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Related Concept Videos

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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
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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...
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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...
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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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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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Related Experiment Video

Updated: Dec 21, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A non-linear reverse-engineering method for inferring genetic regulatory networks.

Siyuan Wu1, Tiangang Cui1, Xinan Zhang2

  • 1School of Mathematics, Monash University, Clayton, VIC, Australia.

Peerj
|May 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mathematical framework to uncover complex genetic regulatory networks controlling blood cell development. The approach accurately models gene expression dynamics, revealing key mechanisms in hematopoietic stem cell differentiation.

Keywords:
Differential equationGenetic regulatory networkHematopoiesisNetwork inferenceProbabilistic graphic model

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Hematopoiesis, the process of blood cell formation, is crucial for development and is regulated by intricate genetic networks.
  • Understanding the precise regulatory mechanisms governing hematopoietic stem cell (HSC) fate determination remains a significant challenge in biology.

Purpose of the Study:

  • To develop a novel mathematical framework for accurately inferring detailed genetic regulatory mechanisms in hematopoiesis.
  • To investigate the roles of protein heterodimers and synergistic effects in gene regulation during cell differentiation.

Main Methods:

  • A hybrid approach combining the Extended Forward Search Algorithm (top-down) for network structure inference and a non-linear mathematical model (bottom-up) for dynamical property inference.
  • Application to 11-gene regulatory networks for erythrocyte and neutrophil differentiation pathways using published experimental data.
  • Inference of network topologies, including potential heterodimers and synergistic effects, followed by parameter estimation and network refinement using edge deletion tests and robustness analysis.

Main Results:

  • Successfully predicted network topologies and inferred model parameters for two distinct hematopoietic differentiation pathways.
  • The developed mathematical model accurately simulated experimental gene expression data for both erythrocyte and neutrophil differentiation.
  • Identified potential nonlinear regulatory terms, suggesting the importance of heterodimers and synergistic interactions.

Conclusions:

  • The proposed computational approach effectively infers both the topological structure and dynamic properties of genetic regulatory networks.
  • This method provides a powerful tool for unraveling complex biological processes like hematopoiesis.
  • The findings highlight the significance of nonlinear interactions in regulating cell fate determination.