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Related Concept Videos

Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
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Olfaction01:25

Olfaction

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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
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Related Experiment Video

Updated: Oct 6, 2025

A Free-breathing fMRI Method to Study Human Olfactory Function
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Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in

Suma L Sivan1, Vinod Chandra S Sukumara Pillai2

  • 1Department of Computational Biology and Bioinformatics, University of Kerala, Trivandrum 695581, India.

Biomolecules
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm, Smell Detection Agent (SDA), to identify critical disease subnetworks in complex molecular networks. The SDA algorithm effectively pinpoints key biomarkers and drug targets for triple-negative breast and colorectal cancers.

Keywords:
breast cancercolorectal cancerdifferential expressiondisease genesdrug targetsmell detection agent optimisationsubnetworktopological weight

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Network biology is crucial for understanding complex diseases.
  • Efficient computational methods are needed to detect dys-regulated subnetworks.
  • Existing methods may not fully capture the complexity of molecular interactions.

Purpose of the Study:

  • To develop an efficient computational method for identifying dys-regulated subnetworks.
  • To apply a novel nature-inspired algorithm for detecting cancer-related subnetworks.
  • To identify potential biomarkers and drug targets for triple-negative breast cancer and colorectal cancer.

Main Methods:

  • Constructed an integrated network using gene and protein-protein interaction data.
  • Quantified dys-regulation using differential expression and topological weights.
  • Designed and applied the Smell Detection Agent (SDA) optimization algorithm.
  • Analyzed subnetworks for triple-negative breast cancer and colorectal cancer samples.

Main Results:

  • The SDA algorithm identified optimal dys-regulated subnetworks for both cancer types.
  • Identified known biomarkers (e.g., PIK3CA, BRCA1) and drug targets (e.g., TOP2A, EGFR) in breast cancer.
  • Proposed novel drug targets (e.g., PLK1, HSP90AA1) for breast and colorectal cancers.
  • SDA-derived breast cancer subnetwork showed higher enrichment (75%) compared to MCODE and Minimum Spanning Tree.

Conclusions:

  • The proposed SDA algorithm is an effective and novel approach for deriving optimal dys-regulated subnetworks.
  • The identified subnetworks and drug targets hold potential for further experimental validation and therapeutic development.
  • This method advances the application of network biology in complex disease research.