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Related Concept Videos

Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
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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...
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

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Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Exploring pathways from gene co-expression to network dynamics.

Huai Li1, Yu Sun, Ming Zhan

  • 1Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

Methods in Molecular Biology (Clifton, N.J.)
|April 22, 2009
PubMed
Summary
This summary is machine-generated.

Computational systems biology offers new tools for understanding gene networks. CoExMiner and PathwayPro analyze gene co-expression and network dynamics, revealing insights into disease and potential drug targets.

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Area of Science:

  • Computational Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Understanding gene networks is crucial for post-genomic research.
  • Physiological and pathological phenotypes arise from gene expression networks.
  • Current methods lack comprehensive analysis of static and dynamic gene network features.

Purpose of the Study:

  • To develop computational algorithms for exploring gene co-expression and network dynamics.
  • To provide tools for analyzing transcriptional responses and network behaviors.
  • To apply these algorithms to identify disease mechanisms and therapeutic targets.

Main Methods:

  • CoExMiner: B-spline approximation and coefficient of determination (CoD) for gene co-expression patterns.
  • PathwayPro: Finite-state Markov chain model for transcriptional network dynamics.
  • Application to ligand-receptor interactions in cancer cells and BCR-ABL pathway in leukemia.

Main Results:

  • Identified linear and nonlinear ligand-receptor interactions in cancer development.
  • Disclosed disease and drug targets for leukemia.
  • Provided new biological insights into cancer and leukemia through computational analysis.

Conclusions:

  • CoExMiner and PathwayPro are valuable tools for systems biology research.
  • Computational approaches offer significant utility in biological and medical research.
  • These algorithms facilitate the understanding of gene networks in health and disease.