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Related Concept Videos

IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and produces two-second...
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...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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...
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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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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Regulation of Expression Occurs at Multiple Steps

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

A single source k-shortest paths algorithm to infer regulatory pathways in a gene network.

Yu-Keng Shih1, Srinivasan Parthasarathy

  • 1Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA.

Bioinformatics (Oxford, England)
|June 13, 2012
PubMed
Summary

We developed a new algorithm for inferring gene regulatory pathways in biological networks. Our method enhances path diversity and significantly speeds up analysis compared to existing approaches.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Inferring regulatory pathways in gene interaction networks is crucial for understanding biological systems.
  • Identifying potential regulatory pathways through a specific gene is a key challenge in Systems Biology.

Purpose of the Study:

  • To propose a novel algorithm for inferring regulatory pathways in gene networks.
  • To enhance the diversity of discovered paths for a more comprehensive understanding of biological systems.

Main Methods:

  • A novel single-source k-shortest paths based algorithm.
  • Explicitly accounting for and enhancing the diversity of discovered paths.

Main Results:

  • The proposed algorithm demonstrates utility over state-of-the-art inference algorithms on the yeast gene network.
  • The algorithm achieves a significant speedup compared to existing methods.

Conclusions:

  • The novel k-shortest paths approach effectively infers regulatory pathways.
  • Enhanced path diversity aids in understanding underlying biological systems.
  • The algorithm offers improved efficiency for gene network analysis.