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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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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.
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Cis-regulatory Sequences02:02

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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...
Regulation of Expression at Multiple Steps01:23

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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.
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Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster
08:19

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Published on: December 19, 2011

Using qualitative probability in reverse-engineering gene regulatory networks.

Zina M Ibrahim1, Alioune Ngom, Ahmed Y Tawfik

  • 1School of Computer Science, University of Windsor, 401, Sunset Avenue, Windsor, ON N9CB3P4, Canada. ibrahim@uwindsor.ca

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|September 30, 2010
PubMed
Summary
This summary is machine-generated.

Qualitative probabilistic networks (QPNs) improve gene regulatory network learning from gene expression data. This method enhances accuracy by reducing uncertainty and identifying true regulatory interactions more efficiently.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
  • Learning GRN structure from microarray gene expression data is challenging due to inherent noise and uncertainty.
  • Existing methods often struggle to distinguish true regulatory interactions from spurious correlations.

Purpose of the Study:

  • To demonstrate the utility of Qualitative Probabilistic Networks (QPNs) in enhancing Dynamic Bayesian Network (DBN) based GRN structure learning.
  • To develop a model that leverages QPNs to identify candidate gene regulators and establish a prior structure for DBNs.
  • To improve the accuracy and efficiency of GRN construction from gene expression data.

Main Methods:

  • Utilizing Qualitative Probabilistic Networks (QPNs) to model monotonic relations for identifying regulatory interactions.
  • Developing a model that maps genetic network interactions to QPN constructs.
  • Employing the QPN-derived prior structure to guide Dynamic Bayesian Network (DBN) learning algorithms.
  • Validating the model using known gene regulatory interactions in Drosophila Melanogaster.

Main Results:

  • QPNs effectively identify regulatory interactions, showing reduced susceptibility to uncertainty in gene expression data.
  • The proposed model successfully provides candidate regulators, distinguishing true regulations from spurious correlations.
  • The method enables the discovery of coregulators for target genes and leads to more efficient GRN construction.
  • The model's performance was validated against existing literature using Drosophila Melanogaster data.

Conclusions:

  • QPNs offer a robust approach to enhance GRN structure learning from gene expression data.
  • The integration of QPNs with DBNs provides a more accurate and efficient method for inferring gene regulatory relationships.
  • This approach contributes to a better understanding of gene regulation by improving the reliability of inferred networks.