect associations.

Conclusions:

  • The PCA-CMI method offers a robust and accurate approach for GRN reconstruction.
  • This method advances the understanding of complex regulatory mechanisms by accurately identifying causal gene interactions.
  • The developed approach provides a valuable tool for systems biology research.