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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
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Selection of human tissue-specific elementary flux modes using gene expression data.

Alberto Rezola1, Jon Pey, Luis F de Figueiredo

  • 1Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.

Bioinformatics (Oxford, England)
|June 8, 2013
PubMed
Summary

This study introduces a novel method to compute elementary flux modes (EFMs) in human metabolic networks, enabling better analysis of high-throughput molecular data and uncovering metabolic pathways.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Bioinformatics

Background:

  • Analyzing high-throughput molecular data requires understanding metabolic pathway structures.
  • Elementary flux modes (EFMs) capture cellular metabolism's complexity but are computationally challenging for genome-scale networks.
  • Optimization-based techniques offer a promising approach for EFM computation in human metabolism.

Purpose of the Study:

  • To generalize the K-shortest EFM algorithm for human genome-scale metabolic networks.
  • To create a valuable database of EFMs for contextualizing high-throughput data.
  • To predict characteristic EFMs in human tissues using expression data.

Main Methods:

  • Exploitation and generalization of the K-shortest EFM algorithm.
  • Application to a human genome-scale metabolic network.
  • Enrichment analysis of gene expression data from 10 healthy human tissues.
  • Utilizing a multivariate hypergeometric test for statistical analysis.

Main Results:

  • A subset of EFMs was determined, encompassing known and potential novel human metabolic pathways.
  • Characteristic EFMs were predicted for 10 human tissues based on expression data.
  • The multivariate hypergeometric test yielded more biologically meaningful results than the standard hypergeometric test.
  • Analysis of liver EFMs showed strong agreement with existing literature.

Conclusions:

  • The developed method provides a valuable resource for metabolically contextualizing high-throughput data.
  • The approach enhances the biological interpretation of molecular data within the framework of EFMs.
  • This work facilitates deeper insights into human metabolic complexity and tissue-specific functions.