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Related Concept Videos

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 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...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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...
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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Related Experiment Video

Updated: Jul 17, 2026

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

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

Published on: March 1, 2024

Bayesian learning of sparse gene regulatory networks.

Zeke S H Chan1, Lesley Collins, N Kasabov

  • 1Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand. shun.chan@aut.ac.nz

Bio Systems
|January 16, 2007
PubMed
Summary

This study introduces sparse Bayesian learning (SBL) to simplify gene regulatory network (GRN) models. SBL reduces model complexity, improving the accuracy and interpretability of inferred gene relationships.

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Last Updated: Jul 17, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Differential equations (DEs) are widely used for modeling gene regulatory networks (GRNs).
  • DE models often suffer from over-parameterization, especially for large numbers of genes (d), leading to data over-fitting and difficult interpretation.
  • The O(d^2) parameter requirement in DEs poses a significant challenge for complex GRN inference.

Purpose of the Study:

  • To address the over-parameterization issue in DE-based GRN modeling.
  • To introduce and apply sparse Bayesian learning (SBL) for GRN model sparsification.
  • To enhance the plausibility, interpretability, and consistency of inferred GRNs.

Main Methods:

  • Application of sparse Bayesian learning (SBL) to sparsify GRN models represented by differential equations.
  • Leveraging the parsimony principle to drive redundant parameters to zero.
  • Validation using time-series gene expression data from yeast Saccharomyces cerevisiae.

Main Results:

  • Successfully sparsified GRN models using SBL, reducing the number of effective parameters.
  • Inferred GRNs were more plausible and interpretable due to the sparse parameter sets.
  • Achieved more optimal and consistent solutions by reducing the solution space volume.

Conclusions:

  • Sparse Bayesian learning is an effective method for overcoming over-parameterization in differential equation-based GRN models.
  • SBL enhances the biological relevance and interpretability of inferred gene regulatory networks.
  • The approach reliably reproduced known regulatory events, demonstrating its practical utility.