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The Notch signaling pathway is a major intracellular signaling pathway that is highly conserved over a broad spectrum of metazoan species. It stands unique from other intracellular signaling mechanisms in animals because notch protein itself acts as the receptor as well as the primary signaling molecule.
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The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Wnt is a zygotic effect gene that is expressed during very early embryonic development. It regulates various processes in animals starting from early development through the adult stage, such as organogenesis in the embryo and maintenance of neuronal and blood stem cells. Wnt proteins can induce a wide variety of intracellular pathways depending upon the specific abilities of different Wnt ligands to form a complex with shared and cognate receptors in the presence of different co-receptors. The...
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The gene encoding the main signaling molecules of the Wnt signaling pathways (the Wnt proteins) was discovered almost four decades ago by Nüsslein-Volhard and Wieschaus. They identified and originally named the gene "wingless" (wg) after a phenotype discovered during their landmark genetic screen in Drosophila for body pattern defects. At around the same time, another researcher named Harold Varmus found that a murine tumor virus activates the mammalian wg homolog, Int-1, which...
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Related Experiment Video

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways.

Ming Shi1,2, Yanwen Chong3, Weiming Shen4

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. shiming@whu.edu.cn.

Genes
|June 30, 2018
PubMed
Summary

This study introduces DynSig, a novel method for identifying differentially expressed pathways (DEPs) by analyzing molecular signaling dynamics and gene links within cellular networks. DynSig effectively detects pathway alterations in cancer cells.

Keywords:
Markov chain modeldynamic signalinggene linkshigh-throughput datapathway analysis

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Identifying differentially expressed pathways (DEPs) is crucial for understanding disease mechanisms.
  • Existing methods often overlook the dynamic interactions (gene links) within biological pathways.
  • Pathway network dynamics are key to accurately characterizing cellular signaling.

Purpose of the Study:

  • To propose DynSig, a novel method for identifying DEPs by incorporating pathway network dynamics.
  • To detect molecular signaling changes in cancerous cells by analyzing gene links.
  • To develop a robust statistical framework for assessing pathway activity alterations.

Main Methods:

  • DynSig utilizes a Markov chain model (MCM) to represent pathway dynamics based on gene links.
  • It focuses on the dynamic behavior of pathways rather than static gene nodes.
  • A signaling perturbation score is formulated to quantify pathway activity differences between sample classes.

Main Results:

  • DynSig effectively identifies differentially expressed pathways by considering molecular signaling dynamics.
  • The method demonstrates high effectiveness and efficiency in simulations and real-world datasets.
  • Incorporating gene link dynamics provides deeper insights into pathway activity.

Conclusions:

  • DynSig offers a significant advancement in identifying DEPs by modeling pathway dynamics.
  • The method provides a powerful tool for characterizing molecular signaling changes in complex biological systems.
  • DynSig's focus on gene links and MCM modeling enhances the accuracy of pathway analysis in cancer research.